What we do
Three outcomes, built into every system
We design and build AI systems that stay under your control, work with any model, and show what they cost and deliver. Each section starts with what the outcome means for the business, then how we build it, then what you can check for yourself.
Under control
Your AI acts only within the limits you set. Each agent reaches only the tools and data it was given, a person approves the actions that matter, and every action leaves a record. When a customer, an auditor or a regulator asks how your AI is controlled, in any jurisdiction, you can show them. We treat the EU AI Act, ISO/IEC 42001 and the NIST AI RMF as design inputs and read them into specific decisions in the system.
How we build it
Agent boundaries: per-tool permission scopes, human-in-the-loop approval policies for consequential actions, per-tenant data isolation and retrieval scoping, and audit logging of what each agent did and who approved it.
Security designed in and tested before launch: threat modelling, data pipeline and retrieval hardening, and boundary controls between agents, tools and data. Agents are tested for misuse: tool chain exploitation, trust built up over several steps, prompt injection, data poisoning and exfiltration. We created and maintain Ziran, our open-source tool for agent security testing.
Regulation read as engineering: the rules and standards that apply to you, mapped to the controls in the system. For example, risk classification under Article 6 of the EU AI Act, conformity assessment routes and Annex IV technical documentation, ISO/IEC 42001 AI management systems, and the NIST AI RMF.
Full tool list: Under control
Methods
Testing tools
Frameworks
Regulation and standards
What you can check
Ask for any agent's permission scope and the log of who approved what. Read the security test results and the findings we closed. Ziran, the tool we test agents with, is open source, so you can read exactly what it tests.
Yours to own
We have no partnership or reseller arrangement with any model vendor, cloud or platform, so the architecture is designed around your needs. You own the architecture, the code and the knowledge behind it. Your team can run and change the system without us, and when a better or cheaper model appears, it can be swapped in.
How we build it
One reference architecture and one set of standards for every team that builds AI. Patterns for retrieval over company data, tool use, multi-agent orchestration and model serving in the cloud and at the edge, with MLOps and LLMOps standards.
Models sit behind interfaces the system controls, so a model can be replaced and re-evaluated without rebuilding what is around it. Agents connect through open protocols: MCP for access to tools and data, A2A for agents working across systems.
Decisions are written down as architecture decision records, with C4 diagrams where useful. Build-vs-buy and platform choices come with the trade-offs written down. Code is written in your repository, in commits your team reviews, and handed over with documentation and runbooks.
Full tool list: Yours to own
Architecture methods
Open protocols
AI and agent frameworks
Model platforms
Cloud and infrastructure
Data and vector stores
Backend and APIs
Languages
What you can check
The code and the decision records live in your repository. Ask your engineers to run the system from the runbooks, or to swap the model in a test environment and compare the results.
Accountable value
Every AI system should be able to say what it costs and what it returns. We agree the measures with you before the build, in terms the business already uses, and track them from the first release. Decisions to extend, change or stop a system can then rest on evidence.
How we build it
Cost mapped per system: what each one costs to run, where the spend goes, and which model, team or use case drives it.
Evaluation suites with baseline results, set before launch, so every change of model, prompt or tool is compared against the same bar. Reliability targets with budgets per stage, and acceptance bars agreed before launch.
Observability and evaluation with tools such as MLflow, Langfuse and LangSmith, chosen to fit what you already run.
Full tool list: Accountable value
Measures
MLOps and evaluation
What you can check
Each review sets out what each system costs to run, as far as your billing data shows it.
Tools we work with. We choose per system; none is a requirement.
Start with a 30-minute call
Tell us what AI you run and where it is heading. We will tell you which of the three outcomes needs work first.
Book a 30-minute call