From the first honest assessment to something that runs on Monday
Most AI work fails between the strategy and the system. These services are arranged along that path, and each one can be bought on its own.
AI Strategy
Which decisions are worth changing, what each would cost, and what would measurably be different. Including the ones where the answer is not AI.
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AI Transformation
The part after the strategy: sequencing against real capacity, changing how work is done, and making adoption somebody's job rather than a hope.
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AI Agents Engineering
Agents that do work rather than answer questions — with bounds, tools, approval gates and a decision log that survives the first incident.
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AI-Native Apps Design
Designing software around a component that is fast, useful and sometimes wrong — which is a different design problem from anything before it.
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AI-Native Apps Engineering
Turning something that works in a notebook into software your people can depend on — evaluation, retrieval, cost control and everything a prototype skipped.
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Ontology Design and Engineering
One agreed model of your business — entities, relationships and rules — implemented so software and agents can reason over it instead of guessing.
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AI Data Engineering
The pipelines, quality checks and lineage that decide whether an AI system is trustworthy — usually the difference between a good demo and a good system.
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AI Workshops and Training
Sessions built around your processes and your data. People leave with a decision or a working thing, not with notes.
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AI Managed Services
Running what has been built. Monitoring designed for systems that go wrong without an error, response times in writing, and a support tier meant to shrink.
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Field Deployment AI Engineering
AI that works on a shop floor, in a vehicle or on a site — offline-tolerant, on hardware that survives the environment, usable by people not at a desk.
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AI Agents Spec-Driven Development
A development method where the specification is the artefact under version control and agents generate the implementation against it, with review where it belongs.
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AI Dark Factory Context Development
The machine-readable context an unattended operation needs before it can run: state, constraints, exception handling and provenance for every decision.
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