Our AI / LLM Integration team helps enterprises decide when fine-tuning adds value over RAG or prompt design alone — and executes the full pipeline from curated datasets to monitored production deployment.
When Fine-Tuning Makes Sense
Fine-tuning is justified when you need the model to internalise domain terminology, follow rigid output schemas, or perform specialised classification at scale. It is less appropriate when answers must always cite live data — in those cases, RAG is typically the better foundation.
Data Preparation Is Everything
Quality beats quantity. We help clients build instruction-tuning datasets from internal documentation, support transcripts, and validated examples — with human review, deduplication, and PII redaction built into the pipeline.
- Curate — Select representative, high-quality examples
- Structure — Use consistent prompt/response formats
- Validate — Hold out test sets that reflect real production queries
Training, Evaluation, and Guardrails
We fine-tune on enterprise-grade infrastructure with hyperparameter optimisation tuned to your cost and latency targets. Every model is evaluated against accuracy, hallucination rate, latency, and safety benchmarks before release.
Production Deployment
Fine-tuned models integrate into your existing stack via APIs, with versioning, rollback capability, and continuous monitoring for drift. We pair model deployment with human-in-the-loop review where regulatory or high-stakes decisions require it.