# Ambit

- **Event:** [The Persistent Context Sprint Hackathon | Live at MongoDB .Local Build Fest](https://cerebralvalley.ai/e/persistent-context-sprint-hackathon)
- **When:** Thu, Aug 13 at 12:30 – 8:00 PM (PDT)
- **Where:** Pier 48, San Francisco, CA
- **Team:** [pranav achar](https://cerebralvalley.ai/u/prnav), [Shanay Gaitonde](https://cerebralvalley.ai/u/shanaygaitonde)
- **GitHub:** https://github.com/PranavAchar01/ambit
- **Demo video:** https://drive.google.com/drive/folders/1FKCJvWrr_MQcfZxHpNE-7_Hnis50dzs7?usp=sharing
- **Gallery:** https://cerebralvalley.ai/e/persistent-context-sprint-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/persistent-context-sprint-hackathon/hackathon/gallery/61

A hardware startup builds forty units of a prototype. Optical inspection machines start
around fifty thousand dollars and have to be programmed per board, which never pays back
at that volume, and there is no defect data to train on because the board did not exist
last month. So small teams inspect by eye, or skip it. Quality control is one of the
reasons hardware iterates slower than software.

Ambit makes inspection cost fourteen photos and three seconds. You photograph known-good units and it fits a specialist for that exact part from normal examples only, with zero
defect labels. Show it any part afterwards and it embeds the frame, vector searches a
registry of specialists, and loads the one trained on the closest imagery. When nothing
in the registry has competence over what it is looking at, it refuses instead of passing
an uninspected board.

That refusal is the hard part. Five specialists are registered and a sixth board is
withheld. Top-1 routing is 125/125. Per-model coverage gates refuse 25/25 out-of-registry
frames, while every global threshold we tested refuses 0/25, because the withheld board
scores 0.9399, above all of them. It genuinely is a PCB and the registry is full of PCBs.
Only a per-model gate declines it. The cost, stated plainly, is that 6 of 125 in-registry
frames are refused too.

MongoDB Atlas is the system, not a store beside it. Routing is $vectorSearch over
model_router_idx, one 512-d OpenCLIP centroid per model. Weights are PatchCore coresets in GridFS at 7.55 MB each. PatchCore has no gradient-trained parameters: training stores a
coreset of patch features and inference is nearest-neighbour against it, so a model is
remembered examples of normal. Vector search picks which memory applies, nearest-neighbour searches inside it. Findings carry their own embedding indexed for episodic recall, LangGraph checkpoints live in the same cluster so a kill -9 mid-training resumes instead of relearning, and fleet health runs as aggregation pipelines where the data already is.

LangGraph runs the agent graph with node-level checkpointing and cold-start as a branch.
Fireworks runs Qwen3-VL to describe the difference between the defect crop and the same
region of the golden reference. OpenRouter does structured-output adjudication inside the
0.04 ambiguous band with model fallbacks. ElevenLabs is a voice analyst over five
MongoDB-backed tools including explain_refusal and recall_similar, and every claim it makes appears as a tool call first.

The demo is live. A phone streams a viewfinder to the projected view, holds still, and
uploads one full-resolution frame. An Arduino board is cold-started on site from fourteen
photos, comes back nominal untouched, and is flagged with a localized heatmap and a written description once a pin is bent.

Built during the event: the Arduino specialist and local ingest path, Fireworks VLM defect
narration, OpenRouter adjudication, ElevenLabs voice, project grouping in the registry, the
capture and analysis interface, and the trends view. The README separates this from
pre-existing work in full.

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