New playbook: when your own AI model pays. Read
Travelers (property and casualty insurance, ~30,000 employees) reported this on 30 Jun 2026 in company self-report (source). It belongs to the learning loop layer.
TravelersLLM, a proprietary insurance LLM built by in-house engineers on millions of company documents with underwriting, claims and service experts; applications route each query to it or to a frontier model.
In testing on tens of thousands of insurance questions it outperformed commercial models on quality, cost and speed (no figures disclosed).
The number covers internal evaluation on insurance-domain questions; used in underwriting, claims and service workflows.
“…if you don't need to be using the most expensive frontier model to answer the same question, we shouldn't be,” Travelers said.
Reviewed corrections are kept as test cases and later used to train models.
Read the guide to the learning loop.
96%
lower estimated serving cost for one agent
A daily continual-learning loop for Sidekick's GraphQL agent: an expert-calibrated LLM judge scores production conversations, a self-healing pipeline repairs failures, and the repaired trajectories are trained into a smaller model (SFT, then GRPO against the judge) that replaces the frontier model (source).
13.8x
lower cost per task, with higher accuracy than the best frontier model
A Qwen3-235B model fine-tuned on Tinker with investor-labelled data to do six daily information-filtering tasks (e.g. which news matters to a macro investor) (source).
1% → 99%
valid structured output after fine-tuning a small model
A proof of value on Red Hat OpenShift AI: GPT-4.1 generated synthetic training data, then small models (Phi-4 Mini 3.8B, Qwen 2.5 1.5B/7B) were fine-tuned for multilingual structured extraction from social media, deployable locally (source).