Intelligence thatsurvives production.
Most AI work is a demo. The difficult part is the ninety days afterwards — when the data is messier, the edge cases are stranger, and someone is accountable for the output.
The bar we hold ourselves to
Evaluated
A harness that runs on every change, before a human reads the output.
Bounded
Explicit permissions. The agent cannot reach what it was not given.
Observable
Traces you can replay, with the reason for every action recorded.
Reversible
A human can always stop it, and the system returns to a safe state.
Costed
Latency and spend budgets enforced in CI, not discovered in finance.
Think Deeply
We model the problem before we model the solution. Most expensive mistakes are made before a line of code is written.
Engineer Precisely
Explicit interfaces, typed boundaries, and errors that say what actually went wrong.
Move Quickly
Short cycles, real code in real environments, and decisions made on evidence rather than consensus.
Software that doesn'tjust run. It thinks,acts and adapts.
We engineer AI agents that understand goals, reason through tasks, use tools, interact with systems, and execute workflows.
- Bounded
- Explicit permissions, not best-effort.
- Verified
- Self-checks before work is declared done.
- Traced
- Every action logged and replayable.
- Reversible
- A human can always step in.
A goal, not a script.
Owns the objective end to end.
Decomposes the goal into solvable steps.
Chooses the shortest safe route.
Calls typed APIs with real contracts.
Acts inside explicit permission bounds.
Checks its own work before declaring done.
Traces reviewed; the loop tightens.
plan · 4 steps
tools.search(knowledge_base)
verify · confidence 0.94
escalate · approval required
The agent loop: User, then AI Agent, then Reason, then Plan, then Use Tools, then Execute, then Verify, then Learn / Improve, and repeat until the work is verified. Currently: User — A goal, not a script.
The stack we build on.
Chosen for the failure modes each one prevents — not for what is trending this quarter.
AI
01 / 06Model interfaces, embeddings, and the evaluation harness around them.
- LLM APIs
- RAG
- Evaluation harnesses
- Guardrails
- Vector search
View all categories
AI
LLM APIs · RAG · Evaluation harnesses · Guardrails · Vector search
Agent Frameworks
Tool use · Orchestration · MCP · Tracing · Memory
Languages
TypeScript · Python · Go · Rust · SQL
Frontend
React · Next.js · Tailwind · WebGL · Edge
Backend & Cloud
Node.js · PostgreSQL · Redis · Kubernetes · AWS / GCP
Automation
Workflow engines · Queues · Schedulers · Webhooks · RPA bridges
Research thatships.
A standing research practice, separate from client delivery. We explore what is genuinely new — multi-agent coordination, computer use, reasoning under evaluation — and bring only what survives into production work.
- tracks
- 08
- active
- 07
- research
- 01
- 01Active
Generative AI
Structured output, tool use, and evaluation harnesses that makes systems predictable enough to depend on.
- 02Active
AI Agents
Agent loops with real boundaries: explicit tools, explicit permissions, and an explicit stop condition.
- 03Active
Multi-Agent Systems
When separate agents beat one larger agent — and the coordination cost that comes with it.
- 04Active
Reasoning Systems
Task decomposition, self-critique, and verification loops for problems too structured for a single pass.
- 05Active
AI Automation
End-to-end process automation where the judgement step is learned rather than hard-coded.
- 06Research
Computer Use
Agents operating software that was never built with an API — and the safety envelope that requires.
- 07Active
Knowledge Systems
Retrieval that respects access control, lineage, and the difference between a fact and an opinion.
- 08Active
AI Infrastructure
Serving, caching, and cost control — the layer that decides whether AI is viable at volume.
Have a complex problem?Let’s turn it into an intelligent system.
Tell us what is not working. We will tell you honestly whether we are the right people for it.
Typical first response within one business day