Project Status That Reports Itself, Risk You See Weeks Earlier
Custom AI built over your Jira, Azure DevOps, timesheets, and finance data so status roll-ups write themselves and slipping work surfaces before the steering committee asks.
How it works
Most project data is already true somewhere - in commit history, ticket transitions, timesheets, and purchase orders - and then a person spends Thursday afternoon turning it into a slide that is optimistic by Monday. We build AI that reads the systems your work actually happens in, produces status without asking anyone, and flags the dependency or resource problem two sprints before it becomes a date change. Not another PM tool to adopt, and not the generic assistant bundled with your tracker.
Status Roll-Ups Built From System Activity
Portfolio, program, and project status assembled from what your systems recorded this week, not from what someone remembered to type.
- RAG status derived from ticket flow, burn rate, open dependencies, and actual hours against plan
- Executive and steering-committee narratives drafted in your reporting format, with every number traceable to its source record
- Portfolio views that reconcile delivery progress with budget consumed from your ERP or PSA
- Variance callouts that explain what changed since the last report and which decision it needs
Slippage, Risk & Dependency Prediction
Models trained on how your projects have actually gone wrong before, scoring live work for the risk of a date, scope, or margin miss.
- Slippage probability per milestone using velocity trends, rework rates, scope churn, and approval latency
- Cross-project dependency mapping that traces one late integration through every downstream date it moves
- Early signals from operational noise - reopened tickets, ballooning WIP, blocked items aging, unanswered vendor requests
- Risk register entries drafted with proposed mitigations, routed to the owner who can act on them
Meetings, Actions & Resource Allocation
Decisions and commitments captured from meetings, then matched against who actually has capacity to deliver them.
- Standup, steering, and client call transcripts converted into assigned tasks in your tracker with owner and due date
- Follow-up detection so commitments made verbally do not quietly disappear before the next meeting
- Allocation recommendations that respect skills, certifications, billable targets, PTO, and existing commitments
- Bench and over-allocation alerts by role and week, with reallocation options costed against project margin
Where teams use it
Built for real revenue work.
Enterprise PMO & Portfolio Governance
PMOs running 40+ concurrent initiatives replace the weekly status chase with roll-ups generated straight from delivery and finance systems. Portfolio leadership sees which programs are drifting while there is still runway to intervene.
Software & Product Delivery Orgs
Engineering leaders get slippage forecasts grounded in real Jira and repository behavior instead of story-point optimism. Dependency chains across squads are mapped continuously, so one blocked platform team does not silently reset four roadmaps.
Professional Services & Client Delivery
Consultancies and agencies tie project health to margin, catching scope creep and utilization gaps in the same view. Staffing decisions are made against live capacity data rather than a spreadsheet refreshed once a month.
“The Thursday status scramble is gone - the roll-up is already written and correct when we open it. What changed the business was the slippage scoring; we now hear about a program in trouble while we can still move people.”
FAQ
Questions, answered.
How is this different from the AI already built into Jira, Asana, or Monday?
Built-in assistants only see the data inside their own tool, so they summarize tickets and nothing else. Your real project truth is spread across a tracker, timesheets, an ERP, a CI pipeline, and a shared drive of client commitments. We build AI over that full picture, trained on your delivery history and your definitions of red, amber, and green - and you own the resulting system outright.
Which systems can you pull from?
Jira, Azure DevOps, Asana, Monday, Smartsheet, MS Project, ServiceNow, Workday, NetSuite, and most PSA and timesheet platforms via API, plus SharePoint, Confluence, Slack, and Teams for the unstructured half. Where a system has no usable API we read from its database replica or scheduled exports. Results are written back into your tracker so PMs never leave the tool they already use.
How much project history do you need before predictions are useful?
Roughly 18 to 24 months of completed projects with real dates and effort data gives strong milestone-level accuracy. With less, we start with rule-based risk signals that still catch the obvious failure patterns and let the predictive model mature as data accumulates. We backtest against your closed projects first, so you see measured accuracy before anything reaches a steering committee.
What does an engagement cost and how long does it take?
We scope one high-value use case - usually automated status or slippage prediction - and quote it at a fixed project cost with a 4 to 12 week delivery window. You get a dedicated team, work built under SOC 2 controls with 99.9% uptime targets, and full ownership of the code and models at handover. No per-seat AI fees layered on top of the licenses you already pay.
Keep exploring
More from the Leadify platform.
Stop Reporting Status. Start Predicting It.
Send us one portfolio and we will scope an AI build against your own delivery data at a fixed price.