LLM systems you can measure, afford and change
Change prompts and models without surprises in quality or cost. Every change is tested before release, rolled out in stages and costed per request.
Three-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
Production AI workloads moved to new hardware for on-prem LLM deployment.
AI agent implementation for a global automotive enterprise.
Text-to-workflow agents building validated node graphs inside the platform.
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HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
Read case studyA prompt edit can pass every health check and still weaken answers.
Where it stallsPrompt edits ship after spot checks.
What we do Evaluations run on every change first.
Where it stallsNobody can compare live with previous.
What we do Every release measured against the last.
Where it stallsToken costs surface on next month's invoice.
What we do Cost per request tracked per workflow.
From how changes reach users today to a release path your team runs.
We trace how a change reaches users today: who edits prompts, what gets tested, what it costs.
On the open 50-scenario benchmark, a raw LLM scored 44%; the guideline-executing system scored 98%. Each release passes an evaluation gate, ships to a slice of traffic first and is traced.
Every version is logged with scores and cost per request; cheaper models ship only when they pass.
Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.
Shaping agentic AI
The Linux Foundation formed it around Anthropic's MCP, OpenAI's AGENTS.md and Block's goose. AWS, Google and Microsoft are platinum members. In its own words: “Together, we're shaping the future of agentic AI.”
Open source
Framework contributors
We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.
Research
Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.
Track record
Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.
The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup.
2 hrsto prepare a workflow by hand
3 minto generate one with the agent
Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations.
~20% → ~40%conversion to a final sale
96%+web-to-print orders handled end to end
The model answers the assessment questions. The guideline decides the disposition, the same way every time.
44% → 98%accuracy, raw LLM vs guideline-executing
94%weighted F1 across 591 scenarios
Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.
We map one workflow with your experts and agree a success threshold before any build.
Best whenYou want the success test agreed before you commit to a build.
Bring one LLM system; we map how its changes reach users.
Why a single-run score overstates an AI agent's reliability, what to measure instead (pass^k, traces, judges) and how to set a target you can defend.
Agents that issue refunds need identity, authorisation, approval and audit before the call lands. What an agentic AI control plane is, and when you need one.
A “minimum 95% accuracy” promise can be worth nothing. An interactive walkthrough of precision, recall and what to ask before any number goes in writing.