What we build.
Six practices, composed deliberately. We rarely engage on a single one — most engagements traverse three or four.
C-01
Intelligent Software Systems
Bespoke product engineering for systems with high integrity demands.
We design and build software for environments where correctness is not a feature but a precondition — financial controls, regulated workflows, operational cores. The work is patient, deeply structured, and produces systems that age well.
Method
- Eight-week structured discovery before the first line of code.
- Domain-driven decomposition with named, owned contracts.
- Property-based testing and observability designed in, never bolted on.
- A staged delivery rhythm tuned for the client's appetite for risk.
Typical artefacts
- Domain map and contract registry
- Architectural decision records
- Production-grade telemetry and SLOs
Related work
C-02
Applied AI & Inference Engineering
Production-grade language and reasoning systems built to be observed.
AI in production is a discipline distinct from AI in a notebook. Our work focuses on the surrounding system — retrieval, evaluation, observability, governance — that converts capable models into systems an organisation can trust.
Method
- Evaluation suites authored alongside domain experts before model selection.
- Retrieval architectures combining semantic, lexical, and structured signals.
- Prompt and tool registries with versioning, rollback, and audit.
- Model-agnostic platforms — frontier models are interchangeable, the system is not.
Typical artefacts
- Continuously running evaluation harness
- Citation-bearing answer surfaces
- Audit trail satisfying regulatory review
Related work
C-03
Enterprise Architecture
Untangling and re-composing complex internal estates.
Most large organisations are quietly held together by the people who remember why something was wired the way it was. We work to make the architecture legible — to humans first, and then, by extension, to the machines that increasingly mediate it.
Method
- Existing-state mapping conducted as observation, not interview.
- Domain seams identified and named in collaboration with leadership.
- Migration plans staged so each step is independently valuable.
- Knowledge transfer treated as an engineering deliverable.
Typical artefacts
- Annotated current-state map
- Target-state architecture with rationale
- Migration plan with named owners
Related work
C-04
Cloud Infrastructure & Platforms
Internal platforms that give engineering teams leverage.
We build the substrate engineering organisations stand on — provisioning, observability, deployment, governance — designed as a coherent product rather than an accumulation of patterns.
Method
- Platform-as-product discipline: a roadmap, named users, and adoption metrics.
- Golden paths that are pleasant enough that teams choose them.
- Cost, security, and compliance modelled into the platform from day one.
- Operational runbooks treated as code.
Typical artefacts
- Platform self-service surfaces
- Tier-zero observability and alerting
- Cost-aware deployment pipelines
Related work
C-05
Automation & Operational Systems
The connective tissue between teams, software, and the real world.
Workflow engines, control planes, and machine-assisted operations — the systems that take the texture of how work actually happens and make it durable. Designed to support human judgement rather than displace it.
Method
- Long observation of how the work is done before it is encoded.
- Decisions treated as data; reasoning chains preserved and inspectable.
- Override pathways treated as first-class — every automation is reversible.
- Continuous calibration against operator-reported confidence.
Typical artefacts
- Reasoning runtime with decision provenance
- Operator-tuned interfaces
- Continuous improvement instrumentation
Related work
C-06
Data Systems & Pipelines
Data infrastructure designed to be trusted.
Lineage, quality, and semantic clarity treated as first-order concerns. We build data systems where the meaning of the data is visible alongside the data itself — and where every column has a name, an owner, and a contract.
Method
- Semantic models authored before pipelines are written.
- Column-level lineage and ownership tracked in code.
- Quality and freshness expressed as policy, not folklore.
- Analyst-grade interfaces decoupled from storage-grade plumbing.
Typical artefacts
- Typed semantic layer with metric registry
- Lineage and quality observability
- Policy-aware access controls
Related work
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