Most AI pilots stall between the demo and the production rollout. The model works on a curated sample, then meets real documents, real users and real audit questions. We build for that second environment from the first sprint: evaluation sets, guardrails, monitoring and a named owner for every model.
Our teams pair ML engineers with data and platform engineers, so retrieval pipelines, feature stores and deployment are designed together. You own the code, the prompts, the evaluation sets and the trained models, and your data is never used to train models for anyone else.
GenAI Applications & Agents
We build assistants and agents that read, draft and act inside your systems, with tool permissions, human review steps and audit logs. You get a working application, its prompt library and the evaluation harness behind it.
Retrieval & Knowledge Systems
We index policies, contracts and case files into retrieval pipelines with chunking, access controls and citations tuned to your documents. Answers point back to source pages, and retrieval quality is measured against a golden question set.
Forecasting & Risk Models
We build demand, credit and fraud models on your historical data, with backtests your risk team can read. Each model ships with a feature pipeline, a scoring service and documentation that survives model validation review.
MLOps & Model Monitoring
We set up training pipelines, a model registry and deployment that runs through CI like any other service. Drift, latency and cost are tracked on dashboards, with alerts and retraining runbooks your team can act on.
Model Governance & Evaluation
We write model cards, risk assessments and approval workflows that fit your existing controls. Every release is scored against an evaluation set for accuracy, bias and harmful output before it is promoted.
Howanengagementruns
A typical AI & Machine Learning engagement runs about 20 weeks from kick-off to a working, handed-over system.
1
Use-case and data audit
We shortlist use cases by value and data readiness, sample the real documents and records, and agree the success measures each one must hit.
2
Evaluation set and baseline
We build a golden dataset with your domain experts and score a simple baseline against it, so every later change is measured, not guessed.
3
Model and pipeline build
In two-week sprints we build the retrieval pipeline, prompts or models, guardrails and integrations, scoring each sprint's release against the evaluation set.
4
Shadow run and rollout
The system runs alongside the current process before go-live. We tune thresholds on live traffic, hand over monitoring dashboards and runbooks, and roll out in stages.
