Industries · Pharma & Life Sciences
AI that ships inside a validated process, not around it
Banao builds and deploys AI for pharma and life-sciences operations — packaging and fill-line vision, batch record automation, regulatory document intelligence, and cold chain monitoring — for formulations makers, contract manufacturers, and digital-health teams.
Every system below is built to live in a GxP environment: audit trails, versioned models, and human sign-off where the regulation expects a person. We deliver deployed systems, not a proof of concept that stalls at validation.
CP Plus— line-camera vision applied to packaging and label checks on existing hardware.
The first call is free · 45 minutes · no obligation
What we build
What we deploy in pharma and life sciences
Each of these carries a cost you can already name — rejected packs, deviation backlogs, cold-chain write-offs, or an analyst's week lost to retyping. We start where the loss is measurable.
Packaging & fill-line vision inspection
Defect and presence models on line cameras for vials, blisters, fill levels, and label accuracy — integrated with reject gates and an audit-ready image record for each pack.
Batch record automation
Electronic batch records that pull values from instruments, logs, and forms, flag deviations as they happen, and cut review-by-exception time without losing the paper trail.
Regulatory & quality document intelligence
SOPs, CAPAs, change controls, and submission dossiers made searchable and chat-queryable, so QA stops hunting through PDFs for the one clause that matters.
Cold chain monitoring
Sensor and time-series models that catch temperature excursions and predict failures before a shipment spoils — with the trace a regulator will ask to see.
Pharmacovigilance triage
NLP over adverse-event reports and case intake that classifies seriousness, extracts the structured fields, and routes the cases a human must read to the front of the queue.
Lab data digitization
Instrument outputs, lab notebooks, and CoAs pulled out of paper and screenshots into structured, queryable records your analysts and auditors can both trust.
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-camera vision for packaging and label checks
Banao applies computer vision to existing line and CCTV cameras for packaging, label, and compliance checks — adding an inspection layer onto hardware already on the floor instead of replacing the line. Each decision is logged with the image behind it for audit.
Clinical and case documents structured for downstream review
For a digital-health operation, Banao built document intelligence over clinical and case paperwork — extracting the structured fields, flagging what a clinician must read, and leaving a record of every model decision. The human stays in the loop where the data is sensitive.
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.
That habit matters more in a regulated field than anywhere else: a system we depend on daily has already been pushed on accuracy, traceability, and the boring failure modes. The version that reaches your validated process is one we have already had to trust ourselves.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When pharma AI doesn't earn its keep
Most vendors will sell you a model regardless of whether the regulation, the volume, or the data supports it. We would rather tell you when not to build — it is why quality heads take our second call.
- Low line volume or few SKUs: below a certain throughput, a trained QC inspector is cheaper than a vision pipeline and its validation overhead. We'll say so.
- Validation outweighs the payback: in a GxP setting, computer-system validation is real cost. For a one-off or short-life process, the documentation can exceed the saving — and we'll tell you before you commit.
- No digital signal: if a process is paper-only with no scan, sensor, or instrument feed, week one is digitization, not modelling. We start by giving the data somewhere to come from.
How we start
How we start — fixed-price, low risk
You have been pitched AI by vendors who skip past validation. We start by proving the cost of the problem and how it survives an audit, 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, baseline ROI maths, a validation and data-integrity read per opportunity, and a go/no-go — 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 MES, LIMS, and quality systems — with audit trails and versioning designed in, not bolted on later.
- 03
Production & continuous learning
Deployment with human sign-off where the regulation expects it, a dashboard your QA team will actually open, and change management for the floor and lab. The model keeps improving as each batch and shift adds data.
FAQ
Frequently asked questions
We're in a GxP environment. Can an AI system be validated?
Yes. We build for computer-system validation from the start — documented requirements, versioned models, audit trails, and human sign-off where the regulation expects a person. The model becomes a validated, traceable component, not an unexplained black box bolted onto the line.
We don't have clean data. Can we still start?
Yes. Nobody has clean data, and a regulated process is no exception. 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 you have to meet first.
A vendor ran a pilot that never reached the line. Why is this different?
Most pharma AI dies at validation and adoption — the model works in a notebook, then nobody can document it for an audit or get QA to sign off. We treat validation evidence and change management as deliverables from day one, not problems left for the customer after handover.
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 and the validation read whether or not you continue. Worst case you have a free assessment; best case you have the business case your quality and finance leads both need.
How fast can a system reach the line?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week production rollout, with validation work running alongside rather than after. 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 your plant or lab
Bring your worst source of rejects, deviation backlog, or manual review. In 45 minutes we'll map the AI opportunity, the ROI maths, and what validation will take.
Book a Discovery Sprint