AI & Machine Learning
We build AI that has to hold up on a Tuesday afternoon with real inputs — measured against an agreed bar, wired into the workflow it serves, and monitored once it is live.
Evaluation first, then the model.
The teams that get AI into production are the ones who defined what correct means before they started. We build the evaluation set first, then the simplest system that clears the bar, and add complexity only where the numbers say it is needed.
Capabilities
- Generative AI & LLMs
- ML models & pipelines
- AI automation
- Applied research
The stack behind this service.
Python
PyTorch
TensorFlowFrom evaluation set to a monitored system.
Define the task
The judgement being made, how often, and the cost of getting it wrong — which decides whether AI fits at all.
Build the evaluation
A labelled set and an acceptance bar, agreed before development, so progress is measurable.
Baseline simply
Rules, retrieval or a general model first. Many problems stop here, and that is a good outcome.
Improve where measured
Better retrieval, prompting or fine-tuning — each change kept only if the evaluation improves.
Wire into the workflow
Delivered into the tool where the decision is made, with a human path for low-confidence cases.
Monitor for drift
Continuous evaluation and output logging, because quality decays quietly rather than failing loudly.
What you get.
A defensible quality bar
Changes judged against an evaluation set rather than against impressions.
Answers you can trace
Grounded in retrieved sources, so an output can be checked instead of trusted.
Failure handled by design
Confidence thresholds, human review and an off switch built in from the start.
Common questions.
Rarely at the start. Retrieval over your own content plus a general model solves a large share of business problems. Training or fine-tuning earns its cost when you have proprietary data and an evaluation showing the simpler approach falling short.
You reduce it and contain it. Grounding in retrieved sources, constrained output formats, confidence thresholds and human review where being wrong is costly. Anyone promising to eliminate it is overselling, and we would rather set the expectation correctly.
Yes, with trade-offs. Self-hosted open models and private endpoints both work; you generally give up some capability relative to the largest hosted models, and infrastructure cost goes up. We size that honestly before you commit.
The evaluation set runs continuously and every output is logged, so drift shows up on a dashboard rather than in a complaint. Models change, inputs change, and a system with no monitoring is one you have stopped being able to vouch for.
Ready to start with AI & machine learning?
Tell us where you want to go. We’ll help you get there with the right technology, delivered by a team that ships.