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Resources for Operating AI Teams with Confidence
Practical insights on project health, workflows, and intelligent governance.
AI Has Outgrown Scrum. We Built What Came Next.
Feb 11, 2026AI teams outgrew notebooks and generic workflows. Learn why AI delivery breaks in production and how Umaku was built to fix it.
AI Project Health in 5 Minutes: Instant Agentic Diagnostics
Feb 2, 2026Stop guessing your project’s health. Learn how to use Agentic Diagnostics to identify bottlenecks, track workloads, and fix bugs in under 5 minutes with AI.
Get Contextual Feedback From Your AI Agents
Jan 22, 2026Learn how Umaku uses RAG, vector databases, and sequential AI agents to deliver contextual intelligence across code, tickets, and teams.
Semantic Indexing for Code Search: Beyond Keywords to Meaning
Jan 22, 2026Learn how Umaku team built a context-aware semantic code search engine that goes beyond basic RAG to understand code structure, authorship, and intent.
Umaku In Action
A practical walkthrough of how Umaku governs AI development — end to end.
Understanding the Umaku Platform
A structured free one-to-one demo on how Umaku supports AI teams through intelligent governance — covering boards, roadmaps, backlogs, analytics, and development oversight.
- Board view & sprint governance
- Roadmap & delivery planning
- Backlog orchestration
- Team & contribution analytics
- Governance signals and visibility model
Common Questions About Using Umaku
- Who is Umaku designed for?
Umaku is built for AI product teams — including project managers, engineering leads, data science managers, and delivery leaders responsible for turning AI initiatives into production systems.It’s designed for teams that need structure, governance, and visibility while developing real-world AI products.
- Is Umaku only for AI teams?
Umaku is purpose-built for AI development workflows, but it can also support teams working on traditional software.Its real value shines when managing model-driven products, experimentation-to-production workflows, and complex AI delivery cycles.
- How does Umaku help move from notebooks to production?
Umaku helps teams operationalize AI by adding structure after experimentation — sprint planning, ticketing, code validation, governance, and reporting — turning research into production-ready systems.
- How is Umaku different from Jira with integrations?
Jira requires heavy customization and third-party integrations to support AI workflows. Umaku is natively built for AI product development, offering:
- Context-aware code and ticket validation
- Built-in project assistants
- Comprehensive automated review reports
- AI-powered team members selection
- Does Umaku replace managers or developers?
No. Umaku augments teams — it doesn’t replace them.
Managers keep full control, and developers stay focused on building.
Umaku automates analysis, validation, and reporting so teams can make better decisions faster. - How does governance work without slowing teams down?
Governance is embedded directly into daily workflows. Quality gates and validations are automated, but leaders can always override.This keeps standards high without blocking progress or adding bureaucracy.
- Can Umaku work alongside existing tools?
Yes. Umaku integrates with tools like GitHub and can coexist with your existing stack. It centralizes intelligence and governance while letting teams keep the tools they already use.
When Teams Turn to Umaku
- AI projects move from experimentation to production delivery
- Context gets lost across tickets, code, and documentation
- Leaders need visibility without slowing teams down
- Governance is required without heavy bureaucracy
- Tool sprawl starts hurting productivity