Software that survives an agency review.
Every capability we publish comes with the deliverable, the artifact and the evidence behind it — so a prime can scope a task order straight from this page.
Fairs Place LLC builds AI, data and cloud systems for federal programs and enterprise teams. Modern stack, AI-agent-accelerated delivery, and security and compliance as the line we never move.
Every workstream ends in a release record.
Not a status slide — the artifact a program office receives, with the gate either cleared or not.
Eight areas. Each publishes its deliverables.
Written so you can scope a task order from the page. Full deliverables and sample artifacts →
GenAI Delivery & Enablement
Production agents and workflows in your environment, with an evaluation harness and a live handover.
MLOps & GenAIOps
Versioned artifacts, reproducible evaluation in CI, drift monitoring and tested rollback.
Context & Feature Store Engineering
Context schemas, measured retrieval, and a per-decision trace an auditor can read.
Cloud Platform & Site Reliability
Infrastructure as code, SLOs with alerting bound to them, and a runbook your team can execute.
Health IT Modernization & Compliance
HL7 interoperability and PHI-safe engineering, delivered under an active HIPAA program.
Security & Compliance Engineering
NIST AI RMF and SP 800-171 mapped to named evidence, not generic security language.
Application Development & Delivery
Spec and tests before code, CI gates from the first commit, docs for the team that inherits it.
Mission Automation & Workflow
Pipelines with retries and dead-letter paths, and before/after time-on-task as a number.
AI-agent-accelerated delivery, guardrails named out loud.
Agents draft, refactor, generate tests and write documentation. They are never the last reviewer.
Scope in writing
Named deliverables, acceptance criteria and the data boundary, agreed before any code.
Spec, then build
The spec and its tests exist first, so "done" is checkable rather than argued.
Agents inside the fence
Approved repositories, authorized or synthetic data, human review before every merge.
Test and evaluate
Evaluation suites with recorded baselines. A regression blocks the release gate.
Hand over, not off
Runbook, control mapping and a working session. You can run it without us.
No client or government data enters a tool that has not been approved for it.
A human engineer owns every merge, every release and every answer to your program office.
We work inside your security boundary and your accreditation process, not ours.
GenAI Context Engine
A memory and retrieval layer for GenAI systems that have to be auditable. Most GenAI failures are context failures, not model failures.
Not generally available. Not deployed in a production government environment, and we will not pitch it as one.
See the architecture and current status →
How we de-risk a subcontract
Security posture
Aligned to NIST AI RMF and SP 800-171. Your compliance obligation is ours.
IP & data handling
Work-for-hire by default, inside your existing security boundary.
Communication cadence
Weekly status against a shared plan, direct to the engineer doing the work.
Engagement models
Labor-hour or FFP task orders, embedded augmentation, or a scoped workstream.
One conversation, one form.
Teaming, a services engagement, or the Context Engine design partner program — same form, same engineer replying. Responding to an RFP? Reach out before the deadline, not after.