How we work, step by step.

We sit with the team that will use the system and the team that owns the data feeding it. The goal is one sentence: what decision does this support, and what number tells us if it's working.

• Interview stakeholders from the business side and the data team separately, then reconcile what each expects the system to do.
• Pull sample data extracts and check for the failure modes that kill projects later: survivorship bias, restated financials, missing point-in-time snapshots.
• Define the metric that decides success, and the threshold below which we recommend stopping.
• Write the one-page scope and get sign-off from the budget owner and compliance before architecture work starts.

We design the data layer before the model: where data comes from, how point-in-time correctness is enforced, what the model can and can't see, and where a human has to sign off.

• Map every data source to its owner, refresh cadence, and licensing constraints.
• Design the point-in-time data layer so a backtest can never see information that wasn't available on the date in question.
• Decide where a human approval gate sits in the workflow, and what triggers it.
• Run a go/no-go architecture review with your engineering and compliance leads before a line of production code is written.

We build against real data with evaluation running from day one, not bolted on at the end. The pilot has to beat the current process on the metric agreed in discovery, or we say so plainly.

• Build the first working version against real data, not a synthetic sample.
• Stand up the evaluation harness before the model, so every version gets scored the same way from day one.
• Run the pilot's output against the current process side by side, on the metric fixed in discovery.
• Deliver an honest recommendation, proceed, iterate, or stop, backed by the evaluation report.

We harden the pilot: monitoring for drift and cost, an audit trail, sign-off steps where mistakes are expensive, and integration into the systems your analysts already use.

• Add monitoring for model drift, data quality breaks, and per-query cost, with rollback rehearsed before go-live.
• Build the audit trail: every output traceable back to its inputs and the model version that produced it.
• Wire sign-off steps into the workflow wherever a mistake would be expensive.
• Integrate into the tools your analysts already have open, not a new tab they have to remember to check.

We document the system, hand over the evaluation suite and runbooks, and spend time with the people who'll own it, so it doesn't quietly become tribal knowledge only we have.

• Document the system: architecture, data lineage, and the reasoning behind each guardrail.
• Hand over the evaluation suite so your team can rerun it after every model or data change.
• Run working sessions with the people who'll own the system, rather than a farewell demo.
• Agree a defined support window, after which the dependency on us formally ends.