Problems we help solve
What usually brings teams to us
- A prototype works in the demo but nobody has decided how it behaves with real customer data.
- Agents are being added without clear limits on which tools they can invoke.
- There is no way to tell whether a model or prompt change made things better or worse.
- The AI bill is growing faster than usage or revenue.
Typical situations and decisions
Situations
- Taking an AI beta to general availability
- Adding agents or tool use
- Answering enterprise security questionnaires about AI
- Choosing between model providers
- Introducing RAG over customer data
Decisions we help you make
- Which model, where it runs and how to switch
- What an agent is allowed to do, and on whose behalf
- How to evaluate quality before and after release
- How tenant data stays isolated in retrieval
- How to attribute and cap inference cost
How we work
1.
Map the AI capability against the product and SaaS architecture around it.2.
Agree what 'good enough for production' means, in tests not opinions.3.
Design controls, evaluation and observability with your team.4.
Help implement and validate the first production release.
Experience behind the work
Relevant proof
Siva's prior professional experience includes work as an AWS SaaS Factory and AI specialist on AI-native architecture with software companies, and hands-on building of AI features and agents.
This describes Siva's prior professional experience, not Wolkn Minds client results. Meet Siva
Where this capability is applied: AI Production Readiness
Have a decision in this area?
Tell us what you are working through. We start with the context and tell you plainly if we can help.
Prefer email? hello@wolknminds.com