10x Cost Reduction: Migrating a Fintech Off Frontier LLMs Without Losing Performance

CipherSense AI designed a phased LLM migration and optimization strategy to safely transition high-volume workflows away from expensive general-purpose APIs to a localized, fine-tuned, and cached open-weight model infrastructure.

1. 01

   Evaluation & Golden Dataset Curation

   Using the CipherSense Evaluation Framework, we analyzed over 50,000 production traces to extract a highly representative "Golden Dataset" of 2,500 diverse fintech customer scenarios, grading them using a multi-criteria LLM-as-a-judge system.

2. 02

   Model Distillation & Fine-Tuning

   We fine-tuned a highly efficient 8-billion parameter open-weight model (Llama-3-8B-Instruct) using Parameter-Efficient Fine-Tuning (PEFT/LoRA). We trained the model explicitly on synthesized, high-quality reasoning traces generated by frontier models mapping to the client's internal compliance guidelines.

3. 03

   Optimized Inference & Caching Infrastructure

   We deployed the fine-tuned model on dedicated, auto-scaling GPU instances (vLLM framework) and implemented a low-latency semantic caching layer to instantly resolve duplicate or highly similar queries without invoking model inference.