AI systems and automation
Agent systems, MCP servers and LLM infrastructure built for production, where outages, cost and latency are engineering problems rather than prompting ones.
The gap we work in
You probably already have something working. A prompt that produces good output. A prototype that demos well. A notebook somebody runs by hand on Tuesdays.
Then it meets production. Your provider has an outage and your feature is simply gone. Costs triple in a week because usage shifted and nothing capped it. Latency swings from 400ms to nine seconds and users stop waiting. The same three responses keep coming back. Somebody finds your endpoint and hammers it. And when finance asks what the feature costs per user, nobody in the room knows.
None of that is a prompting problem. That gap is where we work.
What we build
Agent systems
Multi-agent orchestration, tool calling, state and memory, review-gate loops that iterate output against a quality bar before returning it.
MCP servers
Your systems and APIs exposed as native tools for AI agents, with scoped authentication and streaming. We have built these for operations where mistakes are expensive, including guardrails that block high-value transactions without explicit confirmation.
LLM infrastructure
Multi-provider integration with automatic failover, response caching, rate limiting, token streaming, and cost and latency monitoring.
RAG and search
Vector databases, retrieval pipelines, embedding strategy.
AI workflow automation
Where a process has a defined start, defined steps and a defined end, we automate it and layer AI on top so the workflow adapts to context rather than following a fixed path.
AI-assisted engineering
Agent tooling inside your delivery pipeline, automated review, convention enforcement, safety guardrails.
How we know this works
We run it ourselves.
Our own publishing runs on an internal agentic pipeline that ingests a source, drafts platform-specific variants, schedules them and posts via API. Automated review runs against our own repositories. On our platform work, agent tooling handles parallel multi-chain deployments and long-running cryptographic ceremonies, behind hooks that block dangerous operations without explicit confirmation.
When we specify an agent system, we are describing something we operate.
Proof
Internal AI tooling
INTERNALAgent systems we built and run
Snark
OPEN SOURCEA self-hostable LLM API, and a working reference for production LLM infrastructure
Questions
- We already have something working. Do we need to start over?
- Usually not. Most of what we do is putting reliability, cost control and observability around something that already produces good output.
- Do you train or fine-tune models?
- No. We do not do production ML, MLOps, recommenders or forecasting. If your project needs those, we will say so.
- Which model providers do you work with?
- Claude, Gemini, Groq and OpenAI. We usually build multi-provider with automatic failover, so you are not exposed to one vendor's outage.
- What is an MCP server and why would we want one?
- It exposes your systems as native tools an AI agent can call directly, instead of the agent guessing its way through your REST API. Useful when you want an agent doing real work rather than answering questions.