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.
Models that hold up in production, built and monitored by a Kaggle Top 200 data scientist who has shipped them inside a federal health agency.
Agents that call your tools, follow your rules, and leave an audit trail an authorizing official can read.
LLM applications tuned on your documents and deployed inside your boundary, GovCloud or air-gapped.
Pipelines that move mission data reliably at production scale, with lineage and governance in the same code.
Dashboards and decision tools built for the question leadership actually asks, and for the audit that follows.
Catalogs, column-level lineage and CUI classification mapped to the Privacy Act and NIST 800-53, ready for the ATO package.
Federal architectures on AWS, Azure and GCP, designed by an engineer holding 7 cloud certifications and aligned to FedRAMP.
Landing zones, IL4 and IL5 workloads, Bedrock and SageMaker in AWS GovCloud, with 800-53 controls inherited on purpose.
Azure Government and Azure OpenAI at FedRAMP High, with IL5 and IL6 boundaries and Sentinel and Defender wired in.
NIST 800-53 controls, STIG hardening and DevSecOps pipelines that get federal AI and data systems to authorization faster.
NIST 800-207 and the DoD reference architecture engineered into identity, network and workload, not just drawn on a slide.
RMF from control selection to SSP, POA&M and continuous ATO, written by the engineers who built the system.
Whole applications on React, Next.js, FastAPI and PostgreSQL, containerized and shipped with USWDS and Section 508 done.
REST, GraphQL and gRPC to the USDS API standards, documented in OpenAPI 3.1, published on api.gov, fronted by Kong or Apigee.
React, Next.js, Vue and Svelte interfaces on USWDS, accessible to Section 508 and WCAG 2.2 AA before the first review.
AI engineered for air-gapped and export-controlled (ITAR/EAR) environments, with the ATO support to match. JCP / DD-2345 certified.
Remote sensing, geospatial machine learning and ArcGIS pipelines for satellite, drone, LiDAR and terrain data.
Physics-grounded twins for fleets, facilities and mission systems, with the data plumbing and validation that make them true.
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, partnering and contracting questions — every inbound is answered within one business day.
