What we build
Generative AI in federal contexts is not a chatbot on a homepage. It is a production system that drafts policy memoranda, summarizes 800-page inspection reports, translates FOIA requests into structured queries, generates synthetic data for constrained training sets, and routes constituent correspondence to the correct program office. The work is unglamorous and mission-critical — exactly where we operate.
- LLM-powered applications — document drafting, summarization, translation, code generation, knowledge assistants, intake triage, and form auto-fill wired into existing agency systems of record.
- Prompt engineering at production scale — systematic prompt design, version control, A/B evaluation, structured output schemas, and self-consistency decoding for tasks where correctness matters more than cleverness.
- Fine-tuning — supervised fine-tuning (SFT), LoRA and QLoRA adapters, DPO preference tuning, and domain-adaptive pretraining on agency-specific corpora when open-weight models need calibration to federal vocabularies.
- Synthetic data generation — for classifiers that need balanced classes, for privacy-preserving training on surrogate records, and for red-team evaluation sets.
- Evaluation harnesses — domain-specific benchmarks, golden datasets with gold labels, regression test suites, and continuous evaluation pipelines that block bad deployments.
- Guardrails — PII redaction, CUI detection, prompt injection defenses, output classifiers, and policy compliance gates.
The federal GenAI stack
There is no single right model for federal work. The right stack matches the data classification, the latency budget, and the authorization path. We work across:
- Frontier hosted: Claude (AWS Bedrock GovCloud), GPT-4o and o-series (Azure OpenAI FedRAMP High), Gemini (Vertex AI IL4).
- Open-weight self-hosted: Llama 3.1/3.3, Mistral/Mixtral, Qwen 2.5, Gemma 2, Phi-3.5 for on-premise, air-gapped, or classified enclaves.
- Fine-tuning infrastructure: HuggingFace TRL, Axolotl, Unsloth, DeepSpeed ZeRO, FSDP. Training on A100/H100 clusters in GovCloud or on-prem.
- Serving: vLLM, TGI, TensorRT-LLM, Triton Inference Server with continuous batching and prefix caching.
- Evaluation: lm-evaluation-harness, HELM-style custom suites, Ragas for RAG pipelines, DeepEval, and hand-built domain benchmarks.
- Observability: full prompt/response tracing, per-token attribution, hallucination flagging, and cost accounting tied to mission function.
Prompt engineering is real engineering
Most federal GenAI pilots fail because they treat prompts as text, not code. We treat them as versioned software. Every prompt in a production system at Precision Federal has a schema-enforced output contract, a test suite of input-output pairs, a regression suite that runs on every model upgrade, and a rollback plan. Structured outputs via JSON schema or Pydantic models remove the class of bugs where a downstream parser chokes on a model response. Self-consistency and majority voting stabilize high-stakes classifications. Few-shot example banks are curated, not guessed.
Fine-tuning decision framework
Bo's default recommendation to federal clients: exhaust prompt engineering and RAG before fine-tuning. Fine-tuning is expensive to maintain, creates a versioning liability, and locks you to a model generation that will be surpassed by base models in 6-12 months. That said, fine-tuning does earn its keep in four federal scenarios:
- Style and voice — legal opinions, agency-specific memorandum formats, regulatory drafting conventions.
- Constrained classification — a closed taxonomy of 50-200 categories where prompt-based zero-shot is insufficient.
- Latency and cost — a 7B parameter LoRA fine-tune can match a frontier model on a narrow task at 1/20 the inference cost.
- Controlled domain — on-premise or air-gapped work where only open-weight models are available and baseline performance is too weak.
FedRAMP-aligned deployment
The path from LLM prototype to production authorization is where most federal GenAI efforts die. We design for the authorization boundary from day one. Azure OpenAI runs inside a FedRAMP High boundary. AWS Bedrock GovCloud provides Claude under FedRAMP Moderate/High with IL4 and IL5 paths. For classified or unique-data environments, open-weight self-hosted models deployed inside the agency's existing ATO avoid the authorization problem entirely by inheriting it.
Every deployment we ship includes NIST 800-53 control mappings, AI-specific controls from NIST AI RMF, audit logging tied to identity, data classification tagging on inputs and outputs, and pre-built System Security Plan artifacts. See our work on OMB M-24-10 compliance for rights- and safety-impacting AI.
Who we build for
Federal generative AI has natural homes across the enterprise. Precision Federal is actively targeting opportunities with:
- DoD and defense — OSINT triage, after-action report generation, maintenance narrative synthesis.
- HHS and health agencies — clinical documentation, policy synthesis, grant narrative drafting. SAMHSA past performance.
- VA — claims adjudication drafting, benefits letters, clinical note summarization.
- GSA and civilian — FOIA triage, contract language drafting, knowledge management.
- DHS — report synthesis, translation, multi-source intelligence fusion.