We’ve been building with AI since before the hype.
Production systems with LLMs, RAG, and agents — from early research frameworks to enterprise deployments. We’ve watched three hype cycles come and go. We’re still shipping.
What we build with AI.
Eight capability areas. All delivered by senior engineers who’ve shipped these systems to production before.
Frontier & Open-Weight Models
Model selection, evaluation, fine-tuning, deployment, and governance across every major provider — frontier APIs and self-hosted open weights.
Retrieval-Augmented Generation (RAG)
Custom knowledge pipelines that ground AI responses in your proprietary data. Accurate, auditable, hallucination-aware.
AI Agents & Orchestration
Autonomous agents that plan, use tools, and complete multi-step work — with MCP integrations, orchestration frameworks, and human-in-the-loop controls.
Prompt Engineering
Systematic prompt design, evaluation frameworks, meta-prompting, chain-of-thought, and structured outputs.
AI Security & Governance
Threat modeling for AI systems and agents: prompt injection defense, shadow-AI discovery, agent identity and permission boundaries, data leakage prevention, and governance mapped to ISO/IEC 42001.
Enterprise AI Integration
Connect AI to your existing systems — CRMs, ERPs, databases, APIs — over open standards like MCP. Production-grade pipelines, not demos.
AI for Healthcare
HIPAA-compliant AI deployment, PHI-aware pipelines, clinical workflow automation, and compliance documentation.
AI Strategy & Roadmapping
Where to start, what to build, which models to use, how to measure ROI. Clear plans — no buzzwords.
Every major AI stack. In production.
We evaluate, architect, and deploy across the full AI landscape — commercial and open source.
Agent Standards & Orchestration
- Model Context Protocol (MCP)
- MCP server design & security
- Agent-to-Agent (A2A) interop
- LangGraph
- CrewAI
- Human-in-the-loop workflows
- Agent evaluation & observability
Anthropic
- Claude (Opus, Sonnet, Haiku)
- Claude Code & Agent SDK
- Extended thinking & tool use
- Computer use agents
- Claude API
OpenAI
- GPT-5 family
- Responses & Assistants APIs
- Agents SDK
- Enterprise ChatGPT rollouts
Microsoft AI
- Azure AI Foundry
- Microsoft Copilot & Copilot Studio
- Agent Framework
- Azure AI Search
- Entra + Purview AI governance
Google AI
- Gemini (3.x family)
- Vertex AI & Agent Builder
- NotebookLM
- Gemma open models
Amazon AI
- Amazon Bedrock
- Nova model family
- Bedrock AgentCore
- Guardrails & Knowledge Bases
Open Weights & Tooling
- Llama 4
- Mistral
- xAI Grok
- Ollama / local deployment
- Hugging Face
- Vector search (pgvector, Pinecone, Weaviate)
- Eval harnesses & LLM observability
The agent era needs guardrails.
Agents spread through organizations faster than security teams can review them. Industry research now puts confirmed or suspected AI-agent incidents at most enterprises within the past year — while fewer than a third say they are prepared to secure agentic systems. That gap is the work.
of organizations reported a confirmed or suspected AI-agent incident in the past year
say they feel prepared to secure agentic AI systems
have no documented policy for creating or removing agent identities
Figures drawn from 2026 industry security research, including Cisco’s State of AI Security and Cloud Security Alliance agent-governance findings. Directional, not audited.
Shadow agents
Employees are connecting personal AI agents to corporate email, chat, and documents — usually without anyone in security knowing. We find them, assess the blast radius, and give your team a safe path to yes.
Agent identity
Machine identities already outnumber human ones many times over, and most legacy IAM was never designed for them. We give agents first-class identity: scoped short-lived credentials, per-task authorization, and audit trails that separate agent actions from human ones.
Agent supply chain
Community skill and plugin marketplaces have already shipped thousands of malicious packages, and most published MCP servers ship without meaningful auth. We vet agent tooling with the same rigor as any code you run in production.
Least-privilege autonomy
An agent with your credentials is an insider. We design permission boundaries, human-in-the-loop approvals, audit trails, and a kill switch — before the first deployment, not after the first incident.
How we think about AI.
Three principles we don’t compromise on.
Production or Nothing
Most companies are piloting AI; fewer than a third see real returns. The gap isn’t the models — it’s workflow redesign, data readiness, and follow-through. We build for deployment, and every engagement ends with something running in production.
Security First, Always
AI systems are attack surfaces — and agents widen them. We design with prompt injection defense, least-privilege agent permissions, data isolation, and output validation from the first line of code.
Right Tool, Right Problem
We’re not married to any vendor. Frontier models leapfrog each other every quarter — we evaluate against your use case, your data, your latency, and your budget, then recommend accordingly.
Have an AI problem worth solving?
We’ll assess feasibility, recommend an architecture, and tell you what’s realistic — before you spend a dollar on infrastructure.