Real Estate & Construction · Project delay prediction
You find out about schedule slippage the day the milestone is missed
Banao builds project delay prediction that combines your schedule, procurement, and on-site progress data to flag which milestones are at risk — and which sub-contractor, material, or site condition is driving it — weeks before the date moves.
The output is not a risk score. It is a named driver your PM can act on today, fed into the project controls system you already run.
The first call is free · 45 minutes · no obligation
What we build
What a delay prediction deployment includes
A schedule model that says 'high risk' without naming the cause is not useful. Ours identifies the driver, ranks the impact, and pushes an alert the PM opens — not a weekly report that arrives after the damage is done.
Multi-signal schedule fusion
Site progress, procurement status, workforce attendance, and weather feeds are merged against your baseline schedule — so a slip in materials procurement registers before the activity date moves.
Named-driver alerts
When the model flags a milestone at risk, it identifies the leading cause: a late material delivery, a sub-contractor falling behind on concurrent works, or cumulative float erosion. Your PM gets the driver, not a percentage.
Milestone-at-risk ranking
Every upcoming milestone is ranked weekly by predicted delay exposure, so a construction director prioritises the three that need intervention rather than reviewing the full programme.
Sub-contractor performance signals
Progress versus commitment data per sub-contractor, flagging consistent under-delivery before it compounds into a programme extension — and giving the commercial team documented evidence for contract conversations.
Integration with Primavera P6 and MS Project
The model reads from and writes alerts back to the project-controls software your team already uses. Delay flags appear inside the tool your PM opens every morning, with no parallel system to maintain.
Site and portfolio dashboards
A project-level view for the site team and a portfolio rollup for the construction director — the same underlying data, two audiences, no manual re-export between them.
Receipts
Where delay prediction is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; we will not publish a number before it is verified.
Delay prediction across a multi-phase civil programme
A multi-phase infrastructure contractor in the Gulf was managing schedule risk through weekly programme reviews — by which point the delay had already compounded. Banao built a prediction layer over their P6 schedules, procurement logs, and site daily reports that flags at-risk milestones and names the driver before the date would have moved.
Dogfooding
We run our own company on the AI we sell
Banao operates a ~300-person engineering company on its own AI products before any client sees them. InterviewGod screens our own hires. Vikaas runs our own demand generation.
Managing delivery across 300 engineers and five offices means tracking commitments, resource allocation, and programme risk in ways a construction director would recognise. We build delay prediction to the standard we hold our own delivery to.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When delay prediction won't pay back
We will tell you before you spend on a model that the problem needs a different answer:
- Single short project: on a one-off build under six months without historical programme data to train on, a trained planner and weekly look-ahead meetings are cheaper and more accurate than a model.
- Schedule data that isn't kept current: delay prediction is only as accurate as the progress data it reads. If the site team doesn't update actuals in P6 or MS Project consistently, the model predicts from stale inputs — and we will say so in the Sprint, not after the build.
- Delay driven entirely by factors outside your control: when slippage is dominated by regulatory approvals, utility diversions, or neighbour disputes, a model can name the right problem but can't accelerate the resolution. We scope what it can and can't do before you commit.
How we start
How we start — prove the signal before the build
Schedule prediction only earns its keep if the model has enough signal to beat your planner's call. We establish that first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your schedule data, progress logs, and procurement records, test whether the signal is strong enough to predict past delays in hindsight, and hand back a baseline accuracy estimate and ROI model — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Data pipeline first: we connect to your project-controls software, site daily reports, and procurement system. Then the model — trained on your historical programme data, calibrated against your planner's judgement, and integrated so alerts appear where your PM already works.
- 03
Production & continuous improvement
Live deployment with a site dashboard, PM alerts, and a portfolio rollup for the construction director. The model recalibrates with each completed project, so prediction accuracy compounds across your programme.
FAQ
Frequently asked questions
What schedule and project data does the model need?
A baseline schedule (P6 or MS Project), actual progress updates, and procurement logs are the minimum. Weather, sub-contractor commitment records, and site daily reports improve accuracy. The Discovery Sprint audits what you have and establishes the signal quality before we build anything.
How far ahead can the model flag a delay?
Typically two to six weeks before a milestone date would move, depending on how far upstream the leading indicator sits in your schedule. Procurement delays tend to give the longest lead time; site productivity losses give shorter but still actionable advance notice. The Sprint establishes the lead time for your specific programme.
Does it work with Primavera P6 and MS Project?
Yes. Banao integrates directly with P6 and MS Project as the schedule data source and writes delay flags back into the tool your PM already opens. There is no parallel system to maintain alongside your existing project controls.
How is this different from the schedule health reports our PM tool already produces?
Standard PM tools report current status — they tell you what has already slipped. The Banao model reads leading indicators (procurement pipeline, sub-contractor progress versus commitment, float consumption rate) to flag what is going to slip weeks out, and names the driver so the PM can intervene before the date moves.
Do PMs have to change how they work day to day?
The model reads from data your site team already produces — daily reports, schedule updates, procurement logs. The only change is that delay alerts appear in the PM's existing tool rather than surfacing in a Friday programme review. Change management for the site and planning team is included in the deployment deliverable.
Get started
Find out how far ahead your schedule data can see
Bring your last project programme and your biggest delay source. In 45 minutes we'll tell you whether prediction is viable on your data — and what it would take to put named-driver alerts in your PM's hands.
Book a Discovery Sprint