How we deliver
What we deliver
- Custom Model Context Protocol (MCP) servers that expose your tools, data, and internal systems to agents under explicit, auditable permissions.
- Retrieval-augmented generation (RAG) pipelines with vector search, re-ranking, and grounded citations so answers trace back to your sources.
- Multi-step agent workflows with tool-calling, guardrails, and human-in-the-loop checkpoints for high-stakes actions.
- Evaluation harnesses and regression suites that score accuracy, hallucination, latency, and cost on every change.
- Model-agnostic abstraction so you can route between providers and self-hosted models without rewriting your application.
- Observability for prompts, tokens, traces, and spend, so you always know what your AI is doing and what it costs.
How we work
- 01
Map the surface
We audit your data sources, internal tools, and the decisions you want AI to support, then define exactly what an agent is allowed to see and do.
- 02
Ground the model
We build the retrieval and MCP layer so the model reasons over your real data with citations — not its training set — and fails safely when it is unsure.
- 03
Add agency, carefully
We introduce tool-calling and multi-step workflows behind guardrails and approval gates, expanding autonomy only where evaluations prove it is safe.
- 04
Measure and harden
We wire up evals, tracing, and cost controls so quality is monitored continuously and every prompt change is tested before it ships.
Outcomes
AI features that act on your real data with citations, instead of confidently inventing answers.
A model-agnostic architecture you control, free from lock-in to any single provider.
Clear visibility into accuracy, latency, and spend, so you can scale AI with confidence rather than hope.
FAQ
We ground every answer in your own data through retrieval and Model Context Protocol servers, return citations so responses are traceable, and build evaluation suites that flag hallucinations before changes reach production. The model is constrained to reason over verified sources and to defer when it lacks them, rather than guess.
No. We build a model-agnostic abstraction layer so your application is decoupled from any single provider. You can route between hosted models and self-hosted open-weight models, switch as pricing and capabilities change, and keep sensitive workloads in your own environment without rewriting your product.
Yes. We keep your data inside infrastructure you control, scope every agent's access through explicit, auditable permissions, and apply the same encryption and access controls we use across our security work. Nothing is sent to a model provider for training, and you keep a full trace of what was retrieved and why.
AI Infrastructure
One senior team, end to end. Tell us what you're building and we'll architect the path to ship it.