Everything you wanted from an AI,
now in one solution.
Every workspace remembers everything that happens inside it.
A project, programme or portfolio gets its own persistent workspace — every document, conversation, decision and artifact stays contained within it. Context never leaks between engagements, and nothing has to be re-explained the next time you open the chat.
Every answer is grounded in how your organisation actually works.
Org standards, delivery playbooks, past project history and policy documents are indexed and retrieved at query time. The AI doesn't give generic advice — it gives your advice, shaped by your own organisational knowledge.
Upload almost any project document. It knows what to do with it.
A Statement of Work becomes a new project, scoped and structured. Meeting minutes become tasks and risks, assigned and dated. The same engine reads status reports, change requests, even scanned notes — and routes each one to the right action in your PM tool.
Every extracted action comes with a confidence score — and a human checkpoint.
Tasks, risks, decisions and change requests are scored for confidence before anything is written. Review, adjust or approve in one click. Nothing reaches your project management application without a clear audit trail.
An AI you can actually trust with your project data.
Every write-back is reviewable before it happens. Every response is traceable to its source. Workspace data stays isolated, access is role-based, and nothing is used to train a model outside your organisation. Trust isn't a feature here — it's the default.
It knows who to notify, and when to schedule.
Reading conversations and calendars together, the AI surfaces meetings to attend, sessions still waiting to be booked, and notifications that are overdue — so coordination overhead stops living in email threads.
It doesn't stop at the PM tool — it works across the systems around it.
The same agentic core reaches into CRM, HR and finance systems alongside your PM tool — turning a closed opportunity into a scoped project, forecasting delivery against real capacity, validating timesheets, and flagging skill gaps before they become resourcing problems.
Three layers. Built in order.
Agentic AI isn't one skill — it's three, stacked. You can't build the third without the first two holding underneath it. This is the order we built them in, and the order we'd recommend to anyone else.
RAG — giving the model your context
Before an AI can act on your organisation, it has to know it. We index org standards, delivery policy, closed-project history and live documents into a vector store the model can search at query time — every response grounded in your context, not generic advice.
MCP — giving the model your tools
Knowing isn't enough — it has to act. We build on the Model Context Protocol to connect live, two-way to your project management application, CRM, HR and finance systems, so the model can read real state and write real changes, not just describe them.
Agentic AI — giving the model judgement
The final layer: multi-step reasoning that plans, calls the right tool from the right context, and checks its own work. This is where a chatbot becomes a colleague — one that drafts the report, files the risk, and tells you what it did and why.