Unprofitable to acquired.
90% expert alignment on every price.
An AI matter-pricing model that reads client tenure, acceptance history and complexity — not just historical averages — reached 90% alignment with human pricing experts, while a standardised integration layer made every new law firm cheaper to onboard.
pricing experts
live
profitability
Aderant
Client
VPD (Virtual Pricing Director) is an enterprise SaaS matter-pricing platform for law firms.
Goal
Turn a cash-burning legal tech startup into an acquisition-ready business, with operational discipline, a focused product, cheaper integrations, and production-ready AI pricing.
A dumb average was standing in for a pricing engine.
VPD’s price-suggestion feature was a basic average of similar historical matters — same category, similar step count. It ignored the signals that actually drive a defensible quote: client tenure, acceptance history, fee-earner experience, and matter complexity nuances. Accuracy and value were left on the table, and lawyers had every reason not to trust the number.
Enterprise law firms don’t adopt on working software alone. They need a product that fits their workflows, IT teams that can integrate it, and lawyers who trust its recommendations. Each new client meant expensive bespoke integration work, engineering ran reactive to individual sales calls with no coherent product strategy, and the company had never been profitable.
Three constraints were non-negotiable:
- No client-specific pricing signals
- Bespoke integration for every new firm
- Reactive engineering, no product roadmap
Client-aware ML, a standard schema, and hands-on adoption.
ML models were built that went far beyond the historical average, incorporating client tenure, historical acceptance rates, matter complexity indicators, fee-earner experience, and firm-specific pricing patterns. Trained on historical matter data and validated against human pricing expert decisions, the models reached approximately 90% alignment with expert recommendations — turning a flat average into an intelligent, defensible price.
A flat historical average became a client-aware price, validated against the experts who used to do this by feel.
Engineering discipline replaced reactive, sales-driven feature requests. A focused roadmap prioritised what firms actually needed — fee-earner breakdowns for finance teams, matter export without pricing for client proposals — and the VPD Schema standardised data integration, dramatically cutting the cost and complexity of onboarding each new law firm.
Adoption was handled hands-on across the entire chain. Tier 3 technical support went directly to law firm IT teams to clear integration blockers, lawyers were trained on the software, and their pain points were fed straight back into the roadmap, closing the loop between development and real-world deployment.
From cash-burning startup to acquired business.
The company turned profitable for the first time, took the product live with three law firm clients, and replaced reactive feature-building with a focused roadmap. In 2025 it was acquired by Aderant — confirmation that the AI pricing engine and the operational discipline built around it were the strategic assets they were designed to be.
* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.
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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.