Your people are your product. The government never stops grading it.
In small federal contracting, the contract win is one moment. Everything after it — often for years — is the real game: putting the right defensible person in the right seat, at a cost you can justify, and never getting caught flat-footed when the CO, the requirements, or the budget shift under you.
The Applied AI Diagnostic is a structured, four-to-six-week read on where applied AI actually changes the odds in that game — across staffing, BD, capture, proposal, contracts, and the back office — and where it doesn't, at least not yet. Delivered by the founder who built the platform, not handed to a junior team.
Request a scoping conversationThe hard parts aren't secrets. They're the job.
You already know this game. What most AI vendors don't understand is that in your world, the person is the unit of value — bought by the government as a line item, evaluated continuously, and simultaneously your product, your scarcest asset, and your biggest exposure. Here's where that plays out, and where applied AI earns its place.
Lining up cleared talent against a moving start date
You win, the start date is “X” — but X can shift, and you need cleared bodies in seats inside the window. To land a cleared candidate, they have to leave their current contract and firm, and they won’t move without a date you often can’t give. The offer is contingent on CO acceptance anyway. Court someone for months, lose them to timing. Meanwhile your own people get pulled to better contracts with no warning.
Where AI helps: Faster candidate-to-LCAT matching against your live pipeline, so you know who's truly placeable, where, and how fast — and you spend your courting energy on the people you can actually seat.
Defending the LCAT-versus-reality gap
A long-tenured performer with strong reviews can still be removed because the written LCAT requires a degree they don't have. Performance doesn't decide it; the paperwork does. And when you swap a high performer onto a better contract and backfill with a qualified-but-lesser person, you've protected revenue and invited credential scrutiny.
Where AI helps: Audit-ready submission packages that map each person to the labor category honestly and defensibly, with the supporting evidence assembled before anyone asks — not the week the CO does.
Holding margin against the wrap-rate squeeze
On a non-fixed vehicle, justifying a rate increase at cycle end is income for the performer and the firm. On a fixed wrap rate, a contractor you can't afford to lose may force you to eat the cost to keep the seat. People are scarce; the clearance is the ticket; the math is unforgiving.
Where AI helps: Faster, evidence-backed rate-justification narratives and cleaner back-office throughput, so margin decisions are made on real numbers, quickly, when the window to act is short.
Staying ready when the CO and requirements drift
Tastes change at extension or term end. Positions shift, requirements get rewritten, and you're suddenly defensive — resubmitting and defending incumbents you thought were settled. And as a small prime, you often don't get the most experienced COs; the A-team is on the large-prime vehicles, so more of the burden of getting it right lands on you.
Where AI helps: Always-current capability and past-performance evidence, plus the ability to quickly resubmit resumes against revised LCATs — so a requirements shift becomes a fast re-justification instead of a fire drill.
Knowing who you're teaming with before you're bound to them
The partner you bring on can sink an award before or after it's decided — reputational risk on a JV has pulled entire awards post-decision. Who you team with is a pipeline decision, not a paperwork one.
Where AI helps: Faster, more thorough partner and teaming due-diligence synthesis, so you walk into a teaming decision with the full picture, not a rushed one.
Surviving the shutdown-and-freeze accounting reality
When appropriations lapse, the money stops but your costs don't — you float payroll while contracts can't pay you. The restart comes back as a patchwork of bridge contracts, short extensions, and re-baselined CLINs to reconcile fast and tie back to the right work. It's a burden the government's process isn't built to ease, and it lands on you.
Where AI helps: Back-office automation that keeps re-invoicing and reconciliation fast and accurate when the funding calendar won't cooperate.
How we read your firm: the four-move blueprint
The diagnostic isn't a vendor opinion. It runs on the same discipline that separates the firms that get AI right from the ones that walk it back a year later — synthesized in our research, Augmentation, Not Replacement.
Measure before you cut.
A structured read on where AI moves the needle in your actual workflows, grounded in evidence — not a demo.
Redesign the work, then shift the composition.
Reorganize around AI first; change the staffing pattern second. Sequence protects you.
Protect the bottom.
Your performers are your product and your pipeline. Augment them so they're more defensible — don't hollow out the bench you'll need when a dormant award finally activates.
Treat trust as the asset it is.
In a world of continuous evaluation and long relationships, reputation with the CO and your people is the asset you're defending.
What you walk away with
Every engagement produces a written assessment of where applied AI fits your firm, a scoped proposal for what comes next if you proceed, and a working session with your leadership team to walk the findings. Depth scales with the engagement; the structure doesn't. We send the full engagement brief when we talk.
Built by someone who's been in the room
Most AI consultants are researchers or vendors. Keith Elliott is a builder. He architected and shipped the customer-zero version of the Rigovera platform — LCAT-aware, built for the IC staffing cadence — for a small intelligence-community prime. He built and deployed a Shipley-method bid/no-bid engine for that same firm, grounded in 6,000+ of their own contract and proposal documents, with every analysis traced back to source. He wrote Augmentation, Not Replacement. He writes the code and architects the agents himself.
That's the vantage every diagnostic brings: technology serves the mission, not the other way around. Adopting AI isn't the challenge. Using it well is.