I run a small, minority-owned government contracting shop, and I run it on the same pipeline we build here. Not a demo version. The one that pulls my actual opportunities every morning. So instead of describing the product in the abstract, here is what actually happens between when I wake up and when I've decided what to chase today.
The pull happens before I'm at my desk
Every morning, before I open the laptop, the pipeline has already pulled the day's new opportunities from SAM.gov's public opportunities API, filtered to the NAICS and PSC codes we're registered under. This is the same public data anyone can see on sam.gov. Nothing proprietary, nothing scraped from somewhere it shouldn't be. It's just done before I've had coffee instead of me doing it by hand.
The AI reads them against real criteria, not vibes
Each new notice gets scored against a fixed set of criteria: does the NAICS or PSC code actually fit what we do, is the set-aside something we're eligible for, does the size standard match, is there a hard disqualifier buried in the solicitation documents (a certification we don't hold, an OCONUS physical-presence requirement, something classified). This isn't a model guessing whether an opportunity "feels" good. It's a checklist applied consistently to every notice, every day, whether there are five or fifty.
The shortlist is what I actually look at
By the time I sit down with coffee, I'm not looking at a raw feed of everything SAM.gov posted overnight. I'm looking at a shortlist: the notices that passed the hard filters, ranked by fit. This is the part that saves the real time. Reading every solicitation by hand, cover to cover, to figure out if it's worth pursuing is hours you don't get back. Reading a shortlist that's already been narrowed to what's eligible and relevant is minutes.
I still decide what to chase
The shortlist doesn't decide for me. I read the summaries, open the ones that look real, and make the call on which two or three I'm actually going to pursue that day. Some mornings nothing on the list is worth chasing, and that's a legitimate outcome too. A tool that always finds something to bid on is optimizing for busywork, not for winning.
Pricing comes from award history, not a guess
Once I've picked what to chase, the next question is what to price it at. This is where actual award history matters more than anything else in the pipeline. What did similar contracts actually pay, adjusted for scope and vendor. That's a different thing entirely from a model inventing a number that sounds plausible. We wrote more on how that price-to-win estimate gets built in our post on pricing from real award data.
Proposal prep starts, but nothing goes out on its own
For whatever we're pursuing, proposal prep starts the same morning: pulling the compliance requirements out of the solicitation, drafting the sections that map to what's being asked, flagging anything the RFP left ambiguous. What doesn't happen is an AI submitting anything. Every proposal gets reviewed and signed by a person before it goes anywhere near a contracting officer. That's not a limitation we're working around. It's the actual boundary of what the software should be trusted to do.
You can see how the whole sequence fits together, from a SAM.gov notice landing on the board to a signed, submitted bid, on how it works.
The honest version
This isn't a story about a specific number of bids won or dollars in contracts. I'm not going to hand you a stat I can't stand behind. What I can tell you is the mechanism, because it's the same one running every morning whether or not I write about it. The pull happens. The scoring happens. I read a shortlist instead of a flood. I decide, price, and prep. And I still sign everything myself.
The mechanism changes over time too, and we don't sit on that quietly. We publish what actually changed in the pipeline each quarter, in plain English, not a highlight reel.
Related reading
- How AI bid analysis actually works (no black box)
- Using real award history to price a bid instead of guessing
Sources
No external factual claims requiring citation in this post beyond the public SAM.gov opportunities API referenced above, which is linked in-body.