Full ExpressionAI
Case Study

Inside the AI/ML platform that runs our own federal BD pipeline.

Full Expression AI doesn't just advise on AI and automation for govcon BD — it runs one in production, every night, on a real federal pipeline. Here's how it's built.

The problem

Manual solicitation screening tops out fast. A capable analyst can work through maybe 3–4 opportunities a day once you account for reading, qualifying, and pricing each one — nowhere close to the volume that DLA, the services, and civilian agencies post daily. Most of that volume is genuinely not worth pursuing, but figuring out which few are worth a proposal still ate a full-time analyst's day, every day.

What we built

A production system — a Python/ML data pipeline, a deployed MCP tool server, and a React command center over a shared Postgres spine — that took the same daily grind from concept to unattended overnight operation in under two months.

Chalkboard illustration of documents funneling into a brain with circuit lines, then a gauge, then a human hand pressing a decision button
The shape of the pipeline: machine triage in volume, a human hand on the decision.
1

Ingest

Pulls DLA DIBBS, SAM.gov, HigherGov, and USASpending daily — every new solicitation, in one place, before a human ever looks at it.

2

Qualify

A two-pass LLM routine screens every opportunity against a coded disqualifier taxonomy — the same logic a senior capture manager would apply, run at machine speed.

3

Price

Survivors get priced by a self-calibrating machine-learning model that publishes an honest confidence label instead of a false-precision number.

4

Work

A 25-agent automation layer — account executives bound to individual buying commands, shared research and proposal desks, and a manager agent grading it all weekly — carries the analysis forward.

5

Gate & sync

Every CRM commitment sits behind a code-enforced human bid/no-bid gate before it syncs into the Salesforce-based pipeline — the system recommends, a person decides.

Federal contract opportunities posted to SAM.gov in the last 7 days
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Results

75
Unattended overnight production runs to date
~1,700
Opportunities eliminated as no-bid before any AI spend
$1.26T
In tracked federal awards feeding the pricing model
37K
Priced parts referenced by the price-to-win model

Replacing a manual ceiling of 3–4 opportunities a day, the platform now delivers qualified opportunities into the CRM the same morning they post — peaking at 43 in a single day.

Why this matters for your team

This is the same pattern behind every Full Expression AI engagement: start from what actually happens in federal BD day to day, then automate the specific, repeatable decision points — not a generic "add AI to everything" pitch. If your team is screening opportunities, tracking pipeline, or pricing bids by hand and spreadsheet, an Automation Sprint is how this gets built for you specifically, scoped to one workflow at a time.

Want to see where this fits your pipeline?

A Readiness Audit is the fastest way to find out what's worth automating first.

Book a Readiness Audit