Your processes, redesigned for agents
Agents that take the manual work out of one workflow, tested on your data against a target agreed before the build. The code and decision logic are yours by contract.
Three-agent product advisor guiding customers through print-order configuration.
Read case study
HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
Read case study
Text-to-workflow agents building validated node graphs inside the platform.
Read case study
Supply chain intelligence agents reducing manual coordination overhead.
AI agent implementation for a global automotive enterprise.
Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
We redesign the process, prove it on your data, and you decide go or no-go on measured results.
Where it stallsThe old workflow gets an agent on top.
What we do We redesign the work first: fixed rules stay in code, agents take the judgment calls, people keep the decisions that matter.
Where it stallsThe pilot ends with a demo and no decision.
What we do A Proof of Value on your data ends in go or no-go, with measured accuracy and running costs.
Where it stallsYour logic ends up locked in a vendor's platform.
What we do You own the code, data and decision logic by contract, and the model stays swappable.
One team, from the first workshop to a system your people run.
We map the process, cut the steps that only move data, keep fixed rules as code and keep people where judgment matters.
Mixam's print-order agent lifted conversion to a final sale from about 20% to about 40%. Agents where the work needs judgment, code where it must be exact. Our engineers contribute to the open standards agents run on, so your system keeps up as they change.
44% for a raw LLM, 98% for the guideline-executing agent, on an open 50-scenario clinical benchmark. The agent runs in your environment on hosted or open-weight models, and the model stays replaceable.
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.
We choose the architecture in the first phase, test it on your data and write down why the alternatives lost.
Fits whenOne bounded workflow, clear inputs, a handful of tools.
Watch outIt hits a ceiling when the workflow keeps branching into new cases.
Fits whenToo broad for one agent, splits into specialist steps.
Watch outMore moving parts to test, monitor and pay for on every run.
Fits whenAnswers must come from your documents, with sources shown.
Watch outOnly as good as the documents behind it and the permissions on them.
Fits whenPeople ask in chat, and some of them need a person.
Watch outNeeds a clear handoff and firm limits on what it may promise.
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.
Chosen per project, with the model kept replaceable.
A Proof of Value shows accuracy and running costs before the full build.
Ten AI agent development companies for business automation, ranked on delivered agent projects, team credentials, services and thought leadership.
One agent or several? Deterministic orchestration beats both when the flow is predictable. Pros, cons and the architectures, with production examples.
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.