What We Learned Building an AI Agent for Government Teaming

TL;DR: Before building contrl, our team built an AI agent for government contract teaming. The lessons about domain specificity, trust, and the gap between AI output and real deliverables directly shaped contrl's design.

The pre-RFP problem

Government contracts are won before the RFP drops. The best contractors start building relationships, identifying teaming partners, and positioning their capabilities months ahead of a solicitation. This "capture" phase is where wins are decided.

We built an AI agent to help small businesses find teaming partners, identify upcoming opportunities, and position for contracts they could not win alone. The technology worked. The market taught us something more important.

What the market taught us

Contractors loved the teaming intelligence. But their immediate, burning problem was not finding opportunities or partners. It was responding to the opportunities they already had. They would say: "This is great, but I have an RFP due Friday and I am still building slides at midnight."

The proposal assembly problem was so acute that it overshadowed everything upstream. Until contractors could respond efficiently to current opportunities, they had no bandwidth to think about future ones.

How this became contrl

We pivoted our entire focus to the proposal problem. All the domain expertise we built understanding procurement workflows, RFP structures, and contractor pain points went into designing contrl.

contrl exists because we spent years in the government contracting ecosystem and listened to what contractors actually needed first. Not capture. Not teaming. A finished proposal in their template, mapped to the RFP, ready for review. Everything else can wait until that is solved.

The teaming experience gave us something no amount of market research could: deep empathy for the contractor workflow. That empathy is in every feature of contrl.