Industries · Automotive
AI on the assembly line and the dealer lot, not in a slide deck
Banao builds and deploys AI across the automotive value chain — assembly-line and paint-shop vision inspection, predictive maintenance on line robots, warranty-claims analysis, and dealer inventory planning — for OEMs, tier-1 suppliers, and dealer networks.
Every capability below runs against real line cameras, PLCs, dealer management systems, and warranty databases. We deliver deployed systems, not proof-of-concept notebooks.
CP Plus— vision on existing line cameras for assembly and paint-shop inspection.
The first call is free · 45 minutes · no obligation
What we build
What we deploy in automotive
Each of these maps to a cost line — rework, warranty payout, downtime, or stranded inventory. We start where the number is measurable.
Assembly-line vision inspection
Custom defect models on line cameras that check panel fit, gaps, weld points, and missing fasteners within cycle time, wired into the PLC so a fault flags or holds the unit before it moves down the line.
Paint-shop defect detection
Vision models trained on runs, sags, orange peel, and dust inclusions under booth lighting. Every body is graded before it leaves the booth, so rework is caught wet instead of at final inspection.
Predictive maintenance for line equipment
Sensor and vibration models on welding robots, presses, and conveyors that flag a failing spindle or motor before it stops the line, with a maintenance view the plant team actually opens.
Warranty-claims analysis
Models over claim text, repair codes, and dealer notes that group failures into patterns, catch duplicate and fraudulent claims, and surface a component defect weeks before a recall review would.
Dealer inventory & demand planning
Demand models wired into your DMS that match stock and trim mix to each dealer's real sell-through, so capital stops sitting on slow variants and the fast ones stop going out of stock.
Supplier & supply-chain risk monitoring
Models over supplier delivery, quality, and external signals that flag a part shortage or quality drift early enough to re-source — instead of finding out when the line goes down.
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.
Line vision on existing camera infrastructure
Banao applies computer vision to existing line and CCTV cameras for assembly and paint-shop inspection — adding an AI grading layer to hardware already mounted over the line rather than ripping it out and re-cabling the booth.
Warranty claims read as patterns, not paperwork
An OEM with a national dealer network processed warranty claims by hand, so a recurring component defect only surfaced after months of payouts. Banao built a model over claim text, repair codes, and dealer notes that clusters failures into defect patterns and flags duplicate and fraudulent claims as they arrive.
Dogfooding
We run our own company on the AI we sell
Banao runs a ~300-person engineering company on its own AI before any client sees it. InterviewGod screens our own hires. Vikaas runs our own demand generation.
That is the gap between a vendor who has read about production AI and one whose payroll depends on it. By the time a model reaches your line or your dealer network, it 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 automotive 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 plant and after-sales heads take our second call.
- Low build volume: on a line running a few hundred units a shift, a trained inspector can beat a vision pipeline on cost. We'll say so before you spend.
- Churning trim and tolerances: if your model mix and spec change every few weeks, a fixed inspection model decays faster than it pays back, and needs a different setup.
- No usable signal: we don't need perfect data, but a process with no camera, sensor, or claim log gives a model nothing to learn from. Week one there is instrumentation, not modelling.
How we start
How we start — fixed-price, low risk
You have been pitched AI by several 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 at the plant or across the dealer network 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.
- 02
Build
Data engineering first, then the model. We build the cleaning pipeline as a deliverable and integrate with your line PLCs, MES, DMS, and warranty databases — older kit included.
- 03
Production & continuous learning
Deployment with operator override and a dashboard, plus change management for the floor and after-sales teams. The model keeps improving with each shift's images and each month's claims.
FAQ
Frequently asked questions
Our line PLCs and DMS are old. Does that rule us out?
No — it is our specialty. Banao has integrated AI with 1990s PLCs, legacy SCADA, and older dealer management systems via retrofit. The model cares about the data signal, not the age of the kit. We run an integration audit in week one.
Our warranty data is messy and split across dealers. Can we still start?
Yes. Warranty data is always messy — free-text repair notes, inconsistent codes, gaps per dealer. We need some data, not clean data. The first two weeks of any engagement is data engineering, and that pipeline is part of the deliverable, not a prerequisite.
We tried a vision vendor and operators ignored it. Why is this different?
Most line AI dies on operator trust — the model works in a demo, the floor overrides it and stops looking. Our delivery includes change management for the floor team and an operator-override path as non-negotiable deliverables, not afterthoughts.
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 line or the dealer network?
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.
Get started
Find out where AI actually pays off in automotive
Bring your biggest source of rework, warranty payout, downtime, or stranded inventory. In 45 minutes we'll map the AI opportunity and the ROI maths behind it.
Book a Discovery Sprint