Build an AI strategy your business can put to work.

Start with how the work actually runs.

Whether AI can do a task in a demo is the easy question. The harder one is whether your business has the data, process discipline, controls, and ownership to run that workflow reliably after launch.

What I evaluate

• Candidate workflows and the value each could return.
• Data sources, permissions, privacy, and compliance limits.
• Integration architecture and vendor dependence.
• Where people review the agent’s work, and how problems are escalated.
• The measures that show whether an agent is helping or quietly creating extra work.

What you get

A short list of AI opportunities worth pursuing, the readiness gaps to close first, and an implementation plan that avoids building more than you need.

I apply this work inside my own product.

HeroMe is an AI product Vitasphere owns and runs: a storytelling platform where parents create personalized 12-chapter bedtime stories for children ages 4 to 12.

HeroMe is early-stage, so I don’t publish its financials or internal metrics. What matters here is that I operate an AI product myself, where content generation, safety limits, user experience, pricing, and customer support all have to work together. My advice on AI comes from that experience, not only from reviewing other people’s systems.

What hands-on AI work adds to the advice.

Jay works directly in agentic engineering, semantic retrieval, and model evaluation. Building and testing these systems himself has given him working methods for organizing AI tasks, giving models the right context, and judging the results. Vitasphere turns that into technology choices and a delivery approach fitted to your business.

Agentic engineering: from intent to delivery

Turn a business objective into a specification a team can execute. Break AI work into focused tasks, pair automated tests with review by a second model, and move the result through a clear release process. Model capability is held to the standards your team has to maintain.

Knowledge systems: make context useful

Semantic search and retrieval-augmented generation (RAG) connect models to the information a business already holds. The work lies in choosing what to retrieve, keeping track of sources, and testing answers against real questions. Start with one useful knowledge workflow and expand from what it shows you.

Model evaluation: choose for the actual task

Compare approaches on representative cases, with clear success criteria and answers checked against their sources. Weigh the quality of the result against the effort of running it. Keep models and tools replaceable, so a better one can be swapped in without rebuilding the business process around it.

The Enso methodology ties these together: specify the outcome, build, validate, and improve. Your team ends up with a way of working that keeps up as the technology changes.

WebFetch.MCP: connect local AI to current information.

Jay wrote WebFetch.MCP to give local AI clients web search and content extraction through the Model Context Protocol. It pairs search results with readable source content, so an AI workflow can use information newer than the model’s training data.

Because the repository is public, you can inspect this part of the practice directly: how tools are integrated, how sources are accessed, and how outside information is handled.

The source on GitHub · How platform leadership and applied AI meet at LifeChef