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LegalTech / Document Intelligence $5M–$20M ARR Series A, AI-first product PROFIT+ · Research · Optimise · Implement

LegalTech / Contract Intelligence

A confusing cost-plus credit model was rebuilt around the value a contract review delivers to the buyer.

2
buyer segments researched — law firm and in-house

The challenge

A contract-intelligence platform had an AI review engine that analysed and summarised contracts in minutes. It launched a credit system with no design framework: credits priced at cost-plus, with no mapping to the value a review delivered. Law-firm buyers found it confusing and enterprise buyers demanded flat-rate pricing to avoid budget uncertainty — usage was inconsistent and conversion was low.

The approach

Each step below is a step of PROFIT+, named so you can see which part of the method did the work.

  1. Research

    Conducted buyer research across law-firm and in-house legal segments.

  2. Research

    Found the primary value metric was risk identified and time saved per review rather than the number of contracts processed.

  3. Optimise

    Redesigned the credit architecture so cost mapped to review complexity rather than document length, and built a spend-modelling tool.

  4. Implement

    Introduced pre-committed enterprise tiers with rollover rights and credit allowances that created natural upgrade triggers.


The outcome

  • Credit relaunch achieved materially higher conversion than the original model.
  • Enterprise buyers moved to pre-committed tiers with rollover rights.
  • Average revenue per account increased as the upgrade path became clear.

In one line

A LegalTech company had built excellent AI but a credit model buyers could not understand. We redesigned the commercial architecture around what buyers actually valued, and conversion improved significantly.

Relevant to Product teams · LegalTech / Document Intelligence

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