Precision Federal builds AI, data platforms, web applications, APIs, cloud and security systems for U.S. federal agencies, and delivers them into the environment where they will run.
63 capability references across six technical pillars. Each page is written by the engineer delivering the work.
Classification, regression, ensembles, feature engineering — the discipline behind the founder's production ML work at a federal health agency.
Multi-agent orchestration, tool-calling, autonomous decision systems — engineered for ATO-bound federal deployment.
LLM app development, prompt engineering, fine-tuning, and fed-compliant deployment patterns across GovCloud and air-gapped boundaries.
Pipelines, ingestion, orchestration, and governance for federal mission data — lakehouse-native at production scale.
Analytic platforms and decision-support systems for federal leadership — dashboards engineered for the audit and the action.
Catalogs, column-level lineage, CUI classification, Privacy Act, and NIST 800-53 mapping for ATO-ready data platforms.
Multi-cloud federal architectures across AWS, Azure, and GCP — FedRAMP-aligned, IL4/IL5-aware, built by an engineer with 7 cloud certifications.
AWS GovCloud (US) engineering with IL4 / IL5 workloads, Bedrock, and SageMaker — landing zones and 800-53 control inheritance patterns.
Azure Government, Azure OpenAI FedRAMP High, and IL5 / IL6 boundary engineering with integrated Sentinel and Defender.
NIST 800-53 implementation, STIG hardening, and ATO acceleration — the compliance spine that lets federal AI and data systems reach authorization.
NIST 800-207 Zero Trust and DoD Zero Trust reference architecture — engineered into identity, network, and workload boundaries.
NIST RMF, 800-53 control implementation, SSP authoring, POA&M management, and continuous ATO — authorization-aware from day one.
React, Next.js, FastAPI, PostgreSQL, Docker, and Kubernetes — across-the-stack delivery with USWDS and Section 508 baked in.
REST, GraphQL, gRPC, USDS API standards, OpenAPI 3.1, api.gov publication, and Kong / Apigee gateway engineering.
React, Next.js, Vue, Svelte, TypeScript — USWDS components and Section 508 / WCAG 2.2 AA accessibility built in from day one.
Air-gapped LLMs, SCIF engineering, ITAR/EAR-compliant training, MLS, and TS/SCI ATO support — AI delivery into classified federal environments.
GIS, remote sensing, geospatial ML, and Esri ArcGIS for federal — satellite, drone, and terrain analysis pipelines for defense and civilian missions.
Digital twin for federal assets, simulation, and predictive maintenance — physics-grounded models for fleet, facility, and mission-system operation.
A public-record tour of maritime domain awareness software — space-based radar, ship transponders, undersea acoustics, tracking, and the data fusion between them.
What is in production and what is in pilot across VA, HHS, DOT, DHS, SSA, Treasury, DoD and the rest of the federal enterprise, and what each program office is buying next.
What a digital twin is and is not, what DoDI 5000.97 changed, and why the real engineering is data plumbing, state synchronization, and validation.
A public-record map of the Department of the Navy's 2026 AI posture: the offices, programs, enterprise infrastructure, and pathways where small firms fit.
Public-literature reading of AI-assisted modernization — reverse engineering, dependency analysis, test reconstruction, and the engineering discipline that scales.
The software innovation patterns and recurring problem types shaping the Department of the Air Force in 2026.
A practical 2026 playbook to cut AI-system ATO time from 12 months to 3-6 — control inheritance, evidence pipelines, and the engineering decisions made on day one that determine the authorization timeline.
Evaluating retrieval quality in federal RAG: separating retrieval from answer evaluation, gold question sets, recall and rank metrics, groundedness, and drift.
Process monitoring, defect detection, qualification, and the lab-to-field gap — what the public AM-AI literature has settled and what remains open.
Deepfake detection for federal missions: generation families, artifact and frequency cues, C2PA provenance, the generalization problem, and evaluation discipline for acquisitions.
Why disconnected, bandwidth-limited federal environments push toward the 1-15B model class — what it does well, where it breaks, quantization, licensing, hardware, and evaluation.
Sensor modalities, signal-processing pre-stages, public datasets, and the engineering choices that show up in agency-funded prototypes.
Federal programs, state and local work, commercial engineering, teaming and contracting questions — every inbound is answered within one business day.
