Vertical-specific model fine-tuning and performance optimization for healthcare, financial services, and legal applications. From RAG system implementation to prompt engineering and evaluation harness development, we ensure your AI models achieve production reliability with measurable quality metrics.
End-to-end AI model optimization from fine-tuning to production evaluation pipelines.
Domain adaptation for healthcare, financial services, and legal applications. Instruction tuning with curated datasets. LoRA and QLoRA techniques for efficient adaptation. Compliance-aware training with bias mitigation.
Hybrid retrieval combining dense embeddings and sparse methods. Cross-encoder reranking for precision over recall. Vector database optimization (Qdrant, Milvus, pgvectorscale). Citation generation and source attribution for auditable responses.
Systematic prompt optimization with A/B testing frameworks. Chain-of-thought and structured output patterns. Prompt versioning and regression testing. Domain-specific prompt libraries for regulated industries.
Inference latency optimization targeting sub-200ms response times. Throughput optimization with continuous batching and KV-cache management. Quantization strategies (AWQ, GPTQ) for cost-efficient deployment.
Golden dataset curation and maintenance. Task-specific evaluation suites with automated regression testing. RAGAS-based RAG evaluation (faithfulness, answer relevance, context precision). Continuous monitoring with drift detection and quality alerts.
Iterative optimization with continuous evaluation gates ensuring production reliability.
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flowchart LR
subgraph Phase1["Baseline"]
B1[Data Audit]
B2[Golden Dataset]
B3[Baseline Metrics]
end
subgraph Phase2["Optimization"]
O1[Prompt Engineering]
O2[RAG Tuning]
O3[Fine-tuning]
end
subgraph Phase3["Evaluation"]
E1[RAGAS Metrics]
E2[Regression Tests]
E3[Quality Gate]
end
subgraph Phase4["Production"]
P1[CI/CD Integration]
P2[Canary Deploy]
P3[Monitoring]
end
Phase1 --> Phase2
Phase2 --> Phase3
Phase3 -->|Pass| Phase4
Phase3 -->|Fail| Phase2
Medical LLM fine-tuning for clinical decision support. HIPAA-aware training data handling. Human-in-the-loop workflows for high-stakes outputs. Bias testing for demographic fairness. Hallucination prevention for medical accuracy.
Compliance-aware models for regulatory mapping and risk assessment. Explainable outputs for Consumer Duty requirements. Audit trail integration for model decisions. Bias monitoring for fair lending and underwriting.
Contract analysis and document processing optimization. Jurisdiction-specific model tuning. Citation accuracy and hallucination prevention. Privilege-aware RAG with access controls. Legal reasoning chain validation.
Faithfulness scoring to measure grounding in retrieved context. Answer relevance assessment. Context precision and recall metrics. Continuous evaluation with drift detection and alerting.
Llama 3 70B fine-tuned on 25,000 anonymised claim interactions, RAG grounded in policy documents, RAGAS in CI with canary rollback. Manual load down 80%, citations on 100% of responses.
Read the case →30-minute call. Engineering discovery memo within five working days.