Embed intelligence directly into your product
LLM Integration & Fine-Tuning
We fine-tune, align, and deploy large language models on your proprietary data — giving your product a domain expert that speaks your language and knows your business.
Technology Stack
Case Study
Financial Services
The Challenge
A wealth management firm had 20 years of proprietary investment research locked in PDFs, emails, and internal wikis. Junior analysts were spending 40% of their time searching for precedents. The firm wanted a secure, on-premise AI system that could answer complex investment questions from this proprietary corpus.
Our Solution
We built a RAG (Retrieval-Augmented Generation) system with a fine-tuned Llama 3 70B model aligned to the firm's research style and risk communication standards. Documents are chunked, embedded with custom financial embeddings, and stored in a vector database. The LLM is served via vLLM on-premise (no data leaves the firm). A guardrail layer prevents hallucinated financial advice.
Results
- 40% reduction in junior analyst research time
- Hallucination rate below 1.8% on financial fact retrieval (independently audited)
- System answers 95% of queries without human escalation
- Full on-premise deployment — zero proprietary data exposed to external APIs
Technologies Used
"This is the first AI tool I've seen that I'd actually stake a client recommendation on. The citations are precise and verifiable."
— Senior Portfolio Manager, Financial Services Client
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