Case study · From the Pluris libraryReceipt no. 2362
What sucked
An AI-native FP&A software company sold a natural-language finance copilot into finance teams, including Fortune 500.
It stalled at ~60% accuracy. They were losing deals due to incorrect responses, and their prospects were preferring more AI native platforms.
What they built
An AI workbench and evaluation harness for an AI-native FP&A copilot: a semantic data dictionary maps the customer's financial data, a question router sends routine asks to templated fast paths and forecasts to a purpose-built predictive engine, and a custom eval layer scores every answer on held-out data before it reaches a customer.
What changed
144% NRR (up from 82%)
Net revenue retention, customers renewed and grew their spend once the copilot was reliable enough to trust.
60->95% output accuracy increased, 20% CSAT improvement
3X more customer upvotes, 40% drop in downvotes
Live with 100+ enterprise customers
Cleared all security and compliance requirements across Fortune 500 companies
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