How a Legal AI Platform Benchmarked Its Way to the Right LLM

The Challenge

A legal technology firm was manually processing complex, multi-field plaintiff profile forms — a time-intensive task that required contextual reasoning across hundreds of data points per case. As caseloads scaled, the manual approach became a bottleneck. The team needed to evaluate whether AI could automate the process reliably, and if so, which LLM offered the right balance of accuracy and cost — without rebuilding their stack for every test. 

The Solution

1. Multi-model testing, zero code changes — FloTorch enabled the team to switch between frontier and lightweight LLMs using the same codebase, with no engineering rework between experiments
2. Token & cost dashboard — Real-time monitoring provided per-model cost breakdowns and token usage, making trade-offs immediately visible
3. Accuracy benchmarking by form complexity — Tested model performance across simple, standard, and complex form fields to identify where smaller models broke down and where frontier models were worth the cost
4. Production-ready recommendation — Delivered a clear model selection framework tied to form type, accuracy threshold, and cost-per-task targets