Problems you'll recognize.
If your team is adopting AI and hitting walls with your current tools, you're not alone. These are the patterns we see — and what we built for them.
Your tools are disconnected from your AI.
AI runs in one silo, project management in another, documents in a third. Your team is copying context between tools instead of doing the work. The AI can't see the plan, and the plan can't see what the AI did.
Your tools are disconnected from your AI.
AI runs in one silo, project management in another, documents in a third. Your team is copying context between tools instead of doing the work. The AI can't see the plan, and the plan can't see what the AI did.
You're migrating and don't want to blow everything up.
Your team has years of muscle memory in Jira, Asana, or spreadsheets. The last thing they need is a retraining exercise in the middle of a delivery cycle. But you also can't keep duct-taping AI onto tools that weren't built for it.
You're migrating and don't want to blow everything up.
Your team has years of muscle memory in Jira, Asana, or spreadsheets. The last thing they need is a retraining exercise in the middle of a delivery cycle. But you also can't keep duct-taping AI onto tools that weren't built for it.
Everyone wants AI but nobody can see what it's doing.
AI licenses are adding up. The justifications are vague. You have almost no visibility into which processes are actually improving or by how much. You're buying hope, not outcomes.
Everyone wants AI but nobody can see what it's doing.
AI licenses are adding up. The justifications are vague. You have almost no visibility into which processes are actually improving or by how much. You're buying hope, not outcomes.
Your process is too complex for simple tools.
You've tried the quick-start tools. They work great for simple things — and then your real-world process shows up. Context gets lost, earlier decisions get forgotten, and the tools start making mistakes that take longer to fix than doing it manually.
Your process is too complex for simple tools.
You've tried the quick-start tools. They work great for simple things — and then your real-world process shows up. Context gets lost, earlier decisions get forgotten, and the tools start making mistakes that take longer to fix than doing it manually.
Find your surface
Project Managers
Gantt charts, timelines, resource planning — familiar surfaces backed by AI that understands the full project context.
- ✓Gantt and timeline views connected to real project data
- ✓AI-assisted status reports — ask the Knowledge Server to summarize progress
- ✓Risk flagging before standup — the AI sees the plan, the tasks, and the conversations
You're preparing for a status meeting. Instead of chasing updates, you ask the Knowledge Server: "Where are we on the mobile feature?" It pulls from tasks, conversations, and documents to give you a current, sourced answer.
See project viewsDevelopers & Leads
Kanban boards, sprint views, agent integration — connected to the same work the planners see.
- ✓MCP integration — your Claude Code agent reads the plan, updates tasks, and sends summaries without leaving the terminal
- ✓Sprint views with AI-assisted estimation and velocity tracking
- ✓Agent activity feed — see what your AI did while you were coding
Your coding agent just finished a feature. It updates the task, captures the time, and sends a note to the team. You never opened a dashboard.
See agent integrationExecutives
Governance, auditability, AI maturity — managed adoption that brings the organization along.
- ✓See how AI is participating across projects — not just that it's being used, but where it's making a difference
- ✓Role-based access controls govern what the AI can see, same as your team
- ✓Audit trail for every AI action — queryable, attributable, reviewable
Your team is using AI across three projects. You can see which workflows are accelerating, where the AI is most active, and how costs map to outcomes — without asking anyone for a report.
See governance & trust