Solutions · AI Project Management
Project tools that keep themselves up to date
Project management tools have a chronic problem: the people doing the work are the ones expected to keep them updated, and they don't, because they're doing the work. Status reports get written manually on Friday. Blockers get surfaced in stand-ups three days too late. Forecasts stay optimistic until they break.
AI fixes a useful chunk of this. We build PM systems that extract status from the actual artefacts (commits, ticket updates, Slack threads, meeting transcripts), surface blockers proactively, and draft the status report so a human just edits it.
What it actually does
We layer AI onto your existing project tracker (Jira, Linear, Asana, Trello, ClickUp) or build a bespoke one if your projects don't fit the standard task/sprint shape. Either way, the goal is to remove the manual reporting overhead while improving the quality of signal.
The AI watches what's actually happening in your team's tools (commits, PR descriptions, ticket status changes, chat conversations, meeting transcripts) and synthesises it into the artefacts humans currently produce by hand: weekly status updates, executive summaries, blocker reports, retrospective drafts. The human still owns the final decision; the AI removes the typing.
For larger programmes we also build forecasting that's grounded in real velocity data, not gut-feel updates from project leads who are incentivised to be optimistic.
How it works
The shape of a typical build. Yours will vary on the specifics, but the pattern is consistent.
Step 1
Map current sources of truth
We catalogue where project data actually lives today: the official tracker, the Slack channels people use to coordinate, the email threads with the client, the meeting recordings nobody reviews. Most of this is rich signal that's just unstructured.
Step 2
Wire the data sources
Read-only integrations into Jira/Linear/Asana, GitHub/GitLab, Slack/Teams, calendar, and meeting transcript sources. We don't change how anyone works; we just observe what's already happening.
Step 3
Build the synthesis layer
AI summarises activity per project on whatever cadence you want (daily, weekly, real-time). Outputs are designed for the consumer: terse for execs, detailed for delivery leads, action-oriented for the project team.
Step 4
Surface blockers proactively
Pattern detection across the activity stream catches the things humans miss: ticket idle for too long, PR stuck in review, a Slack thread where someone said 'this is blocked' but the ticket still says in-progress.
Step 5
Iterate on what gets surfaced
First few weeks tend to surface too much (false positives) or too little (missed blockers). We tune from real feedback until the noise/signal ratio earns trust.
This fits if you...
- Run multiple concurrent projects with project leads who spend 2+ hours per week writing status reports.
- Have a leadership team that consistently asks 'where are we on X?' and the answer requires manual digging.
- Use a project tracker that's updated unevenly because the team hates the overhead.
- Want better forecasts than the optimistic ones project leads currently produce.
This isn't a fit if...
- You run a single project with a small team. The overhead removed isn't large enough to justify the build.
- Your team genuinely loves your existing tracker and keeps it pristine. Some teams do; we won't replace what's working.
- You're looking for AI to make actual project decisions (what to prioritise, who to assign work to). It can suggest; it shouldn't decide. That's still a human call.
Typical build shape
Scope
Read-only integration with your existing project tracker, code platform, and chat tool. Auto-generated weekly status report, blocker surfacing, and an executive summary view.
Timeline
Roughly 2-3 months for the first phase across several projects. Forecasting and additional analytics in shorter subsequent increments.
Indicative price
Fixed-price build, scaling with the number of integrated tools and project complexity.
All ranges in NZD ex-GST. Precise numbers come out of the operations audit. See our engagement model for the full pricing approach.