Industries · Manufacturing
Banao builds and deploys AI on real production lines — computer vision quality inspection, predictive maintenance, and production planning — for ceramics, steel, auto parts, and process manufacturers.
Every system below is in production somewhere, integrated with line PLCs, cameras, and ERP. We sell deployed systems, not Jupyter notebooks.
RAK Ceramics— real-time tile defect inspection deployed on the line, edge-side.
Each of these is a problem with a rupee or dollar attached — scrap, downtime, yield, or labour. We start where the cost is measurable.
Custom defect models on edge cameras, integrated with PLCs and sorting hardware. Real-time grading on ceramics, steel surface, auto parts, packaging.
Sensor and time-series models that flag failures before they stop the line, with a maintenance dashboard your plant team actually opens.
Combinatorial models that cut sheets to minimise scrap against live order specs — for steel, glass, textile, and wood. Operator override built in.
Predictive models over meter and sensor data that surface energy waste before the bill does — for refineries, cement, and heavy industry.
Constraint-based schedulers wired into your ERP, so plan changes and delivery dates stop living in one planner's spreadsheet.
BOMs, drawings, and specs pulled out of PDFs and CAD into a searchable, chat-queryable knowledge base for your engineers.
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.
RAK Ceramics
One of the world's largest ceramic tile makers ran inspection by eye — fatigued, inconsistent, slow. Banao trained a defect model on thousands of crack, glaze, and colour images and deployed it edge-side on line cameras with auto-grading into the conveyor sort.
CP Plus
Banao applies computer vision to existing CCTV and line cameras for safety, compliance, and quality use cases — adding an AI layer to hardware already on the floor rather than ripping it out.
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.
That is the difference between a vendor who has read about production AI and one who depends on it every working day. When a model has to survive our own operation, the version that reaches your floor is already battle-tested.
Most AI vendors will sell you a model regardless. We would rather tell you when not to build — it is why plant heads take our second call.
You have been pitched AI by five vendors already. We start by proving the cost of the problem, not by quoting a build.
On-site if needed. You walk out with a prioritised list of AI opportunities, 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.
Data engineering first, then the model. We build the cleaning pipeline as a deliverable and integrate with your PLCs, MES, and ERP — old kit included.
Deployment with operator override and a dashboard, plus change management for the floor team. The model keeps improving with each shift's data.
Our shop floor is too old for AI. Does that rule us out?
No — it is our specialty. Banao has integrated AI with 1990s PLCs, SCADA systems, and analog sensors via retrofit. The model cares about the data signal, not the age of the machine. We run an integration audit in week one.
We don't have clean data. Can we still start?
Yes. Nobody has clean data. We need some data, not perfect data. The first two weeks of any engagement is data engineering, and the cleaning pipeline is part of the deliverable, not a prerequisite.
We tried analytics with a big vendor and it failed. Why is this different?
Most manufacturing AI dies on operator adoption — the model works in the lab, the floor doesn't trust it. Our delivery includes change management for the floor team as a non-negotiable deliverable, not an afterthought.
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 floor?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week production rollout. Banao's ~300-engineer bench means delivery starts in weeks, not the months a local hire would take.
Bring your biggest source of scrap, downtime, or manual inspection. In 45 minutes we'll map the AI opportunity and the ROI maths behind it.
Book a 45-min scoping call