Industries · Real Estate & Construction
AI that runs on the site, not in the brochure
Banao builds and deploys AI across construction sites and property portfolios — site-camera vision for progress and safety, delay prediction, valuation models, and lease document automation — for developers, contractors, and facility operators.
Every system below runs against real site cameras, drawings, schedules, and lease data, wired into your project controls and ERP. We hand over deployed systems, not a proof-of-concept deck.
CP Plus— site-safety camera vision running on cameras already mounted on the project.
The first call is free · 45 minutes · no obligation
What we build
What we deploy in real estate and construction
Each of these has a cost attached — a slipped handover date, a safety stand-down, rework, or a lease term nobody read. We start where that cost is measurable.
Construction progress monitoring
Site-camera and drone imagery compared against the schedule and BIM model, so a site engineer sees what is actually built versus what was billed — without a manual walk-through.
Site safety monitoring
Vision models on existing site CCTV that flag missing PPE, people in exclusion zones, and unsafe lifts in real time, with alerts the safety officer acts on, not a report read next week.
Property valuation models
Models over transaction history, location, comparables, and asset attributes that put a defensible number on a unit or a portfolio — and show the factors behind it.
Lease & contract document automation
Clauses, rent escalations, renewal dates, and obligations pulled out of lease PDFs into a structured, queryable register — so a 400-page lease stops being a liability nobody tracks.
Project delay prediction
Schedule, procurement, and on-site progress signals combined to flag slippage weeks before a milestone is missed, with the driver named so a PM can act on it.
Facility maintenance prediction
Sensor and building-management data on operational assets that flags HVAC, lift, and pump failures before a tenant logs a complaint, with a work-order feed for the facilities team.
Receipts
Deployed, with names attached
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.
Site-safety vision on cameras already on the project
Banao applies computer vision to CP Plus surveillance cameras already mounted on sites — PPE checks, exclusion-zone breaches, and unsafe-lift detection — adding an AI safety layer to hardware the project already runs rather than installing a parallel system.
Lease registers built straight from PDF contracts
A regional developer with hundreds of commercial and residential leases tracked renewals and escalations by hand in spreadsheets. Banao built a document pipeline that reads each lease, extracts the terms that carry money, and feeds a register the asset team queries in plain language.
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.
A development team and a construction firm both run on dates, documents, and headcount that have to add up. We build to that standard because we hold ourselves to it first — the version that reaches your site has already had to survive our own operation.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When construction AI doesn't earn its keep
Most AI vendors will sell you a model regardless. We would rather tell you when not to build — it is why project directors take our second call.
- One-off project: on a single short build with no portfolio behind it, a site team and a clipboard beat the cost of standing up a vision pipeline. We'll say so.
- No cameras, no sensors, no records: valuation and prediction need history, and site vision needs a feed. If an asset has none of these, week one is instrumentation, not modelling.
- Shifting definitions: if what counts as a safety breach or a billable milestone changes site to site, a fixed model rots faster than it pays back. That needs a different approach.
How we start
How we start — fixed-price, low risk
You have been pitched AI by five vendors already. We start by proving the cost of the problem, not by quoting a build.
- 01
AI Discovery Sprint
2 weeks · fixed price
On-site if needed. You walk out with a prioritised list of AI opportunities across sites and the portfolio, baseline ROI maths, and a go/no-go per opportunity — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Data engineering first, then the model. We build the ingestion pipeline as a deliverable and integrate with your site cameras, project-controls software, BMS, and ERP — existing kit included.
- 03
Production & continuous learning
Deployment with a dashboard and alerts your site and asset teams actually use, plus change management for the people on the ground. The model keeps improving as each project and quarter adds data.
FAQ
Frequently asked questions
Our site cameras are basic CCTV. Can they still run AI?
Usually yes. Banao adds a vision layer to the surveillance cameras already on a project rather than asking you to replace them. The model cares about the feed, not the brand of camera. We run a feed and coverage audit in week one.
Our lease and contract data is a mess of PDFs. Can we still start?
Yes. Nobody hands us a clean register. We need the documents, not a tidy database. The first phase of a document engagement is building the extraction and cleaning pipeline, and that pipeline is part of the deliverable, not a prerequisite.
We tried a PropTech tool and the site team ignored it. Why is this different?
Most construction AI dies on adoption — it works in a demo, the site doesn't trust it. Our delivery includes change management for the site and facilities teams as a non-negotiable deliverable, with alerts wired into the tools they already open.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, you keep the ROI model whether or not you continue. Worst case you have a free assessment; best case you have your board business case.
How fast can a system reach the site?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week rollout. Banao's ~300-engineer bench means delivery starts in weeks, not the months a local hire would take.
Get started
Find out where AI actually pays off across your projects
Bring your worst source of delay, rework, safety risk, or lease admin. In 45 minutes we'll map the AI opportunity and the ROI maths behind it.
Book a Discovery Sprint