Most AI grant tools have never seen a winning grant. They were trained on public data: NIH RePORTER abstracts, SBIR.gov award notices, scraped titles and outcome metadata. Wide data. Virtually zero signal. None of it shows how a winning Specific Aims was framed, where an Approach section overpromised, or which sentence drew a strength comment from a reviewer.

Streamline has leveraged Grant Engine’s proven system of winning, refined over a decade of submissions, to build an AI that doesn’t just generate grant copy. It generates drafts grounded in what reviewers have actually rewarded and penalized. The system trained on over 324,000 reviewer-scored clauses, broken out from a FOIA-cleared corpus of 5,000+ selectively screened SBIR/STTR grants and paired with the summary statement language reviewers actually wrote in response. Outcomes run 2 to 4 times the national SBIR win rate.

In this 45-minute session, attendees will see the NIH SBIR Hub live. The Hub is a subway-mapped workspace where three parallel tracks (Strategy, Documents, Business) run through to a single filed application. We will walk through the features doing the heaviest lifting: Thesis Framing (a 50-page deep-research artifact in 7 minutes), 4th Reviewer (an automated mock review trained on thousands of winning and losing grants), the Biosketch and Ancillaries generators, the Apply step where the Research Strategy gets drafted, and the submission-readiness checks that catch compliance issues before submission.

Attendees will leave knowing how to take a blank page to a strong first draft in hours rather than weeks, how to get reviewer-style feedback before a real reviewer sees the application, and how to catch the compliance issues that most often cause an NIH SBIR to be returned without review. Every registrant gets the option to book a 1:1 with the team to walk through their specific solicitation, timeline, or fast-track question.