AI & Machine Learning
The difficulty with AI in an enterprise is rarely the model. It is everything around it: which decisions the system is allowed to make, how you know it is still working a month from now, and what happens when it is confidently wrong. We build AI capabilities that are evaluated, bounded, and integrated into the workflows people already use.
What this covers
LLM application design and retrieval
We design applications around language models, including retrieval-augmented generation over your own content. That means the retrieval layer as a first-class concern — chunking, indexing, ranking, and permission-aware filtering so a model never surfaces a document the person asking was not entitled to see. Retrieval quality, not prompt wording, is usually what separates a demo from something people trust.
Evaluation harnesses and model selection
Before choosing a model we build the harness that tells you whether a change helped. That means a representative test set drawn from your real inputs, graded against criteria you actually care about, and run automatically on every change. It turns model and prompt selection into a measurement rather than an argument, and it makes provider migration a decision you can evaluate rather than fear.
Guardrails and failure behavior
We define what the system does at its edges: input validation, output constraints, refusal and escalation paths, and the human review step for consequential actions. We also design for the failure everyone forgets — the plausible, well-formatted, wrong answer — with confidence signals, citation to source material, and an audit trail of what the model was given and what it produced.
MLOps and production monitoring
For custom models we build the pipeline around them: reproducible training, a model registry with lineage, staged rollout, and monitoring for input drift and output quality after deployment. A model that was accurate at launch and unmonitored for six months is an unmeasured risk, and the drift is usually gradual enough that nobody notices until it matters.
How the work runs
We begin with the decision the system is meant to support and work backwards. That establishes what accuracy actually needs to be, what an error costs, and whether the problem warrants a model at all — a meaningful share of proposed AI work is better served by a query, a rule, or fixing the data underneath.
We then build the evaluation before the feature. Having a graded test set in place first means every subsequent choice — model, retrieval strategy, prompt, threshold — is settled with evidence, and the system can be improved by someone who was not in the room when it was built.
What you get
- A working AI capability integrated into an existing workflow, not a standalone demo
- An automated evaluation suite that gates changes to prompts and models
- Documented guardrails, escalation paths, and human review points
- Cost and latency measured per request, with the levers to change both
- Monitoring for drift and output quality after launch
Who this is for
- Teams with a promising AI prototype that has stalled short of production
- Organizations that need AI decisions to be auditable and permission-aware
- Groups running custom models without a reproducible training pipeline
Talk through your ai & machine learning work
Tell us what you are running today and what is not working. We will tell you whether this is the right engagement, or point you at the one that is.
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