build-small-hackathon/packetcourt-field-notes

Field Notes: Building PacketCourt

The packet takes the stand

PacketCourt began with a narrow household problem: a food packet's front is designed to persuade, while the evidence needed to interpret that persuasion is scattered across the back. A shopper should not need to understand serving bases, ingredient ordering, date arithmetic, or regulatory language while standing in a grocery aisle.

The first idea was a nutrition scanner. That was too broad and too easy to turn into an unexplained health score. PacketCourt instead asks one auditable question:

Does the evidence printed on this packet support the impression created by its front?

Small models as witnesses, not judges

The system deliberately separates three responsibilities:

1. OpenBMB MiniCPM-V-4.6 transcribes visible front and back label evidence.
2. A fine-tuned 4.4M-parameter PacketCourt router selects the evidence tools required by each detected claim.
3. Deterministic code performs calculations and produces final verdicts.

The models can read and route an investigation. They cannot silently invent a nutrition value or override the evidence standard.

NVIDIA Nemotron-Mini-4B-Instruct performs a second, independent review after the investigation plan completes. It can identify the highest-priority missing evidence, but it cannot alter PacketCourt's deterministic verdict.

What the investigation agent does

Each packet creates a claim-dependent investigation plan. A NO ADDED SUGAR claim sends the investigation toward ingredients. HIGH PROTEIN requires a nutrition panel and its measurement basis. FSSAI APPROVED requires licensing evidence and a warning that registration is not a health endorsement.

The agent stops in one of two explicit states:

• all evidence tools required by the detected claims completed; or
• required evidence is missing, so the audit returns a concrete request rather than guessing.

Every plan, tool decision, evidence extraction, calculation, verdict, and limitation is exported as a trace.

Real packets broke the two-photo assumption

A real packet quickly showed that front-and-back capture was too neat an abstraction. Claims, dates, directions, ingredients, and nutrition tables can wrap across several panels. PacketCourt now accepts additive multi-angle phone capture, labels each transcription by photo number, merges unique evidence, and skips exact duplicate transcriptions.

The same test exposed table-style OCR such as Protein (g) 12 and Sodium | mg | 410. The deterministic parser was expanded to recover those rows while explicitly explaining when OCR found a nutrition basis and packet size but omitted the nutrient quantities needed for arithmetic.

A failed first fine-tune

The first evidence-router training run reached only 0.40 held-out accuracy. The dataset was too small and its random split did not preserve every routing class. That model was published privately but was not enabled in the product.

The corrected run balanced claim variants across five routing classes and used a stratified held-out split. PacketCourt only enables the router after its measured result is recorded in the model card and its suggestions remain bounded by deterministic policy fallbacks.

A failed Nemotron deployment

The first NVIDIA reviewer used NVIDIA-Nemotron-3-Nano-4B-BF16. A real ZeroGPU probe failed because the hybrid Mamba runtime required a specialized CUDA build unavailable in the standard Gradio image. Rather than claim a model that did not run, PacketCourt switched to Nemotron-Mini-4B-Instruct. The replacement completed a real ZeroGPU evidence-gap review before it was connected to the product.

Persuasion Gap

Claim verification alone was not enough. A HIGH PROTEIN claim can be technically supportable while a full packet also contains substantial sugar or sodium. PacketCourt therefore calculates a Persuasion Gap: material back-label context that competes with the impression emphasized on the front.

This is not a health score. The output cites the exact calculation and leaves the decision with the user.

Community learning without silent self-training

Users can confirm an audit or submit an evidence-backed correction through the Community Review Agent. Each review preserves the original evidence, investigation path, and Nemotron review in a public queue.

Feedback is not automatically trusted. New records remain in pending_human_review with training_eligible: false until the supplied packet evidence is checked. Approved cases can enter a versioned router-training release and must pass the golden-case suite before deployment.

Current evidence

• 20 unit and end-to-end integration tests pass.
• 35/35 golden-case checks pass across 10 packet cases.
• 10 transparent investigation traces are exported.
• Multi-angle packet capture and exact-duplicate removal run publicly.
• A public correction-driven learning queue contains real review records.
• The vision model has 1.30B parameters.
• The fine-tuned evidence router has approximately 4.4M parameters.
• The independent NVIDIA Nemotron reviewer has approximately 4B parameters.
• The complete product interface is responsive and built on Gradio.

What PacketCourt refuses to claim

PacketCourt does not declare food healthy, safe, illegal, or fraudulent. It does not treat OCR as ground truth. It does not use an LLM to perform arithmetic that deterministic code can perform exactly. When supplied evidence is insufficient, the correct result is CANNOT VERIFY.

That refusal is not a missing feature. It is the product's standard of proof.