Energy & Oil/Gas · Asset integrity monitoring
Corrosion and fatigue don't wait for your next inspection cycle
Banao builds AI asset integrity monitoring that pulls together inspection photos, drone imagery, ultrasonic readings, and sensor history — then ranks which tanks, vessels, structures, and pipelines carry the most risk right now, not the most time since last survey.
The output is a risk-ranked asset register your integrity engineers can act on: where to send the inspector next, which asset can extend its interval, and which one cannot wait.
The first call is free · 45 minutes · no obligation
What we build
What a Banao asset integrity deployment includes
Integrity monitoring is not a single data feed. It is disparate inspection records, sensor streams, and engineering judgement combined into a ranked, defensible risk picture — we build the model and the workflow around your team.
Multi-source data ingestion
Inspection reports, drone and UT thickness readings, corrosion-under-insulation surveys, and continuous sensor feeds from corrosion coupons and acoustic emission monitors — pulled from paper, spreadsheets, and historian tags into a single risk model.
Risk-based inspection ranking
Every tank, vessel, and structural element scored by probability of failure combined with consequence of failure — so inspection budget goes where the risk-weighted cost of getting it wrong is highest, not where the inspection calendar lands next.
Degradation rate modelling
Models trained on your asset's corrosion history and process chemistry estimate how fast wall thickness is declining, giving your integrity team a defensible remaining-life projection rather than a static design figure.
Anomaly detection on continuous sensor streams
Acoustic emission, guided wave, and corrosion coupon streams monitored in real time — flagging an acceleration in degradation rate between inspection intervals, not just at the annual or biennial survey.
Inspection interval optimisation
Assets with stable, low-risk profiles are identified for interval extension; assets showing deviation are flagged for early survey. The result is the same inspection headcount covering greater risk exposure.
Regulatory and audit-ready records
Inspection findings, risk scores, and remaining-life estimates stored in a format that maps to API 510, API 570, API 653, and your site's statutory reporting requirements — not a proprietary schema you cannot export.
Receipts
Where this pattern has been deployed
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Risk-based ranking replaced calendar-driven survey scheduling
Inspection scheduling was calendar-driven: every tank surveyed on the same cycle regardless of process chemistry, age, or previous findings. Banao integrated UT thickness histories, corrosion monitoring logs, and process data into a risk-ranked register that directs inspection resource to where failure probability and consequence are highest.
Dogfooding
We run our own operation on AI before we deploy it on yours
Banao operates a ~300-person engineering company on its own AI products. InterviewGod screens every engineering hire before a recruiter reads a resume; Vikaas runs our own demand-generation pipeline. We feel a system that fails before you do.
Asset integrity is high-stakes: a missed call has regulatory and safety consequences. The same standard we hold our own production systems to — operational proof before deployment, not a proof-of-concept frozen after the demo — is the standard we apply to every integrity model we build.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When AI asset integrity monitoring is not the right next step
Data quality and inspection programme maturity both have to be sufficient before a risk model adds value. We will say when they are not:
- No inspection history in digital form: if thickness readings and inspection reports live in paper files or personal spreadsheets, the first project is digitisation. We can scope that work, but the risk model comes after.
- Single failure mode already known: if one specific corrosion mechanism or fabrication defect is the cause of all your incidents and the fix is a physical intervention, a statistical model will not accelerate the resolution.
- Very young asset fleet: assets without enough operating history to have accumulated meaningful corrosion data have limited signal for a degradation model. Sensor-based continuous monitoring may be the right entry point instead.
How we start
How we start — baseline risk before you build a model
We do not quote a risk-ranking system off a list of your assets. We audit your actual inspection records and sensor coverage first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We pull a sample of your inspection history, corrosion data, and sensor feeds, run a preliminary risk ranking across your asset population, and hand back a data-readiness assessment and go/no-go with ROI model — yours to keep. If you proceed, the Sprint fee is credited against the build.
- 02
Build
Integrate your inspection management system, sensor historians, and process chemistry data; train the degradation and risk models; and build the ranked asset register with a review workflow your integrity team can run without a data scientist in the room.
- 03
Production & continuous monitoring
Live risk register with continuous sensor ingestion, inspection findings feeding back into the model, and a scheduled risk review cadence. Regulatory record export is part of the production system, not a separate step.
FAQ
Frequently asked questions
What inspection data formats does the model work with?
We ingest thickness measurement records, inspection reports, and corrosion logs from whatever format they exist in today — CSV exports from inspection management software, PDFs, structured XML from UT tools, or spreadsheets. The Discovery Sprint catalogues your data sources and maps a practical ingestion path.
Which standards does the risk scoring align to?
The risk model is built around API RBI (API 580/581) principles — probability of failure times consequence of failure — and can be mapped to your site's existing RBI programme or a new one. Outputs are structured to support API 510, 570, and 653 inspection records and regulatory submissions.
How does the model handle assets with sparse inspection history?
Where asset history is thin, the model draws on degradation rates from similar assets, process chemistry inputs, and industry corrosion data as priors. The Discovery Sprint identifies which assets are data-rich enough for a trained model and which need a sensor-led approach or staged data collection.
Can it extend inspection intervals where risk is demonstrably low?
Yes — interval extension for low-risk assets is one of the primary financial returns. The model provides a documented, risk-ranked basis for extending intervals that your integrity engineer reviews and signs off. That audit trail is part of the deliverable, not an afterthought.
Does this replace our existing RBI software?
Not necessarily. Banao builds an AI layer that can sit alongside existing RBI tools, improving the quality of inputs and the consistency of risk scoring — or we build a full risk-ranking system where no RBI programme exists. The Sprint establishes which fits your programme and statutory obligations.
Get started
Tell us which asset keeps your integrity team up at night
Bring your worst corrosion problem and the inspection history behind it. In 45 minutes we will tell you whether a risk model can rank it — and what an AI-driven integrity programme would cost to build.
Book a Discovery Sprint