Project Overview
We developed a custom, domain-tuned large language model for a client with strict data-privacy needs — delivering expert-level performance on their specialized tasks while keeping all data inside their own infrastructure.
The Challenge
General-purpose APIs could not match the client's domain terminology and could not be used at all for their most sensitive data, which had to stay on-premise for compliance.
- Generic models misunderstood domain-specific language
- Sensitive data could not leave client infrastructure
- API costs scaled painfully with volume
- No control over model updates or behavior
Our Strategic Approach
We curated a domain dataset, fine-tuned an open-weight base model, and deployed it privately with an inference stack the client fully controls, plus an evaluation harness to prove quality.
The Solution We Delivered
The result is a private, domain-expert LLM running in the client's environment, served through an internal API with monitoring and a clear retraining path.
- Domain-tuned model on curated proprietary data
- Fully private, on-premise or VPC deployment
- Internal API with autoscaling inference
- Evaluation harness proving task quality
- Guardrails and safety filtering
- Documented retraining and versioning path
Technologies Used
- Open-weight base LLM — Foundation for fine-tuning
- LoRA / PEFT — Efficient domain fine-tuning
- vLLM — High-throughput private inference
- PyTorch — Training and evaluation
- Kubernetes — Scalable private deployment
- Weights & Biases — Experiment tracking and evals
Development Process
- Data curation — Assembled and cleaned a high-quality domain dataset.
- Fine-tuning — Tuned the base model efficiently with PEFT methods.
- Evaluation — Built task-specific evals to validate quality and safety.
- Private deployment — Deployed the inference stack inside client infrastructure.
- Monitoring & handover — Set up monitoring and a documented retraining path.
Results & Impact
The custom model outperformed general APIs on the client's tasks while keeping data fully private and costs predictable.
- Domain-task accuracy exceeded general-purpose APIs
- All sensitive data kept on client infrastructure
- Per-query cost reduced at the client's volume
- Full control over updates, behavior, and safety
🎯 Key Takeaway
A custom, privately deployed LLM gave the client domain-expert AI on their own terms — accurate, compliant, controllable, and cost-effective at scale.
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