Agents that reason, and act.
We build multimodal AI that understands language, vision, and structure, connected to your systems through the Model Context Protocol, fine-tuned to your domain, and governed from the first line of code.
AI that earns its place in production.
Demos are easy. Reliable AI inside a real business is not. We engineer the context layer of retrieval, tools, memory, and guardrails, so models act on the right information and stay inside the lines.
Every agent we ship is observable, evaluable, and accountable. You see what it did, why, and what it cost.
Four ways we put AI to work
From the agents themselves to the strategy that points them at the right problems.
AI Agents
Multimodal agents that reason over your data and take action through your tools, built to run reliably in production, not just in a demo.
- Context Engineering & Retrieval AgentsThe retrieval, memory, and grounding that let agents reason over the right information.
- Agent Tracing & TrackingFull observability: every step, tool call, and cost, captured and auditable.
- Agent HarnessThe evaluation and guardrail framework that keeps agents safe, tested, and accountable.
MCP Integration
Model Context Protocol servers that give your models safe, structured access to your data, tools, and systems.
Fine-Tuning Models
Domain-tuned models that speak your language and outperform generic baselines on your specific tasks.
AI Strategy
Where AI fits, what to build first, and how to do it responsibly, with a clear roadmap from opportunity to production.
Governance, by default
Policy, audit trails, and evaluation run through everything above, so the AI we ship stays safe, compliant, and explainable.
Across the stack
AI is strongest when it sits on solid data and ships as real product.
Bring intelligence into your workflow.
Tell us where a decision or task is slow, manual, or error-prone. We'll show you where an agent fits.