GovScout: AI Search and Proposal Drafting for GovCon

GovScout is a Cartisien product. I designed it and I build it.

Government contracting has a discovery problem and a trust problem. Opportunities are scattered across federal, state, and local systems that do not talk to each other, and the documents describing them are long, inconsistent, and dense with requirements that decide whether a bid is worth making at all. Contractors lose most of their time before they ever start writing.

GovScout puts an AI layer over live procurement data: search, bid alerts, and proposal drafting. That immediately raises the questions every AI product has to answer. Where did this result come from. Why is this opportunity being recommended to me. Can I trust drafted proposal text enough to put my company's name on it and submit it to a federal agency.

Those are interface problems rather than model problems, and they shaped most of the design work.

Search and discovery over messy public data. Solicitations arrive from many sources in inconsistent shapes. The interface has to make a normalized result feel trustworthy while staying honest about what came from where, including when a source is incomplete or out of date.

Provenance in generated output. Drafted proposal content stays traceable back to the solicitation language it came from. A contractor reviewing AI-drafted text needs to see the source rather than take it on faith.

Bid and no-bid decision support. Fit and readiness are surfaced as reasoning a person can disagree with, not as an opaque score. The user stays the decision maker, and the system's job is to show its work.

Notice-aware next steps. Different notice types call for different actions. The interface adapts to the state of an opportunity instead of offering one generic call to action regardless of context.

This is the work my AI services describe, applied to my own product: making model behavior legible, keeping provenance visible, and designing carefully for the moments where the system is uncertain or wrong. Running it rather than handing it off is where a lot of what I know about AI product UX came from, including the parts that only show up after real users arrive.