AI R&D
AI Model Training & Fine-tuning
EXAGON AI R&D fine-tunes foundation models on your proprietary data, builds RAG pipelines with vector stores, and benchmarks models for accuracy, latency, and cost — delivering domain-specific AI that outperforms generic models on your tasks.
94%
Eval accuracy
<2%
Hallucination rate
200K
Docs indexed
Domain-specific AI
Key outcome
Capabilities
LLM fine-tuningRAG pipelinesCustom embeddingsModel benchmarking
Why organisations choose this
- Fine-tuned models trained on your docs, tickets, and product data
- RAG pipelines with chunking, retrieval, and citation strategies
- Custom embedding models for semantic search and classification
- Rigorous benchmarking against baselines with reproducible evals
Use cases
01
Legal document AI
Fine-tuned model for contract clause extraction and risk flagging.
02
Support knowledge RAG
Retrieval-augmented agent grounded in product docs and past tickets.
03
Industry terminology model
Custom embeddings for medical, legal, or engineering vocabularies.
What we deliver
Fine-tuned model weights or API endpoint
RAG pipeline & vector index
Evaluation benchmark suite
MLOps deployment guide
Insurance policy assistant
An insurer needed an internal copilot trained on 200K policy documents with citation requirements.
Result: Answer accuracy 94% on eval set; hallucination rate below 2% with RAG citations.
Ready to discuss AI Model Training & Fine-tuning?
Speak with our team about scope, timeline, and fit for your organisation.