By Geko Technologies ·
Many organisations have run a generative AI pilot. Far fewer have a system that real users depend on every day. The gap is rarely the model. It is usually everything around the model: data, evaluation, security and cost control.
1. Start with a measurable use case
Pick a workflow where success can be measured: time saved per ticket, accuracy of extracted fields, deflection rate for support questions. "Add AI to the product" is not a use case; "draft first replies to tier-1 support tickets" is.
2. Ground the model in your data
Most business use cases need answers based on your documents and systems. Retrieval-augmented generation (RAG) lets the model cite approved sources instead of guessing. The quality of chunking, metadata and access control matters more than which vector database you choose.
3. Build an evaluation set before you scale
Create a set of realistic questions or tasks with expected outcomes. Run it on every prompt, model or retrieval change. Without evaluation, every change is a guess, and regressions reach users.
4. Add guardrails and human approval for actions
When AI agents take actions (updating a CRM record, issuing a refund, sending an email), add:
- Least-privilege permissions for each tool
- Human approval steps for high-impact actions
- Full logging of inputs, outputs and tool calls
5. Watch cost and latency from day one
Track cost per task, not just total spend. Caching, smaller models for simple steps and good retrieval often cut costs dramatically without hurting quality.
6. Plan for operations
Production AI needs owners: someone to review failures, update the evaluation set and roll out improvements. Treat it like any other production service, with monitoring and on-call.
Geko Technologies builds and runs production AI systems for teams in the USA, Canada, UK, Australia and the Middle East. Talk to our AI engineers.