AI/ML Development

We build for production from day one — rigorous evaluation frameworks, load testing, fallback handling, monitoring pipelines, and a defined accuracy threshold (85%+ on production corpora) before any system goes live. Demo performance and production performance are measured separately.

A well-scoped RAG application — ingestion pipeline, retrieval layer, evaluation framework, and a chat interface — typically takes 8–12 weeks from kick-off to production-ready. Complex enterprise knowledge bases with multiple data sources take 12–20 weeks.

Both, depending on the use case. Most enterprise applications achieve strong results with prompt engineering and RAG before fine-tuning is needed. We recommend fine-tuning only when the base model consistently fails on domain-specific tasks where retrieval alone isn't sufficient.

We design for data minimisation from the start — on-premise or VPC-deployed models where required, strict PII handling in ingestion pipelines, role-based access to vector stores, and audit logging throughout. Compliance requirements (HIPAA, GDPR, financial data) are scoped before architecture is finalised.

An agent is a loop: perceive input → reason → select tool → execute → observe result → repeat. We build these with defined tool sets, guardrails, memory management, and human-in-the-loop escalation for edge cases. In practice, a customer query arrives, the agent classifies it, retrieves context, drafts a response, checks it against policy, and either sends or escalates.