The AI capability layer for federal prime contractors.

You win the contract. We sub in to add the AI capability your team doesn't have in-house: RAG, agent workflows, LLM integrations, AI infrastructure, voice AI, evals. We never prime. We can't compete for your contracts.

RAG + Agents Voice AI LLM Infra AI Evals + Governance SAM-Registered · Sub-K Only
How we engage

Three steps from teaming call to deployed AI.

No long discovery, no slideware. Scoped SOW, fast mobilization, end-to-end ownership.

Step 01

Teaming conversation

A 20-minute call on the contract you've won, the agency context, and where AI capability would unlock the most value in your delivery.

Step 02

Scoped sub-K SOW

One defined AI capability, one module, one deliverable. Typically $100K to $200K. We sub under your existing contract vehicle.

Step 03

Two-week mobilization

Signed to first sprint in 14 days. Owned end-to-end through deploy. We document, we hand off, you keep delivering the rest of the contract.

How we fit

We add the AI capability. You keep the contract.

We sub in, deliver the AI capability your team doesn't have in-house, and hand it back. And because we have no prime contract vehicle of our own, we're structurally unable to compete for your work — that's not a pledge, it's just how we're built.

What this means for you

  • You keep the prime relationship, the past performance, the agency credit
  • We deliver, document, and hand off — your team owns the rest
  • We never bid your contracts when they re-compete
  • We don't pitch your agency client behind your back

What we need from you

  • A 20-minute teaming conversation
  • The contract scope and AI capability gap you're trying to close
  • A defined module or feature we can own end-to-end
  • About two weeks of calendar time to mobilize and ship
Capability menu

Four leads. Twelve capabilities.

We scope new opportunities around the four headline capabilities below. The full menu is what we can deliver when scope expands or shifts.

Plus the full capability menu

5

Data → AI pipelines

Document ingestion, chunking, embedding, knowledge base curation, synthetic data.

6

AI safety + guardrails

PII detection, content filtering, policy enforcement, bias and fairness validation.

7

LLM / GenAI features

Smart search, summarization, auto-drafting, translation, code generation, content review.

8

Fine-tuning + model custom

LoRA, distillation, smaller specialized models, domain adaptation.

9

AI-augmented engineering

Cursor / Claude Code-accelerated delivery, AI-driven test generation, code review.

10

AI strategy + architecture

Capability roadmap, build-vs-buy analysis, federal AI implementation playbook.

Past performance

Federal credentials, through BetaQuick LLC.

BetaQuick Services inherits the past performance and federal posture of its parent. Full capability statement with citations available on request.

Federal Health

Social Security Administration

AI voice agents and intelligent workflows deployed in SSA programs.

Federal Health

National Institutes of Health

Software engineering and AI capability work supporting NIH programs.

Defense / Enterprise

General Dynamics · Capital One · Under Armour

Enterprise-scale software engineering across defense and Fortune 500 environments.

About

The AI specialty arm of BetaQuick.

BetaQuick Services is the AI capability practice inside BetaQuick LLC, the SAM-registered federal contractor with past performance at SSA, NIH, General Dynamics, Capital One, and Under Armour. Led by Wale Fawehinmi, an engineer and operator with a decade of federal and enterprise software experience.

Our entire model is sub-K. We have no prime contract vehicle and no plans to build one. That makes us the cleanest teaming partner small primes have: we add AI capability to your delivery, we never compete for your contracts, and we mobilize in two weeks.

Get in touch

wale@betaquick.com

Working with companies across North America.

Just won a federal contract with AI scope inside it?

20-minute teaming conversation. We'll tell you honestly whether the AI capability gap is one we can close, and how the sub-K would scope.