Implementation discipline,
not pilot purgatory.
Most federal AI work fails not because the model is bad but because the work around the model is unowned. Our method puts a measurable outcome, a security boundary, and an operations plan in place before a model is selected.
The path, end to end.
What ships at each step.
- 01phase
Scope to a measurable outcome
Anchor to a specific mission metric — review cycle time, cost per case, time-to-decision, error rate. If a use case can't be tied to one, we say so.
DeliverablesTarget metric · Baseline · Success criteria · Security boundary · Acceptance plan - 02phase
Map the data foundation
Catalog the inputs the system needs, identify gaps, design ingestion and governance — including how PII / FOUO / CUI are handled within your boundary.
DeliverablesData inventory · Lineage diagram · Governance plan · Retention model - 03phase
Choose the right model strategy
Off-the-shelf, fine-tuned, or hybrid retrieval — driven by accuracy, cost, latency, and where the data is allowed to live. No model loyalty.
DeliverablesModel selection memo · Hosting plan · Fallback strategy · Exit criteria - 04phase
Build with evaluation in
Every system ships with an evaluation harness — task-specific test sets, regression tracking, human-in-the-loop where stakes warrant.
DeliverablesEvaluation suite · Dashboards · Review workflow · Escalation paths - 05phase
Deploy inside your boundary
On-prem, GovCloud, classified enclaves, or hybrid. Identity from your IdP. Logging and audit feed your existing SIEM.
DeliverablesATO-ready architecture · Runbooks · Monitoring · Incident playbook - 06phase
Operate, measure, hand off
Run with you until performance is steady against the target metric. Then hand off — code, models, and institutional knowledge.
DeliverablesSLO report · Training materials · Ownership transfer · Support runbook
How we run an engagement.
Auditability over novelty
Every output is explainable, sourced, and reviewable. If a model can't show its work, it doesn't ship.
Small wins, then scale
A 6-week pilot that moves a real metric beats a 12-month roadmap that doesn't. We compound from there.
Boundary-respecting design
Data classification, accreditation level, and identity model are inputs to the architecture, not afterthoughts.
Human-in-the-loop where stakes warrant it
Adjudication, eligibility, safety calls — humans approve, AI accelerates.
No vendor lock-in
Open formats, exportable models, replaceable components. Your team can swap providers without rewriting the system.
Operate before declaring done
We run the system in production with you until performance is steady, then hand off cleanly.