precisionBOM
AI-powered BOM optimization with live DigiKey inventory, price breaks, alternates, and export paths that move a hardware team from spreadsheet to sourcing decision.
- Compresses BOM sourcing from a spreadsheet-heavy workflow into a live, data-backed sourcing pass
- Uses real DigiKey catalog data for inventory, lead-time, and quantity-price visibility
- Adds AI-assisted alternates and optimization around engineering fit, availability, and order cost
precisionBOM is the most clearly commercial project in this group, but it still carries the same systems instinct as the others: turn a messy technical workflow into typed roles, structured flows, and inspectable decisions.
The problem is electronic component sourcing. A hardware team needs to reconcile manufacturer part numbers, supplier availability, lead times, price breaks, alternates, order quantities, and export formats, usually while a deadline is already moving. The public site frames the product around a simple loop: upload a BOM, match parts against DigiKey's live catalog, review stock and alternatives, then export or order.
The current architecture pairs a Next.js product surface with a Python multi-agent service. Supplier data starts with DigiKey, where direct API integration gives the app live inventory counts, quantity-tier pricing, and current lead-time context. Specialist reasoning can then work over the same shared BOM state instead of asking the user to manually stitch together spreadsheet rows, vendor pages, and email notes.
The project also has a stranger competition footnote: we won first place, but the promised prize money never materialized. After the win, the sponsors stopped responding instead of completing the payout. So the public artifact is both a product prototype and a record of an awkwardly common hackathon failure mode: the builders ship, the judges award, and the incentive structure evaporates after the cameras are off.
A short celebration clip from the precisionBOM build.
What makes the project distinctive is the workflow framing. This is not just "AI for procurement." It is a claim that hardware sourcing can become an agentic workflow where engineering fit, sourcing risk, and cost optimization are evaluated in parallel, with the final recommendation returning as a concrete, exportable BOM decision rather than a loose chat answer.
Site: precisionbom.com
Repo: precision-bom/precisionBOM