↘ What we offer
Modernization, AI implementation, data infrastructure, and operations — delivered by senior engineers who stay until the system runs.
Booking projects — Q4 2026
01
Modernize critical systems without unnecessary disruption — using AI as an engineering aid, not a silver bullet.
Includes
Dependencies, risk, and what is safe to change.
Codebase documentation and characterization.
Refactoring, APIs, and migration in controlled steps.
Technical ownership your team can run.
02
Move promising AI initiatives from experimentation into practical, measurable deployment.
Includes
Use cases, data readiness, and success criteria.
Architecture, integrations, and sequencing.
Prove the path before full production spend.
Ship, measure, and hand over with documentation.
03
Make enterprise information usable, accessible, and dependable for AI systems.
Includes
Quality gaps, sources, and access requirements.
Documents, extraction, and transformation.
Search, reranking, and source attribution.
Lineage, controls, and operational ownership.
04
Reduce manual processing and operational friction through intelligent engineering.
Includes
Volumes, exceptions, and friction points.
Classification, extraction, and reconciliation.
Wire into the tools operators already use.
Approval paths where judgment still matters.
05
Improve forecasting and operational decisions using historical data and validated predictive models.
Includes
Historical data assessed before modeling.
Forecasting, classification, anomaly detection.
Prove performance against representative data.
Models in the decision path your team runs.
06
Measure AI quality, improve reliability, and establish controls for responsible deployment.
Includes
Quality criteria and failure modes that matter.
Benchmarks that catch drift before release.
Model and data lineage for auditability.
Controls leadership can see before go-live.
07
Improve the speed, cost, and reliability of AI workloads.
Includes
Latency, cost, and throughput as they stand.
Bottlenecks in serving and infrastructure.
Batching, caching, quantization where appropriate.
Before/after measured — not promised early.
08
Apply visual intelligence to inspection, identification, and industrial processes.
Includes
Visual task, data quality, and success criteria.
Inspection, identification, defect detection.
Results against representative operational data.
Wire vision into the workflows already running.
The approach
How we work →01 — Small teams
Two to four senior AI and systems engineers per engagement. Partners lead the work. No farming out to people you'll never meet.
02 — Short loops
Engagements run long enough to do real work and short enough that nobody loses momentum — measured against production outcomes.
03 — Outputs, not artefacts
We ship systems — models, pipelines, workflows, handover. Decks are a means to an end. Usually a short one.
Most AI engineering engagements are scoped and fixed. Open-ended retainers incentivise the wrong things.
Sprint
Two to four weeks. A single defined output — a feasibility assessment, a modernization plan, a validated pilot path. No bloat, no extensions.
Engagement
Six to twelve weeks. Multi-practice work — modernization and data, implementation and governance, vision and operations. Where the real complexity lives.
Advisory
Monthly structured sessions with a partner. For teams that need consistent AI engineering input without a full build. Capped capacity.
Get in touch
We answer within a working day. Most AI engineering engagements start once scope, data, and constraints are clear.