What It Does
Adapts large language models so they understand the language, data, and workflows of a specific organisation. The pipeline prepares, trains, and deploys models efficiently, even on limited hardware. The result is a domain-specific model ready to run in production without the cost of full model training.
The Business Problem
Generic AI models do not understand how a particular business operates. Training a model from scratch can cost hundreds of thousands in compute. Fine-tuning existing models allows organisations to create domain-specific AI systems at a much lower cost. This pipeline provides a practical way to adapt models to business data without building large training infrastructure.
What Was Built
End-to-end fine-tuning pipeline from raw dataset preparation to deployment
Parameter-efficient model adaptation using LoRA
4-bit quantization enabling models to run on consumer-grade GPUs
Structured dataset preparation and instruction formatting
Export process producing inference-ready models for deployment
Business Impact
AI models that understand the organisation’s specific domain and language
Lower training infrastructure costs compared to full model training
Faster experimentation and iteration on limited hardware
Internal assistants trained on company knowledge and documentation
Production-ready systems rather than research prototypes