What we build
Most "AI" delivered to federal agencies is a chatbot wrapped around a vendor API, fielded as a proof of concept, and abandoned after the demo. We build the opposite: production agentic AI systems that autonomously plan, reason, take actions, and pass security review.
- Multi-agent orchestration — coordinated specialist agents (retrieval, reasoning, tool-use, verification) with clear handoff protocols and audit trails.
- Retrieval-augmented generation (RAG) over federal document corpora, with provenance tracking so every generated claim can be traced back to source.
- Tool-calling systems that safely invoke internal APIs, databases, and workflow engines — with allow-listed actions and human-in-the-loop gates for high-risk operations.
- Prompt injection hardening and adversarial evaluation, because federal deployments face adversaries your open-source eval suites don't.
- Model gateways that route requests to the right model (Claude, GPT-4, Llama, Mistral) based on task, security tier, and cost.
Federal Agentic AI Use Case Maturity
Stack
We work across the major frontier and open-weight model families and their federal deployment paths:
Frontier
Anthropic Claude (via AWS Bedrock GovCloud), OpenAI GPT-4 / o-series (via Azure OpenAI FedRAMP High), Google Gemini (via Vertex AI).
Open-weight
Llama 3.x, Mistral, Qwen — for air-gapped, classified, or on-premise deployments.
Orchestration
LangChain, LangGraph, custom agent frameworks built on Pydantic + FastAPI for auditability.
Vector & retrieval
pgvector, Weaviate, Qdrant, hybrid BM25+dense retrieval.
Observability
full prompt/response logging, LangSmith-equivalent custom tracing, token-level attribution.
Federal deployment considerations
Building agentic AI for federal agencies is not the same as building a SaaS chatbot. The questions we design around from day one:
Data residency
does the model see CUI, PHI, PII, or classified data? That determines the deployment path.
FedRAMP status
only certain LLM API endpoints are FedRAMP-authorized. We map your use case to compliant paths.
ATO boundary
does the system live inside an existing Authority to Operate boundary, or does it need its own?
Audit & accountability
every federal deployment needs traceable logs. We build these in, not bolt them on.
Failure modes
hallucination in a federal context is not a UX issue, it's a legal and mission issue. We design for graceful degradation and mandatory human review on low-confidence outputs.
Who we build for
Our agentic AI work is well-suited to federal missions that involve synthesizing information at scale, routing or triaging cases, or automating document-heavy workflows:
- DoD / intelligence community — OSINT synthesis, report triage, multi-source fusion
- Civilian agencies — grant review, FOIA triage, constituent inquiry routing
- Healthcare (HHS, VA, DHA) — clinical documentation, evidence synthesis, policy analysis
- Law enforcement (FBI, DHS, USSS) — lead generation from tips, case file summarization