From data design to operation, a four-stage method.

Data Foundation

Data Foundation

In most organisations the constraint is distribution rather than absence of data. Work begins by restructuring technical documents, quality data and operating standards into a form a model can reference.

• Inventory of internal documents and systems
• Document structuring and chunking strategy
• Access design by permission and security tier
• Identifying low-quality data and cleaning rules

Model Engineering

Model Engineering

Accuracy, cost and maintenance are assessed together to select the configuration suited to the task. Fluency in manufacturing terminology and internal abbreviations determines early quality.

• RAG pipeline design and retrieval tuning
• Domain fine-tuning and sLLM compression
• Prompt and system instruction optimisation
• On-premise and private deployment

Evaluation

Evaluation

"It feels better" does not secure internal approval. Evaluation sets and metrics aligned to actual business criteria are built so improvement can be presented numerically.

• Evaluation sets built from real work scenarios
• Accuracy, hallucination and latency measured
• Safety and sensitive-data exposure checks
• Feedback loop with the people using it

Operation & Scale

Operation & Scale

After go-live, quality is managed against real usage data. A configuration validated in one department is then extended across the organisation.

• Usage monitoring and quality reporting
• Continuous improvement cycles
• Training and internal rollout support
• Handover and knowledge transfer