Map the path to production
2-4 weeks
Map the operational work, users, data, systems, decisions, risks, and success measures around an AI initiative.
Outcome
A shared product and technical direction, with a prioritised plan for reaching production.
From operational need to production system.
Ratna works across product definition, data, integrations, architecture, controls, and delivery to make AI work in the real organisation.
The AI model is only one part of the system. Production also needs reliable facts, reachable tools, clear permissions, owned exceptions, measurable outcomes, and people who can run what gets built.
Three ways to work together
Start before a prototype exists, bring us something promising, or build shared foundations for several systems.
Take an AI system to production
4-12 weeks
Shape the product, connect real data and tools, add permissions and review, test the system, and introduce it into daily work.
Outcome
A controlled AI system that a team can use, measure, monitor, recover, and own.
Build the shared operating foundation
Scoped together
Create reusable ways to access data, connect systems, enforce permissions, evaluate outputs, observe failures, and support more than one AI use case.
Outcome
A maintainable foundation that helps teams move beyond isolated prototypes without losing control.
Example: from promising prototype to production system
Prototype
A team can classify requests in a demo, but people still paste in data, check several systems, decide what the AI may see, and recover failures by hand.
Production work
We define the product boundaries, connect approved sources and operational tools, handle permissions and exceptions, add evaluation and human review, and prepare the team that will run it.
In operation
The system can be measured, monitored, recovered, and improved against the way the operation worked before.
What production requires
Every system is different. These foundations remain.
- A product owner, real users, and a result worth improving.
- Trusted data sources, explicit permissions, and stable integrations.
- Tests, visible failures, and human review where judgment matters.
- Monitoring, documentation, and a team able to operate the system.
Build once. Reuse deliberately.
A production system often reveals foundations that other teams can share: connectors, access patterns, evaluation, observability, and operating practices. Ratna can help turn those proven parts into a coherent layer for further AI systems, then support the teams running them.
Contact us
A few lines are enough.