Case study — Equipoise Solutions

The situation

A property-services firm priced complex back-office service deals by hand, across a sprawl of spreadsheets that only a few people fully understood. Quotes were slow, hard to reproduce, and easy to get wrong. They needed a tool that anyone on the team could use to price a deal consistently, and that leadership could actually trust.

What Equipoise did

Reverse-engineered the firm's existing pricing logic into a clear, documented specification.
Designed and directed the AI-enabled build of a pricing and financial-modeling application: portfolio inputs, staffing and cost models, margin and scenario analysis, and clean client-facing and internal outputs.
Iterated with the firm's stakeholders over many rounds as the model and the business questions sharpened.

The part most people skip: verification

This is where AI-enabled work earns trust or loses it. Rather than ship a tool that merely looked right, Equipoise:

Built a second, independent model whose only job was to check the first one's math, cell by cell.
Reconciled both against the firm's own internal spreadsheets, line by line, until the numbers matched to the dollar.
Ran structured quality passes that surfaced and fixed real defects before they could reach a customer quote.

The outcome

The firm got a fast, self-serve way to price deals consistently, with the math verified against ground truth rather than taken on faith. The engagement also produced a clear path to a production web app for wider rollout. Equipoise stayed through the iterations and left the firm owning the tool, not dependent on a black box.

The takeaway

Anyone can generate something that looks like a solution. The work is proving it is correct, scoping it to your business, and leaving it in your hands. That is the difference between AI-enabled delivery and AI slop.

A pricing engine the client's own finance team could trust.