Case study · Founder / Developer
GovScout
Built to do what a busy contracts specialist doesn't have time for: read every new SAM.gov solicitation in a given commodity area and flag the ones actually worth a phone call. It scores solicitation copy for pricing-availability signal and extracts the National Stock Numbers and part quantities buried in the text, so a human only has to look at the solicitations the scorer says are worth looking at.
100/100pricing-availability signal scoring algorithm across 4 keyword families
NSN & P/Nautomated regex extraction of National Stock Numbers & part quantities
0 Depszero third-party dependencies beyond standard library & pytest suite
A federal procurement monitoring engine modeled on defense logistics (DLA/DIBBS) RFQ tracking. GovScout ingests live solicitations from the SAM.gov Opportunities API v2, filters them by FSC/PSC codes, detects pricing-availability signals, and extracts structured part data into SQLite.
- Pricing Signal Classifier: Evaluates solicitation copy against weighted keyword families (
online_pricing,historical_pricing,quote_workflow,competitive) to generate a 0–100 pricing feasibility score. - Structured Part Extractor: Automatically parses National Stock Numbers (
NSN \b\d{4}-\d{2}-\d{3}-\d{4}\b), manufacturer part numbers (P/N), and required item quantities from raw text. - Dual Mode Architecture: Operates in Live Mode (REST API client with 0.5s rate-limiting, 429 backoff, and state persistence) and Demo Mode (bundled offline dataset for zero-network testing).
- SQLite Engine & Report Generator: Upserts normalized solicitations with duplicate detection and exports CSV feeds and console digests grouped by signal priority.