Pharma & Life Sciences · Cold chain monitoring
A temperature excursion you don't catch in transit is a batch you write off at the dock
Banao deploys machine-learning models over temperature, humidity, and time-series sensor data to detect cold-chain drift before a shipment is compromised — and to generate the documentation chain your quality team needs for any regulatory inquiry.
The system integrates with your existing IoT loggers, warehouse sensors, and 3PL feeds. It runs predictive alerts on deviations rather than alarm-after-the-fact thresholds, and produces audit-ready logs with chain-of-custody context attached to every record.
The first call is free · 45 minutes · no obligation
What we build
What a Banao cold-chain monitoring deployment includes
Passive temperature logging tells you what happened. A predictive model tells you what is about to happen — with enough time to act.
Predictive excursion alerts
Models trained on your sensor history detect drift patterns before thresholds breach, giving your team a window to re-route, quarantine, or escalate before product is lost.
Regulatory-grade audit trail
Every sensor reading, model decision, and human action is timestamped and stored in a structure regulators can read — not reconstructed from spreadsheets after the fact.
Multi-source sensor integration
Pulls data from IoT loggers, warehouse BMS feeds, 3PL API exports, and manual entry forms into a single model input — no custom hardware required if your loggers already transmit.
Lane and carrier benchmarking
The model tracks excursion frequency by lane, carrier, and season, giving your logistics and QA teams the evidence to renegotiate SLAs or retire a lane before a batch recall forces the decision.
Deviation auto-documentation
When a threshold is breached, the system generates a structured deviation record — sensor trace, exposure time, product class, and the regulatory memo that explains which stability data applies.
Receipts
Where this pattern is running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Predictive alerts across refrigerated lanes reduced write-offs
Manual temperature checks and after-the-fact logger downloads left the QA team discovering excursions at the receiving dock. Banao deployed a time-series model over existing logger feeds — excursion alerts now arrive before transit ends.
Dogfooding
We run our own operation on the AI we sell
Banao is a ~300-person engineering company that depends on its own AI products before any client sees them. InterviewGod screens our own engineers; Vikaas runs our own demand generation. Internal pressure hardens the model in ways a vendor demo never does.
Regulated environments raise that bar further. A cold-chain system that has to survive our own QA scrutiny — auditability, alert precision, log fidelity — is the version that reaches your validated process.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When cold-chain AI is the wrong investment
Not every cold-chain problem is a modelling problem. We will tell you which applies before you commit budget:
- Sparse sensor coverage: if your lanes have intermittent or no digital logger data, the first investment is instrumentation — a model with gaps in its input will miss the excursions you're trying to catch.
- Stable, low-complexity lanes: for a single warehouse with one carrier and consistent ambient conditions, tighter threshold alerts on existing loggers may cost less and deliver faster than a predictive build.
- Validation overhead you haven't scoped: in some markets, computer-system validation for cold-chain monitoring is mandatory. We scope that cost early — if it exceeds the write-off avoidance, we'll say so before you build.
How we start
How we start — low risk, fixed price
Cold-chain problems hide in lanes that look fine until a recall. We start by finding which lanes carry the most undetected risk.
- 01
AI Discovery Sprint
2 weeks · fixed price
We analyse a sample of your sensor history, map excursion frequency by lane and carrier, and return a risk-ranked opportunity list, ROI maths, and a validation read — yours to keep whether or not you continue. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Data pipeline from your existing loggers and feeds, time-series model trained on your product classes and thresholds, deviation-documentation generation, and integration with your QMS or LIMS audit system.
- 03
Production & continuous improvement
Deployment with a QA dashboard, alert-routing configuration, and change management for logistics and quality teams. The model re-trains as new lane and carrier data accumulates.
FAQ
Frequently asked questions
Our loggers already record temperature. Why do we need AI on top?
Loggers record what happened. A model detects the pattern that precedes a breach — so you can act while the shipment is still in transit rather than discovering the excursion at the receiving dock. The model also generates the structured deviation record, which most logger exports do not.
What sensor feeds can you integrate with?
Banao integrates with IoT logger APIs, warehouse BMS exports, 3PL data feeds, and manual entry forms. If your logger transmits data digitally — even via CSV export — we can build a pipeline on it. Custom hardware is only specified when existing coverage has genuine gaps.
Can the system produce audit-ready documentation for regulatory inspections?
Yes. Every alert, model decision, and human action is timestamped and stored in a regulator-readable structure with chain-of-custody context attached. Deviation records include the sensor trace, exposure duration, product class, and the stability memo that explains which data applies.
How do we size the ROI before committing?
The AI Discovery Sprint does this. In two weeks, at a fixed price, we analyse your sensor history, rank lanes by excursion risk, and return a write-off avoidance model and validation cost estimate — yours to keep. If the maths don't support the build, we say so before you commit.
How long does a full deployment take?
A typical path is a 2-week Discovery Sprint, 6–8 weeks of build (data pipeline, model, deviation-documentation layer), and a 3–4 week production rollout. Validation work runs alongside the build rather than after it, so it doesn't extend the project end date.
Get started
Find out which lanes are carrying undetected cold-chain risk
Bring a sample of your temperature logs and your worst lane. In 45 minutes we'll show you what a predictive model would have caught — and what validation will take.
Book a Discovery Sprint