Capability
AI and software development
Squads that own an outcome rather than a task queue. Each engagement has a named delivery lead, a written definition of done, and a measurement approach agreed before build starts.
Application development
Web, backend, mobile and integration engineering. Squads work in your repositories against your review standards.
Data and platform engineering
Pipelines, warehousing, streaming and analytics. Built so the reporting layer does not depend on one person.
Applied AI
Retrieval systems, agent architectures, evaluation harnesses, guardrails and the deployment pipeline behind them.
Applied AI
How we approach AI work
A demonstration is straightforward to produce and tells you very little. Our sequence is designed so that the decision to continue is made on evidence.
Define the measurement first
Before any model is selected we agree what good looks like and how it will be measured. If a task cannot be evaluated, we say so rather than building something that cannot be defended later.
Build an evaluation set from your data
A representative set of cases with expected outcomes, produced with your subject-matter experts. This becomes the regression suite for every later change.
Establish a baseline
The simplest approach that could work is measured first. A significant proportion of requests are met without a model at all, and that result is reported honestly.
Iterate on retrieval and prompting before fine-tuning
Most quality gains come from retrieval design, context construction and evaluation discipline. Fine-tuning is proposed only when the measurements show it is required.
Ship with guardrails and observability
Input and output validation, refusal handling, cost and latency monitoring, and logging designed for later review, all in place at launch rather than added afterwards.
Operate and re-measure
Model versions change, data drifts and usage patterns move. The evaluation suite runs on a schedule and the results go into the monthly service review.
Bring the problem, not the specification.
Most useful conversations start with what the business needs to change, not with a technology choice already made.