Industries · Oil & Gas
AI deployed on live wells, pipelines, and process units
Banao builds and deploys AI across upstream, midstream, and downstream operations — pipeline anomaly detection, predictive maintenance on rotating equipment, refinery process optimization, and HSE monitoring — wired into your historians, SCADA, and control systems.
Every system below runs against real telemetry, not a sandbox export. We hand over deployed monitoring, not a proof-of-concept notebook.
Indian Oil— AI applied to downstream operations data, integrated with existing plant systems.
The first call is free · 45 minutes · no obligation
What we build
What we deploy in oil & gas
Each of these is a problem with a barrel or a fine attached — downtime, spill risk, off-spec yield, or a safety incident. We start where the cost is measurable.
Pipeline anomaly detection
Pressure, flow, and time-series models that flag leaks, blockages, and corrosion signatures across gathering lines and trunk pipelines — before a spill or a regulator does. Wired into your SCADA tags.
Predictive maintenance for rotating equipment
Vibration and sensor models for pumps, compressors, and turbines that call a bearing or seal failure weeks out, so a planned shutdown replaces an unplanned trip. Alerts land in the maintenance system your crew already uses.
Refinery & process optimization
Models over historian data that hold units closer to optimal yield and energy draw, surfacing giveaway and off-spec batches operators cannot watch around the clock. Operator override built in.
Energy demand forecasting
Forecasting over load, weather, and production data so trading, generation, and procurement teams plan against a number instead of last year's spreadsheet.
HSE & compliance monitoring
Computer vision on existing CCTV for PPE, restricted-zone, and hot-work compliance — an AI layer on cameras already mounted, not a new hardware rollout.
Asset integrity monitoring
Inspection photos, drone imagery, and sensor history pulled into a model that ranks which tanks, vessels, and structures need attention first — so integrity budgets go where the risk actually is.
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.
AI applied to downstream operations data
One of India's largest oil & gas operators runs vast volumes of downstream telemetry. Banao applies anomaly and pattern models to that operations data, integrated with existing plant systems rather than a parallel stack, to surface equipment and process issues earlier.
Industrial vision on cameras already on the gantry
Banao runs computer vision on existing CP Plus CCTV for safety and compliance use cases — PPE, exclusion zones, and hot-work — adding an AI layer to hardware already mounted across the site instead of a new camera programme.
Dogfooding
We run our own company on the AI we sell
Banao operates a ~300-person engineering company on its own AI products before a client ever sees them. InterviewGod screens our engineering hires. Vikaas runs our own demand generation.
That is the gap between a vendor who has read about production AI and one who depends on it to run payroll. A model that has to hold up inside our own operation reaches your assets already hardened.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When oil & gas 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 integrity heads take our second call.
- Stable, well-tuned units: if a control loop already holds tight to spec, an AI layer adds cost without moving the number. We'll say so.
- Rarely-run equipment: a model needs repeated cycles to learn. A unit that fires a couple of times a year won't generate the signal to justify a build.
- No telemetry: we don't need a perfect data lake, but we need a signal. If a pump has no sensor and no log at all, week one is instrumentation, not modelling.
How we start
How we start — fixed-price, low risk
You have sat through AI pitches from the majors and three startups. We start by pricing the problem, not by quoting a build.
- 01
AI Discovery Sprint
2 weeks · fixed price
On-site at the plant if needed. You leave with a ranked list of AI opportunities, baseline ROI maths per opportunity, and an honest go/no-go — yours to keep either way. Proceed, and the Sprint fee is credited against the build.
- 02
Build
Data engineering first, then the model. We treat historian cleaning and tag mapping as a deliverable and integrate with your SCADA, DCS, and historian — legacy kit included.
- 03
Production & continuous learning
Deployment with operator override and a monitoring dashboard, plus change management for the control-room and field crews. The model keeps sharpening as each run adds data.
FAQ
Frequently asked questions
Our plants run legacy SCADA and historians. Will AI integrate?
Yes — that is the normal case. Banao has connected models to PI-style historians, legacy DCS, and analog field instruments via read-only taps. The model needs the data signal, not a rip-and-replace. Week one is an integration audit.
Our process data is noisy and full of gaps. Can we still start?
Yes. No operator has clean historian data. We need some signal, not a perfect archive. The first stretch of any engagement is data engineering, and the cleaning and tag-mapping pipeline is part of the deliverable, not a precondition.
An analytics pilot stalled before it reached the field. Why is this different?
Most oil & gas AI dies on control-room trust — the model looks right in a notebook, the board operator ignores it. Our delivery includes change management for the control room and field crews as a fixed deliverable, with operator override on every alert.
How do we justify the spend to the board 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 of where AI does and doesn't pay; best case you have your capital business case.
How fast can a monitoring system reach a live asset?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week rollout in shadow mode before it goes live. Banao's ~300-engineer bench means work starts in weeks, not the months a new hire would take.
Get started
Find out where AI actually pays off across your assets
Bring your worst source of downtime, your trickiest pipeline segment, or your HSE backlog. In 45 minutes we'll map the AI opportunity and the ROI maths behind it.
Book a Discovery Sprint