SlothClerk: a sloth that reads your contracts offline with Gemma

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

I avoid reading contracts. Rent agreements, NDAs and service terms are long, and the one clause that hurts is always buried in the middle.

So I built SlothClerk, a lazy person's contract reader that runs entirely on my laptop. I upload a PDF or DOCX, and about a minute later I get:

• a plain-language summary
• key dates, money amounts and obligations
• red flags (jury waivers, auto-renewals, one-sided repossession, penalties), each with a one-sentence explanation of what it could cost me
• an action checklist
• a chat box for follow-up questions

Every item links back to the sentence it came from. I click [chunk_1] ✓ and see the exact source text highlighted, so I never have to just trust the model.

Demo

Watch the demo, recorded with Wi-Fi off: SlothClerk demo video

In the video: upload a contract, get results in about a minute, click a citation to see the source sentence, and ask a follow-up question in the chat.

Code

SidakSethi-Singh / slothclerk

SlothClerk 🦥📄

100% Offline, Privacy-First Document Assistant

SlothClerk processes sensitive documents (contracts, NDAs, leases, MSAs) locally with zero external cloud API requests, telemetry disabled, and strictly grounded citations verified against the source text.

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🌟 Key Features

• 100% Offline & Private: Powered by local Ollama models (gemma3:4b and nomic-embed-text) with ChromaDB telemetry completely disabled.
• Verifiable Citations: Every claim, date, obligation, and red flag is linked to a source chunk and verified verbatim against the document text.
• Deterministic Clause Scanner: Pre-scans high-signal legal clauses (e.g. repossession, waivers of jury trial, automatic renewal, liquidated damages, penalties) before LLM synthesis (~69,000+ chars/sec).
• 📊 Cloud vs Local Benchmark Suite: Quantitative performance and cost comparison against OpenAI GPT-4o, Claude 3.5 Sonnet, and GPT-4o-mini (python benchmark.py, cli.py --benchmark, or GET /api/benchmark).
• 🎙️ ElevenLabs Voice Executive Briefing: Spoken audio briefings of contract summaries and critical red…

View on GitHub

How I Built It

• Model: Gemma (gemma3:4b) running locally through Ollama
• Search and chat: nomic-embed-text embeddings with ChromaDB (telemetry off) for the RAG chat
• Backend: FastAPI, with one analysis pipeline shared by the CLI and the web app
• Frontend: a single-page UI with progress stages, result cards and a citation popover
• Extraction: pypdf and python-docx, with sentence-aware chunking

The pipeline works in two layers, which I only arrived at after things broke:

1. Rules find, the model explains. A regex scanner finds high-signal clauses ("jury", "waive", "automatically renew", "late charge", "without refund") and uses the sentence itself as the quote. Gemma then writes the title, severity and a one-sentence consequence.
2. Nothing ships unchecked. Quotes are matched against the source text, deadlines must appear in the cited sentence, and duplicates and headings are filtered out.

Why Does Open Innovation Matter?

Contracts are exactly the documents I don't want to upload to someone else's server.

• Privacy: the document never leaves my machine. No account, no API key, no retention policy to read.
• Offline: the demo video is recorded with Wi-Fi off.
• Cost: $0 per document. Running it on a hundred leases costs the same as running it on one.
• Control: I could set temperature to 0, constrain the output with a JSON schema, inspect every prompt, and swap models by editing one line in .env. With a closed API I would have been guessing why an answer changed.

To be honest, a large closed model would probably read these contracts better. What open weights gave me was a tool I can trust with private documents, and the freedom to build verification around the model instead of hoping it behaves.

The Lazy Payoff

• About 35 to 70 seconds per document on my laptop, with a 4B model
• NDA test: 12/12 citations found verbatim in the source text
• A first pass over a 7-page lease in about a minute, instead of reading it line by line
• $0 to run

What Went Wrong (the honest part)

• "Verified" didn't mean "correct." My first version checked that each quote existed in the document, and 19/19 passed, yet the model still invented deadlines ("within 3 days", "Finance Department") that the contract never mentioned. I added a grounding check: a deadline must appear in the cited sentence, or it is dropped.
• A 4B model missed clauses on long documents. On a 7-page lease it flagged one issue and skipped the jury waiver and the late fee. That is why I added the rule-based scanner.
• The model hallucinated a renewal from the phrase "terminates promptly" and called it "implied." I now reject flags that contradict their own quote.
• My web app ran an older pipeline than my CLI, so browser results differed from terminal results. I merged them and added a test that both paths return identical counts.
• My C: drive had 95 MB free while I installed Ollama, and my laptop shut off mid-build. I moved the models to another drive and now commit often.

Limits

• English only. No scanned images (OCR was cut for time).
• The sample contracts are synthetic. I have not tested real contracts.
• A 4B model's wording is sometimes awkward, and some flags overlap.
• Not legal advice.

Prize Categories

• Best Use of Gemma: Gemma 3 4B is the core of the project, running locally.