XPndAI provides expert LLM fine-tuning services — adapting open-source and proprietary language models to your domain-specific data, tone, and task requirements for superior performance.
End-to-end AI and software engineering with measurable enterprise outcomes.
Fine-tune LLMs on your labelled domain datasets to improve accuracy, tone adherence, and task-specific performance beyond zero-shot capabilities.
Reinforcement Learning from Human Feedback to align model outputs with your quality standards, safety requirements, and user preferences.
Cost-efficient fine-tuning using LoRA, QLoRA, and adapter methods — achieving domain adaptation without the compute cost of full fine-tuning.
Rigorous evaluation suites comparing fine-tuned vs. base model performance across domain-specific tasks, hallucination rates, and safety metrics.
Production deployment of fine-tuned models via vLLM, TGI, or cloud endpoints with optimized quantization for your latency and cost targets.
Expert data collection, cleaning, formatting, and augmentation to build the high-quality fine-tuning datasets that determine model quality.
A healthtech company needed an LLM that responds accurately in medical terminology, understands clinical abbreviations, and follows HIPAA-safe output guidelines. We fine-tuned Llama 3 on curated medical Q&A and clinical note datasets — achieving significantly better performance than GPT-4 on their internal medical benchmarks.
A D2C brand fine-tuned our curated Mistral model on their product catalogue descriptions, customer service transcripts, and approved brand copy. The result: an LLM that writes in their exact brand voice, uses correct product terminology, and follows their editorial guidelines — automating content generation at scale.
Talk directly to our lead engineers. We audit your requirements, propose the exact architecture, and give you a transparent roadmap — all in one call.