Case Study
AI Product
Turning AI capabilities into production workflows
By Janahan Thavendran · 1 min read
Challenge
A team had working AI demos but no reliable way to know whether outputs were good enough for real customers.
Context
The product combined retrieval, generation and workflow automation. Quality, cost and latency were not yet measured.
What we did
- Defined the customer workflow the AI needed to support
- Introduced an evaluation framework with clear quality thresholds
- Designed human-in-the-loop review for high-risk steps
- Built a production readiness plan covering cost, latency and failure states
Technology
RAG pipelines, LLM orchestration, evaluation datasets, observability and human review tooling.
Product decisions
- Measure quality before expanding scope
- Keep humans in the loop where errors are expensive
- Ship one dependable workflow rather than many fragile ones
Outcome
A focused AI workflow with defined evaluation, understood operating costs and a clear path from prototype to production.