Each engagement composes from three primitives (automation, measurement, and decision frameworks) applied to the question on the table. Below, each capability set out in detail, then the applied-economics disciplines drawn on across all three, then the architecture that connects them.
Agents that authenticate to your operating systems, pull data on a schedule, normalize it into a single clean model, and produce the recurring reports a team is currently rebuilding by hand.
Accounting, CRM, point-of-sale, payroll, marketing analytics, and project-management systems, connected through their official APIs and read on a schedule.
Source-system extracts are reconciled, de-duplicated, and rolled up into a single operating data model. The same numbers flow consistently through every downstream report.
Dashboards, calibrated anomaly alerts, and a weekly LLM-generated executive summary that flags what shifted, what looks routine, and what deserves attention before the next operating review.
When a price moves, a market opens, or an operating process is redesigned, isolate what the decision caused, separated from the conditions that surrounded it. The basis for knowing whether a change worked.
Most operating dashboards report what *moved together*. Causal measurement reports what was *driven by the decision itself*, separated from market drift, seasonality, and concurrent shifts the business did not control.
The methodology is the one used in peer-reviewed economics to measure policy effects. The vocabulary stays in our notebooks. The read for a leadership team is in business-grade terms. The pricing change drove revenue by X, separated from the seasonal lift the brand would have earned anyway.
Difference-in-differences Event studies Synthetic control Callaway–Sant'Anna IPW
KPI architecture for the specific business model. Financing and expansion analysis. Labor and team-composition decisions. Opportunity-cost framing. The scenario work that translates dashboard readings into decisions worth making.
From first principles, not a templated dashboard. Built so what is measured is what actually shapes the operating result.
When a financing decision is on the table, evaluated against the cost of capital and the alternatives, with the upside and downside priced explicitly.
Headcount, training spend, and compensation structure read against the marginal output they actually produce. The labor-economics lens, applied at operating speed.
Every operating choice is read against the alternative it displaces, not in the abstract, but priced against the specific opportunities the business is currently passing on.
Decisions worth making rarely depend on one number. Scenario and sensitivity work make the dependence visible. Which inputs matter, which do not, and where the decision flips.
Causal inference is one strand among five. The disciplines below are used together (selectively, depending on the question) across every engagement.
Difference-in-differences Event studies Synthetic control Callaway–Sant'Anna IPW NPV / IRR Marginal & sensitivity analysis Real-options valuation
KPI infrastructure is not one thing. It is the definition layer (what to measure) sitting on top of the plumbing layer that moves the data, sitting on top of the display layer that surfaces it. The economist's value-add lives at the top. The AI agents take care of the bottom two.
The right KPIs for the specific business model, anchored in economic theory, not borrowed from a generic dashboard template. Economist judgment. Not automatable.
Authenticated agents pull from source systems and normalize everything into a clean operating data model. Runs on a schedule; flagged when it breaks.
Dashboards, weekly anomaly reports, and quarterly reviews. Not a wall of charts. The reading is short, the alerts are calibrated, the decisions are surfaced first.
Every engagement begins with a question, usually one sentence. We respond with the engagement format that fits, the scope, the fee, and a written read of the question. Usually within a week.
