OMAIOMAI

Routine operational work — triaging tasks, moving information between tools, making small repeatable decisions — eats hours every week. Most of it follows patterns an AI layer can genuinely handle, if it's scoped honestly.

• Operators and small teams drowning in repetitive workflow steps
• Businesses that want AI assistance grounded in their real processes

• Operator

What I actually did on this project — kept honest, especially on collaborative work.

• Designed the automation model around real, observed workflows
• Integrated the Google Gemini API for task handling and decision support
• Built the interface, deployed, and maintain the product

• Automate only workflows that were first understood manually
• Use Gemini as an assistive layer with clear boundaries, not a black box

Next.js App Router frontend on Vercel with the Google Gemini API driving task handling and decision support; Firebase backs identity and workflow state.

Every feature is labelled by its real state. Nothing planned is shown as shipped.

• Keeping AI behaviour predictable enough to trust with real operations
• Scoping automation honestly — knowing what the model should not decide

• Constrained, well-defined prompts per workflow instead of one open-ended agent
• Human-visible outcomes so every automated step can be checked

Continuous deployment to Vercel from the project repository.

What this project does not do yet — stated plainly.

• Coverage is focused on a curated set of workflows, not every business process

• AI automation works best when it encodes a process you already understand by hand

• Broaden the set of supported workflows
• Deeper integrations with the tools the workflows touch