Build Small Hackathon - Quillwright

AaryaP — Mon, 15 Jun 2026 22:59:04 +0000

How Quillwright turns a photo and a voice note into a tradesperson's estimate, with an orchestra of small models, on your own machine, and not a single number invented by an LLM.

The job nobody wants

Every tradesperson does the same unpaid hour after the real work is done: writing up the estimate. Parts, quantities, labor, a defensible total. Quillwright is an on-device, human-supervised agent that does that draft from a field capture (a job photo plus a spoken note) and hands back an itemized, editable estimate.

The constraints we set ourselves were the interesting part: small models (≤32B), no third-party AI APIs, and a hard rule that no customer-facing number ever comes from a language model. Those three constraints shaped every decision below.

An orchestra, not a soloist

There's no single model doing the work. Each role in the pipeline resolves to a small, purpose-fit model:

Perception: MiniCPM-V (OpenBMB) reads the job photo into observations ("RUN CAPACITOR", a nameplate model number).
Agent Brain: NVIDIA Nemotron-3-Nano drives a narrow tool-calling loop: which items, what quantities, when it's done.
Audio: Cohere Transcribe turns the voice note into text on-device.
Multilingual: Cohere Aya translates the customer-facing copy (Spanish, French, Mandarin), descriptions only, never the numbers.
Embedding: a small embedder powers semantic recall of similar past jobs.

The brain's tool surface is deliberately tiny: essentially add a priced item and finish. That narrowness is why a 4B model is reliable here: it does routing and judgment, not arithmetic.

Facts-from-Tools: the rule that runs through everything

The correctness rule is simple to state and ruthless to enforce: any number that reaches the customer (price, quantity, tax, total) comes from a tool (a catalog lookup, a deterministic compute) or from a human edit. Never from the model's free generation.

It holds in the obvious places (the brain calls lookup_price, not "I think this costs $40") and the non-obvious ones:

Edits re-run through a server-authoritative recalc. The browser never computes its own total.
Translation changes words, not digits.
Document Capture (reading a supplier quote) produces Proposed Line Items: the document is the source, but a price only becomes customer-facing once a human confirms it.
The refinement chat keeps a sanitized history: when you reopen an estimate and keep editing, the model sees what you asked ("make it 2 hours") but takes the numbers from the current line items, so a stale dollar figure can never leak back in. Even the conversation's own compaction is done in code, not by asking a model to summarize.

The eval story (the part I'd tell another builder)

Here's the moment that changed how we built this. We ran the agent by hand on a handful of jobs and it looked perfect. Then we wrote an eval set and scored it.

Item F1: 0.367.

Manual testing had been lying to us: we'd unconsciously fed it the cases it handled. The eval set didn't. Two fixes, both measured:

Fuzzy catalog lookup: "refrigerant" should find refrigerant_r410a. F1 jumped to 0.880.
Prompt tuning the brain's tool-calling, to 0.967, with quantity accuracy going from 0.40 to 1.00.

The lesson isn't "we got a good number." It's that the good number only existed because we were willing to be told a bad one first.

Memory that gets smarter, measured the same way

Quillwright recalls similar past jobs to inform a new estimate. The first version used keyword matching. We measured recall@1 = 0.750. Swapping in a small embedder for a semantic re-rank moved it to 0.875, with one honest remaining miss we left in, because a benchmark with no failures is a benchmark you don't trust.

Fine-tuning a small vision model on receipts, and on the real domain

The 🎯 artifact is a MiniCPM-V LoRA fine-tune. On the public CORD receipt benchmark, the tune lifted item F1 from 0.588 → 0.681 (+0.09). But CORD is receipts, not trade invoices, so we also generated a grounded-synthetic set of trade invoices (built from a real 381-entry trade catalog) and fine-tuned on that. In-distribution, the tune went from 0.703 → 0.933 (+0.23), with price accuracy hitting 1.00.

The +0.23 is the honest headline: a small model, fine-tuned on the actual domain, closes most of the gap to a clean read. The +0.09 on CORD is the conservative one: it's a harder, out-of-domain benchmark, and we report it anyway.

Artifacts

Both LoRA adapters are on the Hub, and every number above is reproducible from the eval scripts in the repo:

🎯 Aarya2004/minicpmv-trade-lora: the in-domain trade-invoice tune (0.703 → 0.933).
Aarya2004/minicpmv-cord-lora: the conservative CORD baseline (0.588 → 0.681).

Metric	Before	After
Agent Brain item F1	0.367	0.967
Episodic recall@1	0.750	0.875
MiniCPM-V item F1 (trade, in-domain)	0.703	0.933
MiniCPM-V item F1 (CORD, OOD)	0.588	0.681

"On your own machine", and the honesty around it

The hero claim is no cloud. The honest version of that claim has two parts:

The Private Stack is open small models with no third-party AI APIs. Locally, those models genuinely run on the dev machine via Ollama / llama.cpp, and we filmed an Airplane-Mode Proof: Wi-Fi off, a real forge completing.
The hosted demo Space is wired live to Modal GPUs, the Best Stack: a Nemotron-3-Nano 30B brain, Nemotron-Omni for vision and audio, Aya-Expanse for multilingual. It's the same agent loop and the same Facts-from-Tools guarantees as the local run, just with more headroom; the apps scale to zero when idle, so the Space can fall back to a lightweight CPU mode (and says so on the page) when the models aren't wired. The local Private Stack and the hosted Best Stack are the same family at two tiers: flip one env var and the brain moves from a 4B on a laptop to a 30B on a GPU without touching the agent code.

Same agent, same tools, same Facts-from-Tools guarantee. Only the models behind each role change:

Role	🔒 Private Stack (local)	⚡ Best Stack (Modal)
Brain	Nemotron-3-Nano 4B (NVIDIA)	Nemotron-3-Nano 30B (NVIDIA)
Perception	MiniCPM-V (OpenBMB)	Nemotron-Omni 30B (NVIDIA)
Audio	Cohere Transcribe (on-device)	Nemotron-Omni 30B (NVIDIA)
Multilingual	Aya (Cohere)	Aya-Expanse 8B (Cohere)
Embedding	on-device (sentence-transformers)	same on-device path
Extraction	no local path	Parse extractor (fine-tuned)
Runs offline?	✅ Yes, Airplane-Mode Proof	❌ No, hosted GPU endpoints
Cost / GPU	$0, your hardware	scales to zero when idle

We hold the same line everywhere a feature could over-claim. The "Finalize & Send" feature really texts or emails the estimate on the local path with your own provider creds; on the public Space it drafts only and tells you nothing was transmitted. Same for the phone call and the phone-capture QR: real on the tunneled local machine, honestly framed.

Three ways in

Once the core was solid, the capture surface grew. Each path lands in the same pipeline and the same Facts-from-Tools guarantees:

The Workspace: type/paste a note, add a photo, watch the Digital Apprentice stream.
Call a phone number: describe the job out loud; it transcribes the call, forges a draft estimate, reads the total back, and texts you the PDF. A human approves later.
Scan a QR: capture a photo and voice note on your phone; the desktop forges it live on screen.

What I'd carry to the next project

Write the eval before you trust the demo. 0.37 was the most useful number in the whole build.
Keep the model's job small. The brain is reliable because it never touches arithmetic.
Make the honesty structural, not aspirational. "The model never emits a number" is a code path, not a promise, and it's the same code path on every capture surface.

Quillwright: tell it about the job; it drafts the estimate.

DEV Community: AaryaP