Production AI. Built on thirty years of engineering.
Put AI to work across your documents, applications and multi-step tasks. We engineer the context, tools, permissions and recovery around each workflow, then test the completed work.
AI engineering since 2023 Built on experience of identity, integration and recovery.
Choose the capabilities that fit the job. Connect the right information and tools, define the permitted actions and check the result.
Controlled browser and desktop workflows for software without a suitable API.
Real-time conversation, visual context and tools connected to a useful workflow.
Search documents, diagrams, images and recordings with references to the source.
Specialist agents with clear handovers, tracked progress and responsibility for the result.
Defined ownership, scoped permissions and controls around execution.
Relevant context, saved progress and recovery for interrupted tasks.
Local, hosted and hybrid models with explicit data and network boundaries.
Investigate code, build tests and prepare changes with engineers in control.
Model choice, controls and delivery follow the requirements of your systems and the people using them.
See the reference architectureFor existing software, application modernisation starts with understanding its behaviour and dependencies.
Inspect the engineering
A useful conversation starts with something concrete. Explore an illustrative project, try the demonstrations and inspect the controls being tested.
Scope, information flows, permissions and acceptance criteria.
Read the blueprint →02 / DEMONSTRATEInputs. Checks. Decisions.Try synthetic examples with missing information and conflicting sources.
Explore the lab →03 / VERIFYResults you can inspect.Executed control tests, expected outcomes and explicit limits.
Open the test report →04 / OPERATEFollow the data.What reaches the model, tools and logs, and who decides.
Read the data guide →Thirty years of engineering experience helps us spot the dependencies around an AI system, from identity and infrastructure to applications and recovery. Start with the workflow, its test evidence or the way it is operated.
01 / Automate the work
Find information in approved documents, prepare records and reduce repeated handoffs, with people approving consequential changes.
Start with: one process, its inputs and agreed completion checks.
Explore workflow automation02 / Test the change
Measure completed tasks, answer quality, tool use, response time and cost. Repeat the checks when models, instructions, data or integrations change.
Start with: a prototype or live workflow and a representative set of test cases.
Explore evaluation and assurance03 / Operate the service
Define ownership, access, monitoring and change approval. Make it clear who responds to an incident and how interrupted work is handled.
Start with: a review of the service, its controls and operating responsibilities.
Explore operations and governanceHow we structure an AI agent that works inside an organisation. Each layer has a job, an owner and a limit.
Verify who or what initiated a request before granting access.
Planning, task state and model routing, with attributable identities and explicit authority.
Policy is enforced outside the model. Consequential actions wait for a person.
Each tool gets the narrowest credential that works.
Delegated and application access have explicit limits.
Keep a record of the request, actions and result.
Engineering notes
What to compare, what to record and what should hold a release.
KNOWLEDGE & EVIDENCEWhen the documents disagree.Keep authority, freshness and uncertainty visible in an answer.
RELIABILITYResume the work. Don’t repeat the action.Recover from an interruption without quietly creating a second change.
Bring a document process, a workflow or an AI system you want to improve. We agree the review boundary and the evidence needed to decide the next step.
What you get. A defined scope, prioritised risks and a practical plan for integration, testing and operation.
Talk to an engineer