Capability
Generative AI & LLMs
LLM applications federal missions can actually use: retrieval-grounded answers, agentic workflows, and document intelligence — deployed inside your authorization boundary and evaluated like any other mission system.
Proven where it counts
120-day MVP delivery
Partnered with a prime contractor to rapidly deliver an MVP web application for audit reporting — deployed within 120 days.
- 18F
- Agile
- Rapid Delivery
High-performance data engineering
Built and supported a data warehouse capable of sub-second, high-throughput queries. Designed ETL pipelines using Apache Spark Structured Streaming in the cloud.
- Apache Spark
- ETL
- Big Data
100+ microservices, event-driven architecture
Accelerated build, deployment, and scaling timelines for a large-scale program by maintaining 150+ AWS EC2 instances and a 90-microservice polyglot containerized platform.
- Java
- GovCloud
- Microservices
What we deliver
RAG & Grounded Chat
Retrieval-augmented generation over your authoritative documents — answers that cite their sources, with chunking, retrieval, and faithfulness tuned and measured.
Fine-Tuning & Adaptation
Domain adaptation of open-weight and commercial models — trained inside your environment, on your data, under your control.
Agentic Workflows
Tool-using, multi-step agents that draft, triage, and route — with human-in-the-loop checkpoints and a full audit trail.
Guardrails & Safety
PII redaction, prompt-injection defenses, output filtering, and jailbreak red-teaming before anything reaches a user.
Evaluation & Assurance
Task-level evaluation harnesses with faithfulness and quality metrics — evidence mapped to the NIST AI Risk Management Framework.
Deployment & Operations
LLM serving in GovCloud, IL4/IL5, and air-gapped environments — latency and cost tuning, versioned models, and production monitoring.
Approach
Grounded answers, not confident guesses.
A language model on its own is a fluent liability. We make it dependable the way you would any mission system: retrieval grounds every answer in your authoritative sources, evaluation gates every release, and monitoring watches quality in production. Capability you can brief, evidence you can authorize.
- Retrieval over your authoritative sources — with citations
- Evaluation gates before anything reaches users
- Your data never trains shared models
- Runs inside your authorization boundary
Governed data across cloud and on-prem. Built secure data pipelines connecting Salesforce cloud and on-prem TSA systems — the governed, real-time data access grounded AI systems depend on.
Technologies we work in
- Claude
- GPT
- Llama
- Mistral
- LangChain
- LlamaIndex
- pgvector
- OpenSearch
- vLLM
- Guardrails AI
Common questions
Can we use LLMs with CUI or other sensitive data?
Yes — by keeping everything inside your authorization boundary. We deploy open-weight models on your infrastructure (GovCloud, agency VPC, or fully air-gapped), so prompts and documents never leave your control. Your data is never used to train shared or third-party models.
How do you control hallucinations?
Grounding first: answers are built from retrieved passages of your authoritative sources and must cite them. We add output guardrails, measure faithfulness with task-level evaluations before release, and keep a human in the loop where the stakes demand it. Ungrounded speculation fails evaluation; it doesn't reach users.
Commercial model APIs or open-weight models?
Both, chosen per mission constraint. Commercial APIs (Claude, GPT) are often right for unclassified work with strong capabilities; open-weight models (Llama, Mistral) run inside your boundary when policy or data sensitivity requires it. We stay model-agnostic so you can switch as the field moves.
How does this align with the NIST AI RMF and OMB guidance?
Delivery follows the framework's Govern, Map, Measure, Manage functions: documented use-case risk, measurable evaluation criteria before build, and monitoring plus incident response after launch. Model cards, evaluation reports, and usage policies ship with the system and feed your authorization and OMB AI reporting.
What makes a good first GenAI project?
A painful, well-bounded knowledge workflow: searching policy manuals, drafting routine correspondence, triaging cases or tickets. We scope a pilot around one such workflow with evaluation criteria agreed up front — typically weeks, not quarters — then harden what proves out into production.