Systems that survive production.
We work as an embedded engineering team—across architecture, evaluation, infrastructure, and interface—to build systems that deliver measurable value in the real world.
Technical index
Hover or tap an entry. Every line is something we build, run or hire for.
Applied AI strategy, prototypes and production systems that earn their keep.
The measurement layer
under the model.
Proprietary eval datasets, RL environments, scoring systems, data pipelines, failure taxonomies and benchmarks — built across clients, so every system we ship can be proven rather than demoed.
Proprietary eval datasets
Task-specific ground truth we own and version.
RL environments
Simulated settings to train and stress agent behaviour.
Scoring systems
Deterministic and model-graded scoring you can trust.
Data pipelines
Ingestion, labelling and refresh loops that keep evals honest.
Failure taxonomies
Named, reproducible failure classes instead of vibes.
Benchmarks across clients
Comparable baselines that compound with every engagement.
How the
work moves.
- 01
Understand
Business problem, users, data and technical environment.
- 02
Architect
System architecture, AI strategy, infrastructure and product design.
- 03
Build
Rapid engineering, experimentation and iteration.
- 04
Deploy
Production infrastructure, monitoring, evaluation and security.
- 05
Scale
Performance, reliability, additional capabilities and engineering talent.
The stack we work in
Tools are chosen per problem, not per trend. This is the ground we cover across AI engineering, data platforms, cloud infrastructure and enterprise systems.