We combine deep statistical expertise with current regulatory knowledge to help organizations adopt AI responsibly — and prove it to regulators, boards, and customers. The same rigor powers our data science and predictive analytics work.
For organizations deploying AI in regulated or high-stakes settings.
In plain terms, this work answers one question: can you show that your AI is used responsibly? We start by taking inventory of the AI systems you actually use and who owns each, then classify which are high-stakes. From there we write the policies that govern them, test the consequential ones for unfair outcomes, and assemble documentation an outsider — a regulator, a board, a customer — could review with confidence.
Illustrative engagement: a company using an AI tool to screen job applicants needs to show it isn't disadvantaging any group. A typical engagement inventories the tool, runs an independent bias audit comparing selection rates across groups, and produces a written summary suitable for publication — turning an anxious unknown into a documented, defensible position.
You receive: an AI system inventory and risk classification, written governance policies, audit and readiness reports, and training for the people involved.
For teams relying on models for decisions — lenders, insurers, healthcare, operations.
A model can look fine and still be quietly wrong — trained on data that has since shifted, or confident in situations it was never tested on. Validation is independent review: someone who didn't build the model checks its assumptions, tests it on data it hasn't seen, looks for biased or unstable behavior, and confirms it still performs on recent data. The plain-language goal is to know not just what a model predicts, but how much to trust it — and to have that written down.
Illustrative engagement: a lender's credit model was built years ago by a small internal team. A typical validation re-tests it on fresh data, checks whether its accuracy has drifted, examines it for unfair impact across groups, and gathers the scattered knowledge into one documented record an examiner could follow — usually finding the model is sound but its evidence was missing.
You receive: an independent validation report, a consolidated model-documentation package, fairness and explainability findings, and a monitoring plan to catch drift over time.
For organizations that want to turn data into decisions, with the same discipline we bring to governance.
Most useful analytics comes down to a simple question: what is associated with the outcome I care about, and how sure can I be? We take your data, identify which factors most relate to the result you're trying to move — sales, churn, cost — and build a model that turns those patterns into forecasts or rankings. Crucially, we report the uncertainty honestly: a range you can plan around, not a single false-precise number, and a clear line between a genuine signal and noise.
Illustrative engagement: a retailer wants to know what drives repeat purchases. A typical project examines the data, finds that (say) delivery speed and price are the strongest associated factors while region is too uncertain to call, and delivers a forecast plus a plain-language explanation — so the team knows where to act and how confident to be.
You receive: a model or forecast, a clear written explanation of the drivers and their uncertainty, and — where useful — a dashboard your team can keep using.
For leaders deciding where AI fits — and where it doesn't yet.
Strategy work is about choosing well before spending. The method is straightforward: list the places AI could plausibly help, then score each candidate on three things — the value if it works, how feasible it is with your data and systems, and the risk it carries. That turns a vague mandate to "do something with AI" into a ranked, honest shortlist, separating the ideas worth piloting now from the ones to wait on.
Illustrative engagement: a leadership team under pressure to adopt AI doesn't know where to start. A typical assessment inventories a dozen candidate use-cases, scores them on value, feasibility, and risk, and returns a prioritized roadmap — often recommending one or two low-risk pilots first rather than a single large bet.
You receive: a prioritized use-case shortlist, a roadmap with sequencing, and a clear view of what to pilot, what to defer, and why.
EU AI Act, NIST AI RMF, ISO 42001, NYC LL144, SR 11-7, and more.
How we think about applying these frameworks in practice.
A defined assessment with a written report and clear recommendations.
Scoped delivery — a validation, a framework build, or a roadmap.
Ongoing support, or a fractional "AI risk officer" role.