Specialized servicesAI, data, and statistics for decisions that need to work.
We step in when the problem demands more than an off-the-shelf tool: rigorous framing, fit-for-purpose engineering, reproducible evaluation, and a system your team can operate.
Four practices. One delivery discipline.
Projects often span more than one practice. An agent may need modeling, a statistical analysis may become a product, and any AI system may need evaluation before production.
Statistical consulting
Study planning, inference, testing, survival analysis, observational studies, and quantitative support for research and business.
02AI agent development
Agents with tools, RAG, memory, human approval, observability, and quality criteria for real-world tasks.
03Predictive modeling
Forecasting, classification, risk, time series, and machine learning validated against the decision they support.
04AI evaluation and governance
Evals, guardrails, red teaming, observability, and release gates for quality, safety, cost, and latency.
From problem to operation.
The format changes with project maturity, while the essential decisions remain traceable.
Frame
Objective, decision, users, available data, constraints, and success criteria.
Prove
A prototype or initial analysis tests the critical assumption before investment grows.
Build
Implementation, validation, documentation, controls, and integration with the existing workflow.
Transfer
Reproducible delivery, team enablement, and an explicit plan for monitoring and evolution.
Before we start
Do you only work with generative AI?
No. Our work also covers statistics, predictive modeling, data engineering, experimentation, and conventional software systems.
Can we begin with a short assessment?
Yes. A focused assessment can clarify feasibility, risks, missing data, and the smallest useful next step before a larger engagement.
Do you deliver code and documentation?
Yes, whenever the engagement includes implementation. Deliverables, ownership, environments, and handover are agreed in the scope.
Do you work with both research teams and companies?
Yes. The process adapts to the context while preserving methodological rigor, traceability, and operational clarity.
Bring us the difficult decision.
Share the context, available data, and what needs to change. Our first response will address feasibility and the next step, not offer a generic proposal.