Enterprise Model Fine-Tuning & Distillation

Off-the-shelf frontier models like GPT-4o or Claude fail when asked to evaluate complex insurance claims or enterprise support policies that depend on hundreds of internal edge cases. Furthermore, sending millions of tokens to commercial APIs creates exorbitant monthly bills. By fine-tuning open-weights models (such as Qwen3.8 or DeepSeek) using LoRA on the client's historical resolution data, you deliver higher accuracy at 1/10th the inference cost, while securing an ongoing monthly monitoring and retraining retainer.

Nicho de Clientes Objetivo (Perfil de Cliente Ideal):

• Insurance carriers with proprietary underwriting guidelines
• Telecom companies needing customized support tone & policy compliance
• E-commerce brands with 50,000+ SKU catalogs and custom attribute taxonomies
• Financial tech companies with bespoke accounting chart structures

Raw Data (CSVs / PDFs / Tickets)
   ↓ (Cleaning & Filtering)
JSONL Instruction Pairs
   ↓ (Unsloth QLoRA on RunPod 8x H100)
Trained LoRA Adapter (.safetensors)
   ↓ (Merge or Dynamic Adapter Loading)
vLLM Inference Server (Client Private VPC)

Subject: Cut your OpenAI API bill by 70% with a custom Qwen model

Hi [VP of AI / CTO Name],

If [Company Name] is currently spending thousands each month calling proprietary cloud LLMs for customer ticket classification or document parsing, you are overpaying for generic intelligence you don't need.

We train custom open-source models using your historical company data. The result:
1. 99.2% adherence to your specific internal terminology.
2. Zero data leaves your private cloud.
3. Inference costs drop by 70% compared to commercial API endpoints.

We charge an initial tuning fee and an ongoing monthly retainer to retrain the model as your data grows.

Could I review a sample dataset of 50 anonymized examples to benchmark what kind of accuracy and cost savings we can achieve?

Best regards,
[Your Name]