Federal AI Operationalization
Fast Enough to Matter | Secure Enough to Trust | Controlled Enough to Scale
Deploy it division-wide — governed, secure, controlled — in weeks, not years.
Proud Provider of AI@flankspeed
Implementation Award Winner
Trusted Provider
SBIR Phase III Prime
Then what?
Six questions that stop a good AI tool from ever reaching the mission — and how we answer all six from one platform.
“How do we get this to 4,000 people?”
Deploy from one control plane. Publish a validated automation to a team, a program office, or an entire enterprise with role-based entitlements — instead of forwarding prompts and hoping for consistency.
“Where does the data go, and who signs off?”
Inherit a security posture that already exists. We run in Microsoft 365 and Azure-native environments, deployed today under an IL5 ATO rated high/high/high with a PII overlay, inside a Zero Trust architecture.
“Who is accountable for the output?”
A named human, by design. Approval steps, review queues, and escalation into Teams or Slack keep mission experts in the decision path. The expert decides; the AI assists — and tunes or retires the automation as the work changes, without becoming an engineer.
“Who supports it at 0200 on a Sunday?”
Operate it like a system, not a science project. Monitoring, real-time auditing, versioning, and model updates are platform functions with defined ownership — every action, prompt, output, and approval logged for audit, oversight, and IG inquiry.
“What is this costing us?”
Cost dashboards down to the workflow. Track token, compute, and license consumption by use case, team, and time period, so spend is attributable before it’s scaled.
“How do I prove it worked?”
Measure value before you scale spend. Adoption, throughput, task completion, and time returned are captured as the work happens — giving leaders defensible evidence for the next investment decision.
Already operating inside federal security, not adjacent to it.
We've spent over a decade in applied AI and natural language work, and we're in production today inside one of the most security-constrained enterprise environments in government.
Government proof
Deployed as AI@FlankSpeed
Operates inside the Navy's Flank Speed enterprise environment — evidence the platform works past the concept stage in a real federal enterprise.
Compliance proof
IL5 ATO, high/high/high, PII overlay
An existing authorization at Impact Level 5 with a PII overlay, so security review starts from a documented posture rather than from zero. Includes live automations against CUI categorization and RMF onboarding — two of the highest-friction compliance burdens in federal work.
Security proof
Zero Trust alignment
Built to operate under the identity, segmentation, and monitoring expectations agencies are already being measured against. Runs Microsoft 365 and Azure-native, with commercial, classical, and government-owned models operating side by side under one set of controls — including cases where a frontier model isn't an option.
Shrink the problem. Prove the value. Scale what works.
A typical engagement runs in three phases, and there's more than one way to start one.
Phase 01 · First 30 days
Pick the workflow worth fixing
Select a bounded, high-value use case. Curate the data. Confirm the security path, the human control points, and the baseline you'll measure against.
Outcome: a scoped use case with a defined measure of success.
Phase 02 · Days 31–60
Stand it up under control
Build the automation, wire it into the tools users already open, set approval and escalation rules, and put it in front of a real user group.
Outcome: a governed automation in operational use.
Phase 03 · Days 61–90
Prove it, then widen it
Report adoption, throughput, cost, and time returned against the baseline. Retire what underperforms. Extend only what earns the next increment of spend.
Outcome: evidence and a defensible scaling decision.
Routes in
Agency-direct
Work with us directly on a scoped use case inside your program office.
Prime teaming
Add governed AI capability to an existing vehicle or a pending pursuit.
SBIR Phase III
Use the data rights and Phase III pathway to shorten the contracting timeline.
Bounded pilot
Prove one workflow, on a fixed scope, with a defined measure of success.
The same platform. A different problem depending on your seat.
Move faster without surrendering oversight.
- Get a practical path from AI experimentation to governed mission workflows.
- Prove AI value before you scale AI spend.
- Show progress that a hearing, an IG, or a budget review can withstand.
Turn a workflow problem into a measurable automation.
- Start with the bottleneck you already know is costing your team hours.
- See, manage, and improve AI use across the workflows you own.
- Keep your mission experts in control of the output.
Replace shadow AI with a governed deployment model.
- Access control, monitoring, auditability, and lifecycle oversight in one place.
- Azure and M365-native, IL5-authorized, Zero Trust-aligned.
- Reduce uncontrolled AI sprawl by organizing use cases inside a platform you can inspect.
Ready to Deploy Your Future?
Join the organizations transforming their experts into capability creators. Let's discuss how Nearly Human can accelerate your mission.