GovClaw
Every year, billions in funding you qualify for goes unclaimed. Not because you don't qualify, but because chasing it is a full-time job nobody has. GovClaw does if for you.
GovClaw finds the grants where you're genuinely the strongest fit and drafts RFP-ready proposals tuned to what each funder actually rewards.
The situation
Billions in grant and contract funding go unclaimed every year — and not because the teams who need it don't qualify. There's no single source of truth telling a founder or research group what they're eligible for, and discovery is only the start. Every grant carries its own RFP , its own rules, its own custom application. Mapping eligibility and applying across all of them is a full-time job nobody on a lean team actually has. So capital that was theirs to claim goes to whoever had the bandwidth to chase it.
Why it was hard
Matching a project to a list of grants is easy. Keyword overlap produces a long, mostly wrong list in seconds. The hard part is matching accurate and reliable enough to stake weeks of effort on: correlating the specific attributes of a project against what a given funder genuinely funds, and not surfacing a grant it will never win. Harder still is the writing. A proposal that reads well is not the same as one tuned to what a particular funder rewards. And getting there means learning from precedent, which applications won, which lost, and why, then applying those lessons without inventing claims the project can't support. On a grant application an overstated qualification isn't a small error. It can disqualify the whole submission.
The approach
GovClaw is built around a single objective– maximizing the probability of winning. It works on two fronts. First it understands the project and the applicant organization end to end to build a profile. That profile is then run through a matching process that employs complex, cross-domain reasoning based on the Sanscritic reasoning engine. A correlation is built against grants available across government and private databases to pinpoint the grants where the fit is genuinely strongest rather than merely plausible.
Second, it drafts. Drawing on a lessons learned corpus of winning and losing applications, the reasoning engine writes custom, RFP-ready proposals tuned to each funder's priorities and grounded in what the project can actually substantiate.
What happened
GovClaw is deployed and running in live trials with design partners. For a team the change is stark: discovery shifts from an unstaffed, full-time chore to a ranked shortlist of grants they're a real contender for, each already paired with a draft proposal tuned to that funder. Because both the matching and the drafting are grounded in the project's actual profile and in precedent, the output points toward winnable funding instead of a pile of long shots.
The honest boundary: GovClaw drafts submission-ready proposals, but a person owns the final review and the decision to submit, because the cost of an overstated claim is high, and because the lessons-learned corpus sharpens each cycle with human judgment in the loop.
What this means for you
If there's capital you qualify for but have never had the hours to pursue grants, contracts, non-dilutive funding the barrier was never ambition. It's the full-time work of finding the right opportunities and applying well to each of them. That's the work GovClaw compresses, and it's what becomes possible when deep funding expertise and agent infrastructure are built together.