Scale specialized insurance workflows without growing API spend
Insurance AI Automation
A specialized 14B model that outperformed GPT-4o on owned hardware
The situation
The problem
The client was paying about $2,500 a month for a closed-API generalist and wanted to 4× usage on a specialized insurance workload. The meter made scale look like a penalty.
How it usually works
Rented token APIs own the economics. A generalist model is convenient until the task is narrow, the volume grows, and every extra document is another line on someone else's invoice — with data in their logs.
How we ran it
We LoRA fine-tuned Qwen2.5 14B for the specific task and ran it on hardware they owned (~48GB). On their workload the specialized 14B outperformed GPT-4o. The same box absorbs current volume and 4× headroom.
Strategy
Specialize the model to the job instead of renting a frontier generalist. Document parsing, analyses, and extraction belong on owned GPUs when the task is repetitive and the data is theirs.
Implementation
Self-hosted Qwen2.5 14B with TensorRT-LLM and an OpenAI-compatible surface. Batching sized for the current workload and 4× scale so growth does not require a new vendor contract.
Results
- Eliminated a $2,500/mo recurring API cost.
- Fine-tuned 14B outperformed GPT-4o on their core task.
- Usage could 4× on the same owned hardware.