DEV Community: Archit Mittal

Build a GST Invoice Generator in 70 Lines of Python

Archit Mittal — Wed, 03 Jun 2026 12:55:07 +0000

Every Indian freelancer, agency, and small business eventually faces the same boring problem: generating GST-compliant invoices. The big SaaS tools charge ₹500–₹2000 per month for what is fundamentally a templating + arithmetic job. Let's just write it ourselves.

By the end of this post you'll have a Python script that:

Takes a buyer's state, line items, and GST rates
Correctly splits tax into CGST + SGST (intra-state) or IGST (inter-state) — the part most templates get wrong
Produces a clean, printable PDF invoice with HSN codes and a proper tax breakdown
Runs anywhere Python runs, no SaaS, no monthly fee

Total code: about 70 lines.

Why bother building this?

If you've ever used a stock invoice template, you've probably noticed it just hardcodes "CGST 9% + SGST 9%". That works for sellers and buyers in the same state. The moment you bill a client in another state, you legally owe IGST 18% instead — and the invoice has to say so. Get this wrong and your buyer can't claim input tax credit cleanly. Get it wrong at scale and your CA bills you for the cleanup.

The logic is genuinely simple. The reason people pay for it is that bundling logic + PDF rendering + line items into one tool feels like work. It isn't.

Setup

One dependency:

pip install reportlab

reportlab is a battle-tested PDF library — same one that powers a lot of Python report generators. No fonts to install, no headless browser, no Node.

The full script

Save this as gst_invoice.py:

"""
Simple GST Invoice Generator
Splits CGST/SGST or IGST automatically based on buyer's state.
"""
from datetime import date
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
)

SELLER = {
    "name": "Bharat Traders Pvt Ltd",
    "address": "12, MG Road, Bengaluru, Karnataka 560001",
    "gstin": "29ABCDE1234F1Z5",
    "state": "Karnataka",
}

def calc_gst(amount, rate, buyer_state):
    """Intra-state -> CGST+SGST; Inter-state -> IGST."""
    if buyer_state.strip().lower() == SELLER["state"].lower():
        half = round(amount * rate / 200, 2)
        return half, half, 0.0
    return 0.0, 0.0, round(amount * rate / 100, 2)

def line_totals(items, buyer):
    rows, sub, cgst, sgst, igst = [], 0.0, 0.0, 0.0, 0.0
    for it in items:
        amt = round(it["qty"] * it["price"], 2)
        c, s, i = calc_gst(amt, it["gst"], buyer["state"])
        sub += amt; cgst += c; sgst += s; igst += i
        rows.append([it["desc"], it["hsn"], it["qty"],
                     f"Rs.{it['price']:.2f}", f"{it['gst']}%",
                     f"Rs.{amt:.2f}", f"Rs.{c+s+i:.2f}"])
    return rows, sub, cgst, sgst, igst, round(sub + cgst + sgst + igst, 2)

def build_invoice(filename, buyer, items, invoice_no):
    rows, sub, cgst, sgst, igst, total = line_totals(items, buyer)
    doc = SimpleDocTemplate(filename, pagesize=A4, title=f"Invoice {invoice_no}")
    s = getSampleStyleSheet()
    story = [
        Paragraph(f"<b>TAX INVOICE — {invoice_no}</b>", s["Title"]),
        Paragraph(f"Date: {date.today():%d %b %Y}", s["Normal"]),
        Spacer(1, 8),
        Paragraph(f"<b>From:</b> {SELLER['name']}<br/>{SELLER['address']}"
                  f"<br/>GSTIN: {SELLER['gstin']}", s["Normal"]),
        Spacer(1, 6),
        Paragraph(f"<b>Bill To:</b> {buyer['name']}<br/>{buyer['address']}"
                  f"<br/>GSTIN: {buyer.get('gstin', 'Unregistered')}"
                  f"<br/>State: {buyer['state']}", s["Normal"]),
        Spacer(1, 12),
    ]
    data = [["Item", "HSN", "Qty", "Rate", "GST", "Amount", "Tax"]] + rows + [
        ["", "", "", "", "Subtotal", f"Rs.{sub:.2f}", ""],
        ["", "", "", "", "CGST",     f"Rs.{cgst:.2f}", ""],
        ["", "", "", "", "SGST",     f"Rs.{sgst:.2f}", ""],
        ["", "", "", "", "IGST",     f"Rs.{igst:.2f}", ""],
        ["", "", "", "", "TOTAL",    f"Rs.{total:.2f}", ""],
    ]
    t = Table(data, repeatRows=1)
    t.setStyle(TableStyle([
        ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#0b3d91")),
        ("TEXTCOLOR",  (0, 0), (-1, 0), colors.white),
        ("GRID", (0, 0), (-1, -1), 0.4, colors.grey),
        ("FONTSIZE", (0, 0), (-1, -1), 9),
    ]))
    story.append(t)
    doc.build(story)
    print(f"Generated {filename} — Grand Total Rs.{total:.2f}")

if __name__ == "__main__":
    buyer = {"name": "Acme Corp", "address": "21 Park St, Kolkata 700016",
             "gstin": "19XYZAB6789K1Z2", "state": "West Bengal"}
    items = [
        {"desc": "USB-C Cable 1m", "hsn": "8544", "qty": 50,  "price": 199, "gst": 18},
        {"desc": "Wireless Mouse", "hsn": "8471", "qty": 10,  "price": 749, "gst": 18},
        {"desc": "Notebook A5",    "hsn": "4820", "qty": 100, "price": 60,  "gst": 12},
    ]
    build_invoice("invoice_INV-2026-001.pdf", buyer, items, "INV-2026-001")

Run it:

python gst_invoice.py

You'll get invoice_INV-2026-001.pdf in the same folder. Because the seller is in Karnataka and the buyer is in West Bengal, the script automatically uses IGST instead of CGST+SGST. Change the buyer's state to "Karnataka" and rerun — the split flips. That's the whole rule, encoded in five lines.

How the GST split works

The function calc_gst is the brain of this script:

if buyer_state.strip().lower() == SELLER["state"].lower():
    half = round(amount * rate / 200, 2)
    return half, half, 0.0   # CGST + SGST, each half the rate
return 0.0, 0.0, round(amount * rate / 100, 2)   # IGST, full rate

The GST law says you charge the same total tax either way — 18% is 18%. The difference is who collects which half. For intra-state sales the centre and the state each take 9%. For inter-state sales the centre collects the full 18% as IGST and settles with the destination state later. From your invoice's perspective: same amount, different label.

The string compare is intentionally loose (strip().lower()) because in practice state names arrive from CSVs and form inputs with random capitalization and trailing spaces.

Wiring it into your workflow

A few obvious next steps once the basic version works:

Bulk invoices from a CSV. Add a pandas.read_csv("orders.csv") loop around build_invoice. One invoice per row, filenames keyed on invoice number. Useful if you're an agency closing out 30 retainer clients on the 1st of every month.

Auto-email the PDF. Pipe the generated file to smtplib with the client's email taken from the same CSV. The whole "generate + email" pipeline lands under 120 lines.

HSN/SAC lookup. Replace the hard-coded "hsn" field with a lookup against a small dict of common services (consulting = 9983, software = 9973, etc.) so you can't typo it.

Sequential numbering. Add INV-YYYY-NNNN numbering by reading the highest existing PDF in the output folder and incrementing. GST law requires invoice numbers be sequential and unbroken — easy to mess up by hand, trivial to enforce in code.

Validation. GSTINs follow a known checksum (the last character is computed from the first 14). A 10-line validator catches typos before the PDF is generated.

What you've saved

A typical GST billing SaaS in India runs ₹500–₹2000/month for the tier that allows custom branding and unlimited invoices. A solo consultant who issues 10–20 invoices a month is paying ₹6,000–₹24,000 a year for something that ran in 70 lines of Python on their laptop.

The deeper point: most "business automation" tools that get sold as products are 100-line scripts wearing a marketing layer. Once you can read Python, you stop being a customer and start being the supplier.

Follow me on Twitter @automate_archit for daily AI automation tips.

Build a UPI QR Code Generator with Logo in 46 Lines of Python

Archit Mittal — Mon, 01 Jun 2026 12:57:04 +0000

Every time I pay a vendor in India, I'm reminded that UPI is just a URL standard. upi://pay?pa=merchant@bank&pn=Name&am=199.00 is all your phone is reading when it scans that QR taped to the cashier's counter. Which means you don't need PhonePe Business, a payment gateway, or a ₹2999/month "QR builder SaaS" to generate one for yourself.

You need 46 lines of Python.

I built this last week because a friend running a small Chennai cafe was paying a vendor ₹500/month for static-QR stickers with her UPI on them. Forty-six lines of code later, she's printing her own — with her cafe logo in the middle, pre-filled amounts for combo plates, and a transaction reference embedded so reconciliation in Tally takes 30 seconds instead of 30 minutes.

Here's the whole thing.

The full code

import qrcode
from PIL import Image
from urllib.parse import quote
import argparse

def build_upi_url(vpa, name, amount=None, note=None, ref=None):
    params = {"pa": vpa, "pn": quote(name), "cu": "INR"}
    if amount is not None:
        params["am"] = f"{amount:.2f}"
    if note:
        params["tn"] = quote(note)
    if ref:
        params["tr"] = ref
    return "upi://pay?" + "&".join(f"{k}={v}" for k, v in params.items())

def make_qr(data, logo_path, out_path):
    qr = qrcode.QRCode(
        error_correction=qrcode.constants.ERROR_CORRECT_H,
        box_size=12,
        border=2,
    )
    qr.add_data(data)
    qr.make(fit=True)
    img = qr.make_image(fill_color="#0F172A", back_color="white").convert("RGB")
    if logo_path:
        logo = Image.open(logo_path).convert("RGBA")
        size = img.size[0] // 5
        logo.thumbnail((size, size))
        pos = ((img.size[0] - logo.size[0]) // 2,
               (img.size[1] - logo.size[1]) // 2)
        img.paste(logo, pos, mask=logo)
    img.save(out_path)

if __name__ == "__main__":
    p = argparse.ArgumentParser()
    p.add_argument("--vpa", required=True)
    p.add_argument("--name", required=True)
    p.add_argument("--amount", type=float)
    p.add_argument("--note")
    p.add_argument("--ref")
    p.add_argument("--logo")
    p.add_argument("--out", default="upi.png")
    a = p.parse_args()
    url = build_upi_url(a.vpa, a.name, a.amount, a.note, a.ref)
    make_qr(url, a.logo, a.out)
    print(f"Saved {a.out}\nURL: {url}")

That's it. Forty-six lines, two dependencies (qrcode and Pillow), zero API keys, runs on a Raspberry Pi.

Install and run

pip install "qrcode[pil]" Pillow
python upi_qr.py --vpa cafe@okhdfcbank --name "Mocha Lane" \
                 --amount 249 --note "Cold Brew Combo" \
                 --ref ORD-20260601-007 --logo logo.png --out combo.png

You get a combo.png with your logo dead-center and a UPI deeplink that, when scanned, opens any UPI app with the payee, amount, and note pre-filled. The customer just hits "Pay."

Why it actually works in production

There are three details that matter and that ₹2999/month SaaS tools love to hide behind a paywall:

1. Error correction level H. This is the H in ERROR_CORRECT_H — it tells the QR code to encode ~30% redundant data. That redundancy is what lets you cover the center fifth of the code with a logo and still have it scan reliably from a wobbly hand at a counter. Drop it to M and your logo starts breaking scans on cheap Android phones.

2. cu=INR is required. I learned this the embarrassing way. PhonePe will happily scan a QR without the currency tag, populate the payee, and then throw "Unsupported currency" when the user taps pay. NPCI's spec marks cu as optional but every production app I've tested treats it as mandatory. Always include it.

3. URL-encode the payee name and note. If your café is called "Café Bombay" or your note is "Order #7 — table 2," the unencoded ampersands and special characters will silently truncate the rest of the parameters. urllib.parse.quote is two extra characters and saves you a debugging afternoon.

What the parameters mean

The UPI URL spec (NPCI's deeplink standard) accepts these fields:

pa — payee address, i.e. your VPA / UPI handle (yourname@okhdfcbank)
pn — payee name shown to the customer in their UPI app
am — amount in ₹, two decimal places
cu — currency, always INR
tn — transaction note (shown as the description in the customer's app)
tr — transaction reference / order ID — this is the magic field for reconciliation
mc — merchant category code (optional, for registered merchants)

The tr field is the one nobody talks about. If you set --ref ORD-20260601-007 and the customer pays, that exact reference shows up in your UPI app's transaction history. Match it against your order DB, mark the order paid, done. No more "did Rohit's ₹249 come through?" at 9pm.

Three things you can build on this

Once you have the building block, the surface area opens up fast.

Per-order QRs. Wrap build_upi_url in a Flask endpoint, pass the order ID as tr, render the PNG inline. Now every Shopify/Razorpay-free checkout page on your site has a "Pay via UPI" QR with the exact amount and a reference your backend can match. I built this for a client doing handmade soap on Instagram — saved her the 2% Razorpay cut on every ₹400 order. ₹8/order × 600 orders/month = ₹4,800/month, recovered.

Dynamic table QRs for restaurants. Same code, loop over your tables, generate Table_01.png through Table_24.png, each with the table number baked into tn. Customer scans, pays, your staff sees "Table 14 paid ₹890" in the UPI notification — no calling them over to confirm.

Donation pages with suggested amounts. Generate four QRs at ₹100/₹500/₹1000/₹5000 with the same VPA and a campaign tag in tr. Embed all four on a webpage. NGOs love this because UPI doesn't take the 2-3% that Razorpay/Instamojo take on donations.

Where to take it next

The 46-line version is deliberately bare. A few honest extensions I'd add before deploying it for anything customer-facing:

Bulk generation from a CSV — feed it orders.csv and dump 500 PNGs into a folder. Fifteen lines.
PDF sheets for printing — paste 12 QRs onto an A4 grid with reportlab. Twenty lines.
Quiet-zone validation — a logo at exactly 20% of QR area is the safe ceiling. Push past 25% and you start losing scans on older phones.
Print on dark backgrounds — flip fill_color and back_color, but always test with the actual UPI app you expect customers to use. GPay's scanner is more forgiving than PhonePe's.

The whole project is one file. Drop it into a cron job and you've got a self-hosted, zero-cost alternative to half the "UPI for Business" SaaS market.

That's the pattern I keep coming back to: most "SaaS automation" is one or two carefully-chosen Python files away from being a thing you own.

Follow me on Twitter @automate_archit for daily AI automation tips — I post one of these every weekday, all India-relevant, all under 100 lines.

Build a GST Invoice Parser in 60 Lines of Python

Archit Mittal — Mon, 01 Jun 2026 09:56:54 +0000

If you handle Indian invoices for any business, you know the month-end ritual: thirty vendor PDFs arrive in your inbox, and someone has to type GSTIN, invoice number, taxable value, and the CGST/SGST/IGST split into a spreadsheet so the accountant can reconcile with GSTR-2A. One typo means a mismatched input tax credit, and that means a tense email at 9pm.

I built a parser that reads any GST-compliant PDF invoice and writes the numbers straight into a CSV. Sixty lines, one external dependency, no paid OCR service, no API calls.

Here's the full code, then I'll walk through why each piece exists.

The Code

import re
import csv
import sys
from pathlib import Path
import pdfplumber

GSTIN_RE = re.compile(r"\b(\d{2}[A-Z]{5}\d{4}[A-Z]\d[A-Z][A-Z\d])\b")
INVOICE_RE = re.compile(r"Invoice\s*(?:No\.?|Number|#)\s*[:\-]?\s*([A-Z0-9\-/]+)", re.I)
DATE_RE = re.compile(r"(\d{1,2}[/\-.]\d{1,2}[/\-.]\d{2,4})")
AMOUNT_RE = re.compile(r"(?:Total|Grand\s*Total)[^\d]{0,20}([\d,]+\.\d{2})", re.I)
TAXABLE_RE = re.compile(r"Taxable\s*(?:Value|Amount)[^\d]{0,20}([\d,]+\.\d{2})", re.I)
CGST_RE = re.compile(r"\bCGST[^\d]{0,30}([\d,]+\.\d{2})", re.I)
SGST_RE = re.compile(r"\bSGST[^\d]{0,30}([\d,]+\.\d{2})", re.I)
IGST_RE = re.compile(r"\bIGST[^\d]{0,30}([\d,]+\.\d{2})", re.I)

def clean(amount):
    if not amount:
        return 0.0
    return float(amount.replace(",", ""))

def first(pattern, text, default=""):
    m = pattern.search(text)
    return m.group(1).strip() if m else default

def parse_invoice(pdf_path):
    with pdfplumber.open(pdf_path) as pdf:
        text = "\n".join(p.extract_text() or "" for p in pdf.pages)
    gstins = GSTIN_RE.findall(text)
    return {
        "file": pdf_path.name,
        "vendor_gstin": gstins[0] if gstins else "",
        "buyer_gstin": gstins[1] if len(gstins) > 1 else "",
        "invoice_no": first(INVOICE_RE, text),
        "date": first(DATE_RE, text),
        "taxable": clean(first(TAXABLE_RE, text)),
        "cgst": clean(first(CGST_RE, text)),
        "sgst": clean(first(SGST_RE, text)),
        "igst": clean(first(IGST_RE, text)),
        "total": clean(first(AMOUNT_RE, text)),
    }

def main(folder, out_csv="invoices.csv"):
    rows = [parse_invoice(p) for p in Path(folder).glob("*.pdf")]
    if not rows:
        print("No PDFs found.")
        return
    with open(out_csv, "w", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=rows[0].keys())
        writer.writeheader()
        writer.writerows(rows)
    print(f"Parsed {len(rows)} invoices -> {out_csv}")

if __name__ == "__main__":
    main(sys.argv[1], sys.argv[2] if len(sys.argv) > 2 else "invoices.csv")

Install and run:

pip install pdfplumber
python parse_gst.py ./invoices/ output.csv

How It Works

The GSTIN regex. A GSTIN is exactly 15 characters in a strict format: 2-digit state code, 5 letters (the entity's PAN prefix), 4 digits (PAN), 1 letter (PAN suffix), 1 digit (entity number within the state), 1 letter (Z by default), and 1 alphanumeric checksum. The pattern matches that exact shape and rejects everything else, so you won't accidentally pick up phone numbers, PIN codes, or random alphanumeric strings.

A typical invoice carries two GSTINs: the vendor's at the top, usually in the header block, and the buyer's in the bill-to section. The reading order pdfplumber returns is consistent for nearly every template I've tested, so taking the first two matches works in practice. If you want to be defensive, you can label each GSTIN by checking which one appears before the words "Bill To" in the extracted text.

The tax field regexes. Each tax field has the same structural pattern across templates: a label like CGST, then some whitespace or punctuation (@9%, Rs., INR, a colon, or a table cell separator), then the amount. The [^\d]{0,30} window lets the regex skip past anything non-numeric for up to 30 characters before grabbing the next amount. That window is wide enough to handle most table layouts but tight enough that it won't accidentally jump down to the next row's number.

The \b word boundary on CGST, SGST, and IGST matters more than it looks. Without it, you can match parts of compound labels and end up with the wrong number landing in the wrong column on every single invoice in the batch.

Why pdfplumber over PyPDF2 or pdfminer. pdfplumber preserves visual layout when extracting text, which means amounts in a totals box at the bottom of the page don't get scrambled with line items above them. PyPDF2 returns the same characters in document reading order, which sounds fine until you hit a multi-column invoice and CGST from row 3 ends up next to SGST from row 1. pdfplumber pays attention to coordinates, so structured fields stay structured.

Comma handling. Indian invoices write amounts as 1,23,456.00. Python's float() refuses to parse commas. The clean() helper strips them before casting, and returns 0.0 on missing matches rather than crashing — important when a single malformed PDF in a batch of thirty shouldn't kill the whole run.

What It Catches

Run it against a folder of 30 invoices and you get a CSV like:

file,vendor_gstin,buyer_gstin,invoice_no,date,taxable,cgst,sgst,igst,total
inv_001.pdf,27ABCDE1234F1Z5,29XYZAB5678G2Z9,INV-2025-001,15/05/2025,10000.00,900.0,900.0,0.0,11800.00
inv_002.pdf,27ABCDE1234F1Z5,06PQRST9876H3Z1,INV-2025-002,16/05/2025,25000.00,0.0,0.0,4500.0,29500.00

Pipe that into your GSTR-2A reconciliation sheet, sort by vendor_gstin, and you're done in five minutes instead of two hours.

What It Doesn't Catch (Yet)

Scanned PDFs are invisible to pdfplumber — it reads embedded text only. For scanned invoices, add a pytesseract OCR step before the text extraction; that's another ten lines and a brew install tesseract. Non-standard layouts (vendors who label GST as "Tax Amount" or split CGST across two rows) will defeat the regex. The fix is to print the extracted text for that file once, adjust the pattern, and never touch it again. Reverse-charge entries return the right total but with the tax direction flipped; if you process reverse-charge invoices regularly, add a flag check for "Reverse Charge: Yes" in the text.

Running It On Schedule

Drop the script in a cron job that runs nightly:

0 22 * * * cd /home/me/invoices && python parse_gst.py ./incoming output.csv

Now your accountant opens a fresh, accurate CSV every morning at 10am and the manual typing step disappears from the workflow.

Why I Like This Pattern

Sixty lines of regex over PDF text beats a ₹2000/month invoice OCR SaaS for 95% of real-world Indian invoices, and the 5% you can fix by reading one error message and tweaking one pattern. The entire script fits in your head, so when something breaks, you know exactly where to look. No vendor lock-in, no rate limits, no quarterly price hikes. Just Python and a regex book.

Build the boring thing yourself. Charge yourself ₹0 a month. Use the saved money for better chai.

Follow me on Twitter @automate_archit for daily AI automation tips.

Build a UPI Transaction Categorizer in 95 Lines of Python

Archit Mittal — Wed, 06 May 2026 12:53:51 +0000

If you use UPI for everything (and at this point, who in India doesn't?), your bank statement is a wall of cryptic merchant strings: UPI/zomatoonline@paytm/..., UPI/SWIGGY-BANGALORE/..., UPI/9876543210@oksbi/.... Trying to figure out where your money actually goes by reading that mess is a lost cause.

I built a 95-line Python script that ingests a UPI statement CSV from any major Indian bank, classifies each transaction into a sensible category, and spits out a monthly summary. No paid services, no APIs, no LLMs in the loop — just regex, a keyword map, and pandas.

Here's exactly how it works.

The problem

Every bank exports UPI transactions a little differently, but the merchant string is roughly always there. The trick is that real merchants leak through:

Zomato, Swiggy, Dunzo → food delivery
BigBasket, Blinkit, Zepto, DMart → groceries
Uber, Ola, Rapido, IRCTC → transport
Airtel, Jio, Vi, Tata Power, BESCOM → bills
Amazon, Flipkart, Myntra → shopping

If we maintain a keyword → category dictionary and walk every row, we can categorize 80–90% of statements automatically. The remaining slice falls into "Other" and you can label them by hand once a month.

The full code

Save this as upi_categorizer.py:

import re
import sys
from pathlib import Path
import pandas as pd

CATEGORIES = {
    "Food": [
        "zomato", "swiggy", "dunzo", "eatfit", "faasos", "behrouz",
        "dominos", "pizzahut", "kfc", "mcdonalds", "starbucks", "cafe",
    ],
    "Groceries": [
        "bigbasket", "blinkit", "grofers", "zepto", "dmart", "instamart",
        "reliancefresh", "natures", "spencer", "more-retail",
    ],
    "Transport": [
        "uber", "olacabs", "rapido", "irctc", "redbus", "abhibus",
        "namma-yatri", "blusmart", "yulu", "vogo",
    ],
    "Bills": [
        "airtel", "jio", "vodafone", "vi-mobile", "bsnl", "tatapower",
        "bescom", "adani-electricity", "torrent-power", "mahanagargas",
        "act-fibernet", "hathway",
    ],
    "Shopping": [
        "amazon", "flipkart", "myntra", "ajio", "nykaa", "meesho",
        "tatacliq", "firstcry", "lenskart", "boat-lifestyle",
    ],
    "Investments": [
        "zerodha", "groww", "upstox", "kuvera", "paytmmoney",
        "indmoney", "angelone", "smallcase",
    ],
    "Health": [
        "pharmeasy", "1mg", "netmeds", "apollo", "practo",
        "cult-fit", "healthifyme",
    ],
    "Entertainment": [
        "netflix", "hotstar", "primevideo", "sonyliv", "zee5",
        "spotify", "gaana", "bookmyshow", "pvr", "inox",
    ],
}

UPI_PATTERN = re.compile(r"upi[/-]([a-z0-9.\-_@]+)", re.IGNORECASE)


def extract_merchant(narration: str) -> str:
    """Pull the merchant slug out of a UPI narration string."""
    if not isinstance(narration, str):
        return ""
    match = UPI_PATTERN.search(narration.lower())
    return match.group(1) if match else narration.lower()


def categorize(narration: str) -> str:
    merchant = extract_merchant(narration)
    for category, keywords in CATEGORIES.items():
        for keyword in keywords:
            if keyword in merchant:
                return category
    return "Other"


def load_statement(path: Path) -> pd.DataFrame:
    df = pd.read_csv(path)
    # Normalize the columns most banks use; adjust as needed.
    df.columns = [c.strip().lower() for c in df.columns]
    rename_map = {
        "transaction date": "date", "txn date": "date", "value date": "date",
        "narration": "narration", "description": "narration", "details": "narration",
        "withdrawal amt.": "debit", "withdrawal": "debit", "debit": "debit",
        "deposit amt.": "credit", "deposit": "credit", "credit": "credit",
    }
    df = df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns})
    df["debit"] = pd.to_numeric(df.get("debit", 0), errors="coerce").fillna(0)
    df["date"] = pd.to_datetime(df["date"], errors="coerce", dayfirst=True)
    return df.dropna(subset=["date"])


def summarize(df: pd.DataFrame) -> pd.DataFrame:
    df = df[df["debit"] > 0].copy()
    df["category"] = df["narration"].apply(categorize)
    df["month"] = df["date"].dt.to_period("M")
    pivot = df.pivot_table(
        index="month", columns="category", values="debit",
        aggfunc="sum", fill_value=0,
    )
    pivot["Total"] = pivot.sum(axis=1)
    return pivot.round(0)


if __name__ == "__main__":
    path = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("statement.csv")
    summary = summarize(load_statement(path))
    print(summary.to_string())
    summary.to_csv("upi_summary.csv")
    print("\nSaved upi_summary.csv")

How to use it

Log in to net banking and download your last 3-6 months of statement as CSV.
Save it as statement.csv in the same folder as the script.
Run python upi_categorizer.py statement.csv.

You'll get a monthly pivot like:

month     Bills  Food  Groceries  Shopping  Transport  Other  Total
2026-02    3450  6280       9120      4500       2150   1820  27320
2026-03    3450  5840      10100      6700       1980   2310  30380
2026-04    3450  7120       9550      3200       2620   1950  27890

Three months in, you have a ground-truth picture of where roughly ₹85,000 went.

Tweaks worth making

The keyword list is the soul of this script. Spend ten minutes adding the merchants YOUR statement actually shows — the long tail is regional (Big Bazaar in some cities, Star Bazaar in others, local kirana with their own UPI handle). Every keyword you add today saves you from "Other" forever.

If you want to go further:

Auto-tag recurring UPI handles: track per-handle medians and flag transactions that deviate by 3x.
Detect EMIs: same amount, same merchant, monthly cadence — pull these into their own bucket so they don't pollute "Shopping".
Streak the savings rate: subtract Total from monthly credits and chart the gap.

I run a version of this on the 1st of every month via cron. Takes 2 seconds, replaces a 30-minute manual sort, and the only thing I update is the keyword list.

Why I stopped using budget apps for this

Two reasons. One, I don't love handing over read-only bank credentials to a third party I haven't audited. Two, every budget app eventually starts pushing premium tiers, and the ones that survive long enough to be useful tend to be acquired and shut down. A 95-line script that runs on my laptop has none of those problems.

The keyword map is the only thing that needs maintenance, and that's a feature — your categories should match how YOU spend, not how a startup's data scientist thought you'd spend.

If you want more scripts like this (PDF invoice parsers, GST reconciliation, WhatsApp/Telegram automations), follow me on Twitter @automate_archit for daily AI automation tips.

Build a Bulk PDF Invoice Renamer in 55 Lines of Python

Archit Mittal — Fri, 01 May 2026 12:53:58 +0000

Last month, my CA messaged me at 11pm: "Bhai, send all April invoices in a single zip, named properly." I had 47 PDF invoices in my Downloads folder, all with names like Invoice (3).pdf, download.pdf, and INV_FINAL_FINAL_v2.pdf.

Two hours of manual renaming wasn't on my agenda. So I wrote a Python script. By the time my chai was done, the script was done. 47 invoices renamed, audit-ready.

Here's the entire thing — 55 lines, no fluff.

What it does

The script scans a folder of PDF invoices and renames each file to a clean, sortable format:

2026-04-15_AcmeServices_INV-1042_15000.pdf

It pulls four things from each invoice: the invoice date, vendor name (from the GSTIN line), invoice number, and total amount in ₹. If a field is missing, it falls back to a placeholder so nothing gets lost.

The Code

import re
import sys
from pathlib import Path
from datetime import datetime
import pdfplumber

DATE_RE = re.compile(r"(\d{1,2})[\-/\s]([A-Za-z]{3,9}|\d{1,2})[\-/\s](\d{2,4})")
GSTIN_RE = re.compile(r"\b\d{2}[A-Z]{5}\d{4}[A-Z][A-Z\d]Z[A-Z\d]\b")
INV_RE = re.compile(r"(?:Invoice\s*(?:No\.?|#|Number)\s*[:\-]?\s*)([A-Z0-9\-/]+)", re.I)
AMT_RE = re.compile(r"(?:Total|Grand\s*Total|Amount\s*Payable)[^\d₹]{0,15}₹?\s*([\d,]+\.?\d{0,2})", re.I)

def parse_date(text):
    m = DATE_RE.search(text)
    if not m: return None
    raw = m.group(0)
    for fmt in ("%d-%m-%Y", "%d/%m/%Y", "%d %b %Y", "%d %B %Y", "%d-%b-%Y"):
        try:
            return datetime.strptime(raw, fmt).strftime("%Y-%m-%d")
        except ValueError:
            continue
    return None

def vendor_from_gstin(text):
    m = GSTIN_RE.search(text)
    if not m: return None
    line = next((l for l in text.splitlines() if m.group(0) in l), "")
    parts = [p.strip() for p in line.split(m.group(0)) if p.strip()]
    return re.sub(r"[^A-Za-z0-9]+", "", parts[0])[:20] if parts else None

def extract(pdf_path):
    with pdfplumber.open(pdf_path) as pdf:
        text = "\n".join((p.extract_text() or "") for p in pdf.pages[:2])
    inv = INV_RE.search(text)
    amt = AMT_RE.search(text)
    return {
        "date": parse_date(text) or "NoDate",
        "vendor": vendor_from_gstin(text) or "UnknownVendor",
        "invoice": (inv.group(1) if inv else "NoInv").replace("/", "-"),
        "amount": (amt.group(1).replace(",", "").split(".")[0] if amt else "0"),
    }

def safe_rename(src, new_name):
    dest = src.with_name(f"{new_name}.pdf")
    n = 1
    while dest.exists() and dest != src:
        dest = src.with_name(f"{new_name}_{n}.pdf")
        n += 1
    src.rename(dest)
    return dest.name

def main(folder):
    folder = Path(folder)
    for pdf in folder.glob("*.pdf"):
        try:
            f = extract(pdf)
            new = f"{f['date']}_{f['vendor']}_{f['invoice']}_{f['amount']}"
            renamed = safe_rename(pdf, new)
            print(f"OK {pdf.name} -> {renamed}")
        except Exception as e:
            print(f"FAIL {pdf.name}: {e}")

if __name__ == "__main__":
    main(sys.argv[1] if len(sys.argv) > 1 else ".")

How it works

Four regexes do most of the heavy lifting.

The date regex matches common Indian invoice formats: 15/04/2026, 15-Apr-2026, 15 April 2026. The parser tries five strptime formats and keeps the first that sticks.

The GSTIN regex matches the 15-character GST identifier (state code + PAN + entity code + Z + checksum). Once found, the script grabs the text on the same line and assumes that's the vendor name. Crude but works in 90% of real invoices I've seen.

The invoice number regex looks for "Invoice No", "Invoice #", or "Invoice Number" followed by an alphanumeric token. The amount regex scans for "Total", "Grand Total", or "Amount Payable" followed by a ₹ value, then strips commas and decimals so file sorting works.

pdfplumber only reads the first two pages of each PDF — invoice headers and totals almost always appear there, and skipping deeper pages keeps it fast. On my 47-file folder it finished in 8 seconds.

Running it

pip install pdfplumber
python rename_invoices.py ~/Downloads/april_invoices

Output:

OK Invoice (3).pdf -> 2026-04-15_AcmeServices_INV-1042_15000.pdf
OK download.pdf -> 2026-04-18_TataCloud_TC-2026-099_47200.pdf
OK INV_FINAL_FINAL_v2.pdf -> 2026-04-22_FreshworksIndia_FW8821_8400.pdf

Edge cases I hit (and you will too)

Scanned PDFs. pdfplumber returns empty text for image-only PDFs. The try/except in main skips them without crashing — but you'll need OCR (Tesseract via pytesseract) to actually read them. I keep a separate folder for scanned ones.

Multi-page invoices with the total on page 3+. Rare in B2B India, but if you hit it, change pdf.pages[:2] to pdf.pages — slower but complete.

Vendors without GSTIN. International vendors won't have one. The fallback "UnknownVendor" gets used; you can extend vendor_from_gstin to also scan the first line of the document.

Duplicate filenames. safe_rename adds a _1, _2 suffix instead of overwriting. I learned this the hard way after losing a Tata Cloud invoice to a clash.

Why this format

Sorting by filename gives you a chronological audit trail. Your CA can scan a folder and see April's invoices in date order without opening anything. The amount in the filename is a sanity check — quick visual confirmation when reconciling with bank statements.

Where to go from here

A few extensions I've layered on top of this base script:

Pipe the extracted data straight into a Google Sheet for monthly P&L
Move processed PDFs into year/month subfolders
Flag any invoice where the GSTIN checksum doesn't validate (there's a published algorithm)
Send a Telegram alert if any single invoice crosses a threshold

55 lines was the goal. Fancier tooling can wait until the basics work.

Follow me on Twitter @automate_archit for daily AI automation tips.

Build a GST Invoice PDF Extractor in 53 Lines of Python

Archit Mittal — Wed, 29 Apr 2026 12:52:54 +0000

If you run a small business in India, you've felt this pain: a folder full of PDF invoices from different vendors, every one formatted slightly differently, and somebody has to type GSTIN, invoice number, date, and total amount into a spreadsheet. I built a single-file Python script that processes the whole folder in seconds and validates GSTINs along the way.

Total: 53 lines including imports and CLI plumbing. Here's the whole thing.

What it does

Walks a folder of PDF invoices
Pulls out GSTINs (vendor + buyer) and validates the 15-character format
Grabs invoice number, date, and total amount with regex
Writes a clean CSV you can paste into Tally, Zoho Books, or any ledger

The code

# invoice_extractor.py
import re, sys, csv
from pathlib import Path
import pdfplumber

GSTIN_RE   = re.compile(r'\b(\d{2}[A-Z]{5}\d{4}[A-Z][A-Z\d]Z[A-Z\d])\b')
INV_NUM_RE = re.compile(r'Invoice\s*(?:No\.?|Number|#)\s*[:\-]?\s*([A-Z0-9\-/]+)', re.I)
DATE_RE    = re.compile(r'(\d{2}[\-/]\d{2}[\-/]\d{4})')
AMOUNT_RE  = re.compile(r'(?:Grand\s*Total|Total|Amount)[^\d]*(?:Rs\.?|INR)?\s*([\d,]+\.\d{0,2})', re.I)

def is_valid_gstin(g: str) -> bool:
    if len(g) != 15:
        return False
    state = int(g[:2])
    return 1 <= state <= 38

def extract_from_pdf(path: Path) -> dict:
    text = ""
    with pdfplumber.open(path) as pdf:
        for page in pdf.pages:
            text += (page.extract_text() or "") + "\n"
    gstins  = [g for g in GSTIN_RE.findall(text) if is_valid_gstin(g)]
    inv     = INV_NUM_RE.search(text)
    date    = DATE_RE.search(text)
    amounts = [float(a.replace(",", "")) for a in AMOUNT_RE.findall(text)]
    return {
        "file":         path.name,
        "vendor_gstin": gstins[0] if gstins else "",
        "buyer_gstin":  gstins[1] if len(gstins) > 1 else "",
        "invoice_no":   inv.group(1) if inv else "",
        "date":         date.group(1) if date else "",
        "total_inr":    max(amounts, default=0.0),
    }

def main(folder: str) -> None:
    rows = [extract_from_pdf(p) for p in sorted(Path(folder).glob("*.pdf"))]
    if not rows:
        print("No PDFs found."); return
    out = Path(folder) / "invoices_extracted.csv"
    with out.open("w", newline="") as f:
        w = csv.DictWriter(f, fieldnames=rows[0].keys())
        w.writeheader(); w.writerows(rows)
    print(f"Processed {len(rows)} invoices then wrote {out}")

if __name__ == "__main__":
    main(sys.argv[1] if len(sys.argv) > 1 else ".")

Save it as invoice_extractor.py, drop a folder of PDFs next to it, then:

pip install pdfplumber
python invoice_extractor.py ./invoices

You'll get an invoices_extracted.csv with one row per PDF.

How the GSTIN regex works

A valid GSTIN is exactly 15 characters laid out like this:

2-digit state code
5-letter PAN prefix
4-digit PAN middle
1-letter PAN suffix
1 entity number (alphanumeric)
Literal Z
1 checksum character (alphanumeric)

The regex captures that shape and is_valid_gstin adds a sanity check on the state code (1 through 38 covers every state and union territory). The 15th character is technically a mod-36 checksum, so you can layer that in if you want bulletproof matching. For 95 percent of accounting cleanup, the format check is enough.

Why the amount regex uses `max(amounts)`

Indian invoices repeat amounts everywhere: subtotal, CGST, SGST, IGST, taxable value, grand total. Taking the largest match from anything that follows "Total", "Grand Total" or "Amount" almost always lands on the grand total. If your vendors have weirder layouts, tighten the regex to require "Grand Total" specifically and skip "Total" alone.

A real example

I tested this on 47 PDF invoices from one quarter of vendor bills (electricity, internet, three SaaS subscriptions, freight, the works). It processed them in 6.2 seconds on an M1 Air. Two had no extractable text because they were scans, which is the OCR case I describe below. The other 45 came out clean and tied to the Tally ledger on the first try.

Sample output row:

file=ApolloPharmacy_Mar2026.pdf
vendor_gstin=29AABCA1234B1Z7
buyer_gstin=29AAACS5678E1ZR
invoice_no=AP/2526/00412
date=18-03-2026
total_inr=14237.50

Common edge cases and how to handle them

Scanned PDFs. pdfplumber only extracts embedded text. If a vendor sends you a scan, extract_text() returns empty. Add a pytesseract fallback that runs OCR when the text count is below a threshold.

Multi-page invoices. Already handled. The loop concatenates every page before regex runs.

Wrong amount picked up. Happens when the invoice has a "Late fee total" line above the grand total. Fix by requiring "Grand Total" or by sorting by position rather than value.

Date in DD-MMM-YYYY format. The regex matches numeric dates only. Add an alternation for \d{2}[\-/\s][A-Za-z]{3}[\-/\s]\d{4} if your vendors use month names.

What I would add next

Tally XML export instead of CSV (one extra function, maps the dict to Tally's voucher schema)
Dedup detection by (vendor_gstin, invoice_no) to catch double entries before they hit the books
Date normalization to ISO so it sorts in Excel
OCR fallback with pytesseract for scanned PDFs
Batch upload to a Google Sheet using the Sheets API instead of writing a local CSV

The bigger point

The whole script is one file. No frameworks, no databases, no scheduler dependency. Drop it in a cron job or a Windows Task Scheduler entry and your books update themselves at midnight before you get to the office.

If you have ever paid a junior accountant for two hours a day to do data entry on PDFs, this is the kind of automation that recovers that cost in a week. At even a conservative 200 invoices a month at 3 minutes each, you are looking at 10 hours per month, every month, that goes back into work that actually moves the business.

Steal the code. Tweak the regex for whatever your vendors do. Ship it.

Follow me on Twitter @automate_archit for daily AI automation tips.

Build a UPI Transaction Reconciliation Tool in 52 Lines of Python

Archit Mittal — Mon, 27 Apr 2026 12:53:44 +0000

I track every UPI transaction across PhonePe, Google Pay, and Paytm. Three apps, three different CSV exports, and a tax season that looks like a hostage negotiation.

Last weekend I got tired of opening three spreadsheets side by side and built one Python script to merge, deduplicate, and categorize every UPI transaction into a single reconciled Excel file. The whole thing is 52 lines.

Here's what it does:

Reads CSV exports from any of the three UPI apps
Normalizes the wildly different column names each app uses
Detects duplicates (the same payment showing up in two apps)
Categorizes transactions by merchant keywords (food, fuel, rent, bills, shopping)
Exports a clean Excel file with a transactions sheet and a monthly category pivot in ₹

The Code

import pandas as pd
from pathlib import Path
from datetime import datetime

SCHEMA = {
    "phonepe": {"date": "Transaction Date", "amount": "Amount (INR)", "party": "Merchant", "ref": "UTR"},
    "gpay":    {"date": "Date",             "amount": "Amount",       "party": "To",       "ref": "Transaction ID"},
    "paytm":   {"date": "Txn Date",         "amount": "Amount(₹)",    "party": "Payee",    "ref": "Order ID"},
}

CATEGORIES = {
    "food":  ["zomato", "swiggy", "dominos", "kfc", "mcdonald"],
    "fuel":  ["hpcl", "iocl", "bpcl", "petrol", "indian oil"],
    "rent":  ["rent", "landlord", "housing"],
    "bills": ["airtel", "jio", "vi ", "tata power", "electricity"],
    "shop":  ["amazon", "flipkart", "myntra", "blinkit", "zepto"],
}

def categorize(party: str) -> str:
    p = (party or "").lower()
    for cat, keywords in CATEGORIES.items():
        if any(k in p for k in keywords):
            return cat
    return "other"

def load(path: Path, app: str) -> pd.DataFrame:
    df = pd.read_csv(path)
    cols = SCHEMA[app]
    out = pd.DataFrame({
        "date":   pd.to_datetime(df[cols["date"]], errors="coerce"),
        "amount": pd.to_numeric(df[cols["amount"]].astype(str).str.replace("[₹,]", "", regex=True), errors="coerce"),
        "party":  df[cols["party"]].astype(str),
        "ref":    df[cols["ref"]].astype(str),
        "app":    app,
    })
    out["category"] = out["party"].apply(categorize)
    return out.dropna(subset=["date", "amount"])

def reconcile(folder: Path) -> pd.DataFrame:
    frames = [load(p, app) for app in SCHEMA for p in folder.glob(f"{app}*.csv")]
    if not frames:
        raise SystemExit("No UPI CSVs found.")
    combined = pd.concat(frames, ignore_index=True).sort_values("date")
    combined["minute"] = combined["date"].dt.floor("min")
    combined = combined.drop_duplicates(subset=["minute", "amount", "party"], keep="first").drop(columns="minute")
    combined["month"] = combined["date"].dt.to_period("M").astype(str)
    return combined

def export(df: pd.DataFrame, out_path: Path) -> None:
    monthly = df.pivot_table(index="month", columns="category", values="amount", aggfunc="sum", fill_value=0)
    with pd.ExcelWriter(out_path, engine="openpyxl") as xl:
        df.to_excel(xl, sheet_name="transactions", index=False)
        monthly.to_excel(xl, sheet_name="monthly_by_category")
    print(f"Saved {len(df)} transactions to {out_path}")

if __name__ == "__main__":
    df = reconcile(Path("./statements"))
    export(df, Path(f"upi_reconciled_{datetime.now():%Y_%m_%d}.xlsx"))

How it works

The schema dict. PhonePe calls the date column "Transaction Date." GPay calls it "Date." Paytm calls it "Txn Date." Same data, three names. The SCHEMA dict maps each app's column names to a standard set so the rest of the code never has to care which app a row came from. Adding a new app means one more entry in SCHEMA and zero changes anywhere else.

Amount cleanup. Paytm sneaks the ₹ symbol into the amount column. PhonePe uses comma-formatted numbers. The str.replace("[₹,]", "", regex=True) strips both before pd.to_numeric, and errors="coerce" turns any unparseable cell into NaN so the final dropna can sweep it out.

The deduplication trick. When the same UPI transaction shows up in two apps (which happens when you screenshot a payment and Paytm catalogs it from your gallery, or when GPay picks up an SMS confirmation Paytm has already logged), the timestamps usually differ by a few seconds. Floor the timestamp to the minute, drop_duplicates on (minute, amount, party), and you keep one copy of each real transaction. After three months of data this cut my row count by about 18 percent.

Categorization. A simple substring match on the merchant name. Add your own keywords to CATEGORIES. After three months of running this, my "food" category alone has 47 distinct merchant strings (different Zomato franchise codes, mis-spellings, you name it) that all map to the same bucket.

The export. Two sheets in one Excel file. transactions has every reconciled row. monthly_by_category pivots them into a grid you can paste straight into your CA's template at year-end.

How I actually use it

I export CSVs from each app on the first weekend of every month and drop them in a statements/ folder named like phonepe_april.csv, gpay_april.csv, paytm_april.csv. Run the script, get a single Excel file. The whole reconciliation that used to eat my Sunday morning now takes twelve seconds.

The categorization saved me during my GST filing for Q4 — I could see immediately that I'd spent ₹38,000 on shopping vs the ₹12,000 I'd loosely budgeted. Painful but useful. It also caught a ₹1,499 recurring charge from a streaming service I'd cancelled in November but was still being billed for. That one find paid for the entire weekend project.

Where to take it next

Three things I added after the first draft:

A --month CLI flag that filters to a single month — useful when slicing a quarter for tax filing.
A manual_overrides.csv so I can tag a specific UTR with the right category without polluting the keyword list. The override file gets joined on the ref column and wins over the keyword match.
A simple matplotlib bar chart of category totals, saved as a PNG, dropped into my monthly review note.

If you want the version with all three additions, ping me — happy to share. The base script lives in my python-automation-starter repo on GitHub.

A note on edge cases: refunds show up as positive amounts in PhonePe but negative in GPay. If you care about net spend, multiply PhonePe rows where the merchant string contains "refund" by -1 before reconciling. I do this in a tiny preprocessor function I left out of the 52-line version for brevity — it's three lines and you'll know where to put it.

Twelve seconds. Three apps. One Excel file. Worth a Sunday afternoon.

Follow me on Twitter @automate_archit for daily AI automation tips.

Build a GST Invoice Generator in 80 Lines of Python

Archit Mittal — Sun, 26 Apr 2026 04:12:47 +0000

If you're a freelancer, consultant, or small business owner in India, you've probably paid ₹500–₹2000/month for invoicing software you barely use. After helping a CA friend automate his client billing last week, I wrote a clean GST invoice generator in 80 lines of Python. It handles HSN codes, CGST/SGST split for intra-state, IGST for inter-state, and outputs a professional PDF — all from a simple dictionary input.

This is the same script I now use to invoice my own automation consulting clients. Total cost: zero. Total time saved: about three hours per month.

What it does

Pass in your business details, the buyer's GSTIN, line items, and tax rates. The script:

Auto-detects whether the transaction is intra-state (split into CGST + SGST) or inter-state (single IGST line).
Computes line-level taxable amount, tax, and total.
Generates a clean A4 PDF with your branding, HSN codes, and the mandatory GST breakdown.
Names the file with the invoice number so it's audit-ready.

The full script

You'll need one library:

pip install reportlab

Here's the entire generator. Save it as gst_invoice.py:

from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Spacer
from reportlab.lib.styles import getSampleStyleSheet
from datetime import date

def generate_gst_invoice(seller, buyer, items, invoice_no, inv_date=None):
    inv_date = inv_date or date.today().isoformat()
    same_state = seller["state_code"] == buyer["state_code"]
    rows = [["#", "Description", "HSN", "Qty", "Rate", "Taxable", "Tax%", "Tax", "Total"]]
    sub_total = total_tax = grand_total = 0

    for i, it in enumerate(items, 1):
        taxable = round(it["qty"] * it["rate"], 2)
        tax = round(taxable * it["gst"] / 100, 2)
        line_total = round(taxable + tax, 2)
        sub_total += taxable
        total_tax += tax
        grand_total += line_total
        rows.append([i, it["desc"], it["hsn"], it["qty"],
                     f"{it['rate']:.2f}", f"{taxable:.2f}",
                     f"{it['gst']}%", f"{tax:.2f}", f"{line_total:.2f}"])

    if same_state:
        tax_rows = [["CGST", f"{total_tax/2:.2f}"], ["SGST", f"{total_tax/2:.2f}"]]
    else:
        tax_rows = [["IGST", f"{total_tax:.2f}"]]
    tax_rows.append(["Grand Total (INR)", f"{grand_total:.2f}"])

    filename = f"Invoice_{invoice_no}.pdf"
    doc = SimpleDocTemplate(filename, pagesize=A4, title=f"Invoice {invoice_no}")
    styles = getSampleStyleSheet()
    story = []

    story.append(Paragraph(f"<b>{seller['name']}</b>", styles["Title"]))
    story.append(Paragraph(f"{seller['address']}<br/>GSTIN: {seller['gstin']}", styles["Normal"]))
    story.append(Spacer(1, 12))
    story.append(Paragraph(f"<b>Tax Invoice #{invoice_no}</b>   Date: {inv_date}", styles["Heading3"]))
    story.append(Paragraph(f"<b>Bill To:</b> {buyer['name']}<br/>{buyer['address']}<br/>GSTIN: {buyer['gstin']}", styles["Normal"]))
    story.append(Spacer(1, 12))

    item_table = Table(rows, repeatRows=1, hAlign="LEFT")
    item_table.setStyle(TableStyle([
        ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#1f2937")),
        ("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
        ("GRID", (0, 0), (-1, -1), 0.4, colors.grey),
        ("FONTSIZE", (0, 0), (-1, -1), 9),
        ("ALIGN", (3, 1), (-1, -1), "RIGHT"),
    ]))
    story.append(item_table)
    story.append(Spacer(1, 12))

    summary = Table(tax_rows, hAlign="RIGHT", colWidths=[140, 90])
    summary.setStyle(TableStyle([
        ("FONTNAME", (0, -1), (-1, -1), "Helvetica-Bold"),
        ("LINEABOVE", (0, -1), (-1, -1), 0.8, colors.black),
        ("ALIGN", (1, 0), (1, -1), "RIGHT"),
    ]))
    story.append(summary)
    story.append(Spacer(1, 18))
    story.append(Paragraph("<i>This is a computer-generated invoice and does not require a signature.</i>", styles["Italic"]))

    doc.build(story)
    return filename


if __name__ == "__main__":
    seller = {"name": "Mittal Automation Studio", "address": "Sector 62, Noida, UP",
              "gstin": "09ABCDE1234F1Z5", "state_code": "09"}
    buyer  = {"name": "Acme Retail Pvt Ltd", "address": "Bandra West, Mumbai, MH",
              "gstin": "27FGHIJ5678K2Z9", "state_code": "27"}
    items = [
        {"desc": "Workflow automation setup", "hsn": "998313", "qty": 1, "rate": 45000, "gst": 18},
        {"desc": "Monthly support retainer",   "hsn": "998313", "qty": 3, "rate": 12000, "gst": 18},
    ]
    out = generate_gst_invoice(seller, buyer, items, "INV-2026-014")
    print(f"Generated: {out}")

How it works, in plain English

The state-code check is the entire trick. Indian GST law splits tax based on whether buyer and seller are in the same state — if yes, you charge CGST + SGST (each half the rate); if no, you charge IGST (full rate). Here we read the first two digits of the GSTIN (which encode the state) and branch accordingly.

The line-item loop computes taxable value, tax, and total per row, then accumulates the grand total. Everything else is layout — the seller block on top, the bill-to in the middle, line items in a dark-header table, and a right-aligned summary with the legally required tax breakdown.

Extending it

A few easy upgrades I've added in production:

Numbers in words. GST law requires the total amount to be spelled out. Add num2words(grand_total, lang="en_IN") and append it to the summary.
QR code. RBI now requires a UPI/payment QR on B2B invoices over ₹500 crore turnover. Generate one with qrcode and embed it via Image from reportlab.
Bulk run. Wrap generate_gst_invoice in a loop over a CSV. I produce 40 client invoices on the 1st of every month with a single command.
Email send. Pipe the PDF into smtplib and you've replaced a ₹1500/month invoicing tool entirely.

Why this beats SaaS for solo operators

The real win isn't the ₹18,000/year you save. It's that the invoice template, the numbering scheme, and the data layout all live in your repo. When your CA asks for a year's worth of invoices in a specific format, it's a ten-second loop, not a support ticket.

If you're billing fewer than 200 invoices a month and your needs are reasonably stable, an 80-line script will outlast three SaaS subscription cycles.

Follow me on Twitter @automate_archit for daily AI automation tips. Code for this project: github.com/automate-archit/python-automation-starter

Build Your First AI Agent in 60 Lines of Python — No Framework Needed

Archit Mittal — Sat, 25 Apr 2026 01:44:03 +0000

Everyone's talking about AI agents, but most tutorials start with heavyweight frameworks, API keys, and 500+ lines of boilerplate.

What if you could build a functional AI agent in just 60 lines of pure Python?

No LangChain. No CrewAI. No framework at all.

Let's do it.

What We're Building

A simple AI agent that can:

Take a goal as input
Break it into subtasks
Execute each subtask using tools
Return a final result

Think of it as the minimal viable agent — the skeleton you can extend into anything.

The Architecture

Our agent follows the classic Observe → Think → Act loop:

┌─────────────┐
│   USER GOAL │
└──────┬──────┘
       │
       ▼
┌──────────────┐
│  THINK (LLM) │◄──────┐
└──────┬───────┘       │
       │               │
       ▼               │
┌──────────────┐       │
│  ACT (Tool)  │───────┘
└──────┬───────┘
       │
       ▼
┌──────────────┐
│    RESULT    │
└──────────────┘

The Code (60 Lines)

import json
import os
from anthropic import Anthropic

client = Anthropic()

# Define tools the agent can use
tools = [
    {
        "name": "calculator",
        "description": "Performs arithmetic calculations",
        "input_schema": {
            "type": "object",
            "properties": {
                "expression": {"type": "string", "description": "Math expression to evaluate"}
            },
            "required": ["expression"]
        }
    },
    {
        "name": "search_notes",
        "description": "Search through local notes/files",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"}
            },
            "required": ["query"]
        }
    }
]

def execute_tool(name, input_data):
    if name == "calculator":
        try:
            return str(eval(input_data["expression"]))
        except:
            return "Error: Invalid expression"
    elif name == "search_notes":
        # Simulate search — replace with real implementation
        return f"Found 3 notes matching '{input_data['query']}'"
    return "Unknown tool"

def run_agent(goal, max_steps=5):
    messages = [{"role": "user", "content": goal}]
    system = "You are an AI agent. Use the provided tools to accomplish the user's goal. When done, provide a final answer."

    for step in range(max_steps):
        response = client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=1024,
            system=system,
            tools=tools,
            messages=messages
        )

        # Check if agent wants to use a tool
        if response.stop_reason == "tool_use":
            tool_block = next(b for b in response.content if b.type == "tool_use")
            result = execute_tool(tool_block.name, tool_block.input)
            messages.append({"role": "assistant", "content": response.content})
            messages.append({"role": "user", "content": [{"type": "tool_result", "tool_use_id": tool_block.id, "content": result}]})
            print(f"Step {step+1}: Used {tool_block.name} → {result}")
        else:
            # Agent is done
            final = next(b.text for b in response.content if hasattr(b, "text"))
            print(f"\nAgent Result: {final}")
            return final

    return "Max steps reached"

# Run it
run_agent("What is 15% of 847, then round to nearest integer?")

How It Works

Lines 1-4: Import dependencies. We only need the Anthropic SDK.

Lines 6-30: Define tools as JSON schemas. Claude understands these natively — no wrapper needed.

Lines 32-40: Tool execution. Dead simple if/else. Replace with real implementations (API calls, database queries, file operations).

Lines 42-65: The agent loop. This is the core. Send messages → check if Claude wants a tool → execute → feed result back → repeat.

Why This Matters

Every "agent framework" is essentially wrapping this exact loop with abstractions. Understanding the raw pattern means:

You debug faster — no framework magic to untangle
You extend easier — add tools by adding elif blocks
You own the code — no dependency updates breaking your agent

What to Build Next

From this 60-line skeleton, you can build:

A personal assistant — add calendar, email, and file tools
A code reviewer — add git diff and lint tools
A content pipeline — add publishing APIs as tools (I use this exact pattern to automate my content across 6 platforms)
A data analyst — add SQL and chart tools

The Takeaway

You don't need a framework to build AI agents. You need:

An LLM that supports tool use (Claude, GPT-4, etc.)
A loop that feeds tool results back
Tools defined as JSON schemas

That's it. 60 lines. No magic.

I'm Archit — I automate chaos. Follow me for more practical AI automation tutorials.

Currently building a system that runs scheduled AI agents across 6 platforms daily with zero human intervention. More on that soon.

How I Saved a Mumbai CA Firm ₹18 Lakh/Year by Automating GST Invoice Reconciliation

Archit Mittal — Wed, 22 Apr 2026 04:57:08 +0000

Last month, I worked with a mid-sized CA firm in Andheri, Mumbai. They had 4 junior accountants whose full-time job was reconciling GSTR-2B data with purchase invoices for 80+ clients. Each reconciliation took 6-8 hours per client per month.

The math was brutal: 4 accountants × ₹35,000/month × 12 months = ₹16.8 lakh/year in salaries, plus ₹1.2 lakh in infrastructure and overhead. Total: ₹18 lakh/year of manual work that a 200-line Python script could replace.

Here is exactly how I did it.

The Problem

Every month, Indian businesses running on GST deal with two data sources:

GSTR-2B JSON from the GST portal — purchases reported by your suppliers.
Purchase register exported from Tally, Zoho Books, Busy, or plain Excel.

An accountant then matches invoice numbers, GSTIN, taxable values, and tax amounts row by row. Mismatches — wrong GSTIN, missing invoices, ITC eligibility flags — get flagged manually. It is tedious, error-prone, and scales linearly with client count.

The firm's owner told me plainly: "We quote ₹5,000/client for monthly reconciliation. We spend ₹8,000 worth of accountant hours on it. Every new client makes us less profitable."

That was the unit economics problem I needed to fix.

The Solution: A 3-Part Python Pipeline

I built a three-step pipeline that runs in about 4 minutes per client instead of 8 hours.

Step 1: Parse GSTR-2B JSON

The JSON from the GST portal is nested. Flatten it into a reconciliation-ready DataFrame:

import json
import pandas as pd
from pathlib import Path

def parse_gstr2b(json_path: Path) -> pd.DataFrame:
    """Flatten GSTR-2B JSON into a reconciliation-ready DataFrame."""
    with open(json_path, 'r', encoding='utf-8') as f:
        data = json.load(f)

    rows = []
    b2b_section = data.get('data', {}).get('docdata', {}).get('b2b', [])

    for supplier in b2b_section:
        gstin = supplier.get('ctin', '')
        supplier_name = supplier.get('trdnm', '')

        for invoice in supplier.get('inv', []):
            rows.append({
                'supplier_gstin': gstin,
                'supplier_name': supplier_name,
                'invoice_no': invoice.get('inum', '').strip().upper(),
                'invoice_date': invoice.get('dt', ''),
                'taxable_value': float(invoice.get('val', 0)),
                'igst': float(invoice.get('iamt', 0)),
                'cgst': float(invoice.get('camt', 0)),
                'sgst': float(invoice.get('samt', 0)),
                'itc_eligible': invoice.get('itcavl', 'Y') == 'Y',
            })

    return pd.DataFrame(rows)

Step 2: Normalize the Purchase Register

Client data comes in 15 different formats. Column names are inconsistent across Tally, Zoho, Busy, and handcrafted Excel sheets. I normalize everything to the same schema before reconciling:

def load_purchase_register(file_path: Path) -> pd.DataFrame:
    """Handle Tally exports, Zoho CSVs, and client Excel files."""
    if file_path.suffix == '.csv':
        df = pd.read_csv(file_path)
    else:
        df = pd.read_excel(file_path, sheet_name=0)

    # Column aliases seen across 80+ clients
    alias_map = {
        'GSTIN': 'supplier_gstin',
        'Party GSTIN': 'supplier_gstin',
        'Vendor GSTIN': 'supplier_gstin',
        'Invoice Number': 'invoice_no',
        'Bill No': 'invoice_no',
        'Bill Number': 'invoice_no',
        'Taxable Amount': 'taxable_value',
        'Basic Amount': 'taxable_value',
        'IGST Amount': 'igst',
        'CGST Amount': 'cgst',
        'SGST Amount': 'sgst',
    }

    df = df.rename(columns={k: v for k, v in alias_map.items() if k in df.columns})
    df['invoice_no'] = df['invoice_no'].astype(str).str.strip().str.upper()

    numeric_cols = ['taxable_value', 'igst', 'cgst', 'sgst']
    for col in numeric_cols:
        if col in df.columns:
            df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0)

    return df

Step 3: Reconcile and Flag Mismatches

This is the heart of the script. Outer-join both datasets on GSTIN and invoice number, then tag every row with a reconciliation status:

def reconcile(gstr2b: pd.DataFrame, purchase: pd.DataFrame, tolerance: float = 1.0) -> pd.DataFrame:
    """Match on GSTIN + invoice_no. Flag tax differences above tolerance."""
    merged = gstr2b.merge(
        purchase,
        on=['supplier_gstin', 'invoice_no'],
        how='outer',
        suffixes=('_2b', '_book'),
        indicator=True,
    )

    def classify(row):
        if row['_merge'] == 'left_only':
            return 'MISSING_IN_BOOKS'
        if row['_merge'] == 'right_only':
            return 'MISSING_IN_2B'

        diff_igst = abs(row.get('igst_2b', 0) - row.get('igst_book', 0))
        diff_cgst = abs(row.get('cgst_2b', 0) - row.get('cgst_book', 0))
        diff_sgst = abs(row.get('sgst_2b', 0) - row.get('sgst_book', 0))

        if max(diff_igst, diff_cgst, diff_sgst) > tolerance:
            return 'TAX_MISMATCH'

        diff_taxable = abs(
            row.get('taxable_value_2b', 0) - row.get('taxable_value_book', 0)
        )
        if diff_taxable > tolerance:
            return 'VALUE_MISMATCH'

        return 'MATCHED'

    merged['status'] = merged.apply(classify, axis=1)
    return merged

The Orchestrator

A small driver wraps everything into an Excel report the accountant can email to the client:

def run_reconciliation(client_code: str, month: str):
    base = Path(f'/clients/{client_code}/{month}')

    gstr2b = parse_gstr2b(base / 'gstr2b.json')
    purchase = load_purchase_register(base / 'purchase_register.xlsx')
    result = reconcile(gstr2b, purchase)

    summary = result['status'].value_counts().to_dict()
    output_path = base / f'reco_{client_code}_{month}.xlsx'

    with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
        result.to_excel(writer, sheet_name='Full_Reconciliation', index=False)
        for status in ['MISSING_IN_BOOKS', 'MISSING_IN_2B', 'TAX_MISMATCH']:
            subset = result[result['status'] == status]
            subset.to_excel(writer, sheet_name=status, index=False)

    print(f'{client_code} {month}: {summary}')
    return output_path

Drop this into a cron job, point it at a folder structure like /clients/ABC123/2026-03/, and you have a nightly reconciliation run for every client.

The Results — 60 Days Later

After running this pipeline across all 80 clients for two months:

Metric	Before	After
Hours per client / month	7 hrs	15 min
Accountants needed	4	1 (review only)
Monthly labour cost	₹1.4 lakh	₹45,000
Error rate	4-6%	under 0.5%
Client turnaround	3-5 days	Same day

Three of the four junior accountants were not laid off — they were redeployed to higher-value advisory work (ITR-3 filings, tax planning, GST notice responses) at ₹60,000/month. A real upgrade for them, and higher revenue per head for the firm.

Annual savings: approximately ₹18 lakh. Script cost: one weekend of my time, plus ₹0/month in infra (it runs on their existing laptop via a Windows Task Scheduler job).

Gotchas I Hit

A few things that tripped me up the first time and are worth calling out:

Invoice number normalization is the hardest part. Suppliers write "INV-001", "INV/001", "Inv 001", and "invoice 1" for the same bill. On top of the .strip().upper() normalization, I added a difflib.SequenceMatcher fuzzy-match layer with a 0.85 cutoff for leftover unmatched rows. It caught another 3% of matches that would otherwise have been false negatives.
Rounding differences in CGST/SGST. Tally and the GST portal occasionally round in opposite directions. A ₹0.50 tolerance on tax heads removes about 90% of false mismatches.
Credit notes live in a different JSON section. They are under cdnr (credit/debit notes for registered) inside GSTR-2B, not b2b. Parse them separately and flip the sign.
ITC ineligibility flags under Rule 42/43 are not always set correctly by suppliers. The script flags these for human review rather than auto-accepting — human judgment still matters here.

Who Can Use This Today

If you run or work at a CA firm, a tax consultancy, or any business with 20+ vendor invoices per month, this pipeline pays for itself in week one. The full production script is around 200 lines. Swap pandas for polars if you are processing 100,000+ rows per month.

The pattern generalizes. Any reconciliation that compares two structured data sources is a 1-2 day Python project that can eliminate a full-time role:

Bank statements vs. books of accounts
Payroll register vs. PF/ESI challans
Sales register vs. e-way bills
Shopify/Amazon settlement reports vs. GSTR-1

India has roughly 1.4 million CA firms and tax consultants. Most of them are still doing this by hand. The tooling gap is staggering — and the ROI on closing it is immediate.

If you try this and run into trouble, the three places things usually break are: invoice number cleaning, JSON schema drift when the GSTN portal updates, and the pd.read_excel engine choice for large files (use openpyxl for .xlsx, xlrd==1.2.0 for legacy .xls).

Ship it on Monday. Save ₹1 lakh by Friday.

I'm Archit Mittal — I automate chaos for businesses. Follow me for daily automation content.

Automate Your UPI Transaction Tracking: 60 Lines of Python (Free, Works Offline)

Archit Mittal — Tue, 21 Apr 2026 04:54:29 +0000

UPI processes over 16 billion transactions a month in India. If you are like most people, you pay through PhonePe for groceries, GPay for cabs, Paytm for utilities, and your bank app for rent. At every month-end, reconciling spending across all those apps is a nightmare.

I built a Python script that reads exported SMS transaction alerts from my phone, parses them into a clean pandas DataFrame, categorises spending, and prints a monthly breakdown — all in 60 lines, offline, at zero cost.

Here is exactly how it works.

The Problem: Five Apps, Zero Consolidated View

My typical month looks like this:

Groceries: PhonePe (about ₹8,000)
Food delivery: GPay (about ₹3,500)
Cabs: Paytm (about ₹2,200)
Rent: HDFC app (₹28,000)
Bills: Multiple apps (about ₹6,500)

No single app shows everything. Export is locked behind paid "Pro" tiers. Third-party aggregators want permission to read every SMS on my phone. I did not want to hand my financial history to yet another vendor.

SMS is the answer. Every UPI transaction sends a bank-originated SMS. If I can export SMS and parse them locally, I own the data.

Step 1: Export SMS to CSV

Android phones can export SMS via "SMS Backup & Restore" or similar apps. You get an XML or CSV file. Drop it into a folder like ~/upi-data/sms.csv.

The file looks like:

date,address,body
2026-04-15,HDFCBK,"Rs.450.00 debited from A/c XX1234 on 15-APR for UPI/412839-BIGBASKET"
2026-04-15,AXISBK,"INR 120 spent on UPI to OLA-CABS ref 339400 on 15-Apr"

Step 2: The Python Script

import pandas as pd
import re

# UPI transaction regex patterns for major Indian banks
PATTERNS = {
    'hdfc':  r'Rs\.?([\d,]+\.?\d*)\s+debited.*UPI[/-]\w+.*-(\w+)',
    'axis':  r'INR\s+([\d,]+\.?\d*)\s+spent.*UPI\s+to\s+([\w-]+)',
    'sbi':   r'Rs\.?([\d,]+\.?\d*)\s+transferred.*UPI.*to\s+(\w+)',
    'icici': r'Rs\.?([\d,]+\.?\d*)\s+debited.*UPI.*?(\w+)$',
}

CATEGORIES = {
    'BIGBASKET': 'Groceries', 'GROFERS': 'Groceries', 'ZEPTO': 'Groceries',
    'SWIGGY': 'Food', 'ZOMATO': 'Food', 'DOMINOS': 'Food',
    'OLA': 'Transport', 'UBER': 'Transport', 'RAPIDO': 'Transport',
    'NETFLIX': 'Entertainment', 'SPOTIFY': 'Entertainment', 'HOTSTAR': 'Entertainment',
    'AIRTEL': 'Utilities', 'JIO': 'Utilities', 'ELECTRICITY': 'Utilities',
}

def parse_transaction(body, sender):
    bank = (sender or '').lower()[:4]
    for key, pattern in PATTERNS.items():
        if key in bank:
            match = re.search(pattern, body, re.IGNORECASE)
            if match:
                amount = float(match.group(1).replace(',', ''))
                merchant = match.group(len(match.groups())).upper()
                return amount, merchant
    return None, None

def categorize(merchant):
    if not merchant:
        return 'Other'
    for keyword, category in CATEGORIES.items():
        if keyword in merchant.upper():
            return category
    return 'Uncategorized'

def build_report(csv_path):
    df = pd.read_csv(csv_path)
    df['date'] = pd.to_datetime(df['date'])

    rows = []
    for _, row in df.iterrows():
        amount, merchant = parse_transaction(row['body'], row['address'])
        if amount:
            rows.append({
                'date': row['date'],
                'amount': amount,
                'merchant': merchant,
                'category': categorize(merchant)
            })

    report = pd.DataFrame(rows)
    monthly = report.groupby([
        report['date'].dt.to_period('M'),
        'category'
    ])['amount'].sum().unstack(fill_value=0)

    print("\n=== Monthly UPI Spending Report ===")
    print(monthly.round(0).to_string())
    print(f"\nTotal transactions: {len(report)}")
    print(f"Total spent: Rs.{report['amount'].sum():,.0f}")
    return report

if __name__ == "__main__":
    report = build_report('sms.csv')
    report.to_csv('upi_report.csv', index=False)

Step 3: Run It

pip install pandas
python upi_tracker.py

Sample output against my March–April 2026 data:

=== Monthly UPI Spending Report ===
category         Food  Groceries  Transport  Utilities  Uncategorized
date
2026-03          3240       8450       2180       1890           5420
2026-04          2890       7920       1670       1890           4100

Total transactions: 84
Total spent: Rs.42,760

I went from "no idea where the money goes" to a clean monthly breakdown in under 30 seconds.

Why This Beats Third-Party Apps

Data stays on your machine. No SMS permissions granted to random apps. No servers uploading your transaction history.

Fully customisable. Do not like my categories? Edit the CATEGORIES dict. Want a chart? Three lines of matplotlib. Want to track only food spend? One filter.

Zero cost. Commercial expense trackers charge ₹200–500/month. This is free forever.

Scales well. I parsed three years of SMS (2,400+ transactions) in under four seconds on a 2019 MacBook Air.

Extensions I Layered On

After the base 60 lines, I added:

Matplotlib chart — saves a PNG bar chart of category-wise monthly spend.
SQLite cache — stores parsed transactions so re-runs are instant.
Telegram bot — sends a weekly summary to my phone via a free bot token.
Business-expense tag — marks GST-relevant transactions for year-end filing.

Each extension added about 15 lines of Python. The whole script is now ~150 lines and has replaced a ₹499/month expense-tracking app I was paying for.

Honest Limitations

SMS format changes. When a bank updates its template, the regex breaks. I add new patterns every 6–8 months.

Not every transaction triggers SMS. Some UPI auto-pay and recurring flows can be silent. I reconcile quarterly against the bank statement.

Bank-specific regex. I have patterns for HDFC, Axis, SBI, and ICICI. Other banks need 10 minutes of custom regex each.

What You Can Build Next

A full personal-finance dashboard (Streamlit is perfect).
ML-based merchant classification with scikit-learn on a few hundred labelled rows.
A rules engine — "alert me if food spend crosses ₹5,000 in a week".
Cross-reference with Zerodha or Upstox P&L for combined wealth tracking.

The core idea — your data, parsed by your own code, on your own machine — applies far beyond UPI. Start with 60 lines. Add what you actually need.

I'm Archit Mittal — I automate chaos for businesses. Follow me for daily automation content.

I Built a Claude Code Sub-Agent That Processes 2,000 Invoices a Day — Here's the Exact Setup

Archit Mittal — Mon, 20 Apr 2026 04:43:56 +0000

A small Gurgaon-based CA firm came to me last month with a recurring nightmare: 2,000+ vendor invoices landing in a shared Drive folder every day. Three junior accountants spending six hours each manually keying them into Tally. That's ~₹1.8L/month in salary cost for pure data entry.

They asked if an AI agent could handle it.

Short answer: yes. And not just "handle" — Claude Code sub-agents turned out to be the cleanest way I've seen to structure this kind of multi-step workflow. Here's exactly how I built it, plus the code you can fork today.

Why Claude Code sub-agents

If you haven't touched sub-agents yet: they're specialized Claude instances you can spawn from a main agent to handle a scoped task. Each has its own context window, its own system prompt, and its own toolset. You define them as markdown files in .claude/agents/ — no framework, no DAG, no visual editor to learn.

The killer feature for this workflow is context isolation. A 50-page invoice PDF doesn't bloat the main agent's context. Extraction happens in a sub-agent, and only the structured JSON result comes back.

Three other reasons I reached for sub-agents here:

Per-agent model selection. Validation runs on Haiku for pennies. Extraction runs on Sonnet for accuracy.
Tight tool scopes. The writer agent can touch the Tally bridge; the extractor cannot. Blast radius stays small.
Independent prompt versioning. Each .md file lives in git. When accuracy dips, git diff tells you why.

The architecture

For this case I used three sub-agents orchestrated by a tiny Python loop:

pdf-extractor — pulls vendor name, GSTIN, line items, totals
tally-validator — checks GST math, flags anomalies, matches against vendor master
tally-writer — pushes validated records into Tally via its ODBC bridge

Main orchestrator handles retries, quarantining, and Postgres logging.

Sub-agent #1: the PDF extractor

File: .claude/agents/pdf-extractor.md

---
name: pdf-extractor
description: "Extract structured invoice data from Indian GST invoice PDFs. Returns JSON with vendor, GSTIN, line items, and totals."
tools: Read, Bash
model: sonnet
---

You extract structured data from Indian GST invoices.

For each PDF the user names:
1. Read the PDF contents.
2. Identify: vendor name, GSTIN, invoice number, invoice date, line items (with HSN codes), CGST, SGST, IGST, total.
3. Return a single JSON object. No prose. No markdown fences.

If GSTIN is missing or fails the 15-character format check, return {"error": "invalid_gstin"}.

That's the whole prompt — under 15 lines. The magic is in the description field: the main agent uses it to decide when to delegate.

Sub-agent #2: the validator

---
name: tally-validator
description: Validate extracted invoice JSON against GST rules and vendor master. Returns pass/fail with reasons.
tools: Read, Bash
model: haiku
---

Input: invoice JSON from the pdf-extractor agent.

Check:
- GSTIN passes the Modulus-36 checksum.
- CGST + SGST equals IGST-equivalent for intra-state invoices; IGST-only for inter-state.
- Line item totals sum to invoice total within a ±₹1 tolerance.
- Vendor GSTIN exists in /data/vendor_master.csv.

Return {"valid": true} or {"valid": false, "reasons": [...]}.

I put this one on Haiku. It's deterministic validation — no reasoning required, just arithmetic and lookups. Cost per invoice dropped from ₹0.40 to ₹0.06 when I moved validation off Sonnet.

Sub-agent #3: the writer

---
name: tally-writer
description: Post a validated invoice JSON into Tally via the ODBC bridge. Returns the Tally voucher number on success.
tools: Bash
model: haiku
---

Input: validated invoice JSON.

Call /usr/local/bin/tally-push with the JSON on stdin. If exit code 0, return {"posted": true, "voucher": <stdout>}. Else return {"posted": false, "error": <stderr>}.

Do not retry. Do not modify the JSON. Do not summarise.

Boring on purpose. This agent should feel like a shell command.

The Python orchestrator

import subprocess
import json
from pathlib import Path

INBOX = Path("/srv/invoices/inbox")
DONE = Path("/srv/invoices/processed")
FAILED = Path("/srv/invoices/failed")

def run_claude(prompt: str) -> dict:
    result = subprocess.run(
        ["claude", "-p", prompt, "--output-format", "json"],
        capture_output=True, text=True, timeout=120,
    )
    if result.returncode != 0:
        raise RuntimeError(result.stderr)
    return json.loads(json.loads(result.stdout)["result"])

def process(pdf_path: Path) -> dict:
    prompt = (
        f"Use the pdf-extractor sub-agent on {pdf_path}, "
        f"then pass the result to tally-validator. "
        f"If valid, use tally-writer to post it. "
        f"Return the final status JSON only."
    )
    return run_claude(prompt)

for pdf in INBOX.glob("*.pdf"):
    try:
        result = process(pdf)
        dest = DONE if result.get("posted") else FAILED
        pdf.rename(dest / pdf.name)
    except Exception as e:
        (FAILED / f"{pdf.stem}.error").write_text(str(e))
        pdf.rename(FAILED / pdf.name)

Drop it on cron every 15 minutes:

*/15 * * * * cd /srv/invoices && python3 run.py >> /var/log/invoices.log 2>&1

Done.

Numbers from week one

2,047 invoices/day average throughput
94.3% auto-posted without human touch
5.7% kicked to an exception queue (mostly scanned PDFs with poor OCR)
Compute cost: ~₹840/day across all three sub-agents
Replaced roughly ₹1.8L/month of manual effort
Payback period: about 2 weeks

The 5.7% exception rate is the number I'd watch. Two weeks in, a quick tweak to the extractor's prompt around handwritten delivery challans dropped it to 3.1%.

Three things I'd tell past-me

1. Version your sub-agent prompts from day one. Commit the .claude/agents/ folder to the project repo. When accuracy dips after a model update, git diff tells you which prompt tweak broke it.

2. Log the JSON, not the prose. My first version captured the full agent transcript. Useless for debugging at 2 AM. Now I log only the structured JSON each sub-agent returns, plus the prompt and timestamp.

3. Don't over-orchestrate. My initial design had five sub-agents. Three was plenty. If two stages always run together and never branch, they should be one agent. Sub-agents are a scoping tool, not a UML diagram.

Try it this week

If you want to kick the tires on sub-agents, here's a 90-second starter:

mkdir -p .claude/agents
cat > .claude/agents/summarizer.md <<'EOF'
---
name: summarizer
description: Summarize a file in 3 bullet points. Use when the user asks for a quick file summary.
tools: Read
---
Read the file the user mentions. Return exactly 3 bullets. No preamble.
EOF

claude -p "Use the summarizer sub-agent on README.md"

That's it. From there, copy the invoice pattern above — one agent per scoped task, tight tool lists, clear descriptions.

The real unlock isn't Claude being clever. It's the boring discipline of splitting work into agents small enough that each one can be reasoned about in isolation. Everything else is plumbing.

I'm Archit Mittal — I automate chaos for businesses. Follow me for daily automation content.

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