AI temperature risk analysis
Every reading is checked against the product's allowed range. Tempquill measures how long and how far a shipment ran out of range, then scores the risk to the product.
→ Know in minutes which loads need attention.
Cold-chain intelligence · now in development
A warm truck shouldn't cost you the whole load.
Tempquill reads your temperature logs and shipping documents, flags the excursions that put product at risk, and drafts the investigation — so quality teams decide release, test or reject with evidence, not guesswork.
TQ-20418 · Insulin pens · Lagos → AbujaSample data
2h 10m above range near Lokoja. A door-open event at 14:05 lines up with the rise. Suggested: quarantine on arrival and request stability data.
Range checks
Every reading vs. product limits
Excursion timing
Duration, peak and location
AI incident drafts
Summary, cause, next step
Audit trail
Every decision recorded
The problem
The evidence exists. It's just in five places.
When a reefer runs warm, QA has to decide fast — but the logger export, the delivery note, the inspection sheet and the driver's WhatsApp message all live apart.
Day 1
Logger file arrives by email. Someone opens it in Excel and eyeballs the curve.
Day 2
QA chases the carrier for the POD and the driver for an explanation.
Day 3
Stock sits in quarantine. Nobody is sure, so good product is written off.
Later
An auditor asks why. The decision was made, but not recorded.
With Tempquill: upload once, see the excursion scored and explained, decide, and the record is already written.
Capabilities
Built around the excursion decision.
Shipment monitoring dashboard
Live and recent shipments by lane, carrier and client, with status and risk in one list.
→ One view instead of ten logger exports.
AI-assisted investigation
For each excursion, AI drafts a plain-language summary, lines up likely causes from logs and documents, and suggests a next step for QA to accept or change.
→ Investigations in hours, not days.
Document processing
Delivery notes, PODs and inspection reports are read automatically: pallet counts, signatures, timestamps and dock waits become structured fields.
→ Evidence attached without retyping.
Audit-ready records
Every decision — release, test, quarantine, reject — is logged with who made it, when and why, and exports as an incident report.
→ Ready when a customer or auditor asks.
How it works
From logger file to signed-off decision.
- 01
Upload
Data-logger exports (CSV/PDF) and shipment documents, per shipment or in bulk.
- 02
Check
Readings are compared with each product's range; excursions are timed and scored.
- 03
Explain
AI summarises what happened, why it likely happened and what to do next.
- 04
Decide
QA approves release, testing or quarantine. The incident record is saved.
Interactive demo
Try the QA workspace.
Search shipments, filter by lane, open an excursion and make a decision. All shipments and readings are fictional sample data.
Medivance & partners · QA workspace
Sample dataShipments
6
Excursions
2
In review
1
Cleared
1
| Shipment | Lane | 24h trace | Risk | Status |
|---|---|---|---|---|
TQ-20418 · Medivance Pharma | Lagos → Abuja | 82 | Excursion | |
TQ-20419 · Northfield Foods | Lagos → Ibadan | 14 | In transit | |
TQ-20420 · Halima Dairies | Kano → Kaduna | 57 | Review | |
TQ-20421 · Medivance Pharma | Apapa CFS → Ikeja | 6 | Delivered | |
TQ-20422 · Northfield Foods | Lagos → Abuja | 71 | Excursion | |
TQ-20423 · Crestline Labs | Lagos → Ibadan | 22 | In transit |
Who it's for
For teams who own what happens after a warm reading.
Pharmaceutical distributors
Vaccines, insulin and biologics with tight 2–8 °C ranges; one excursion can mean a recall conversation.
Score each excursion and keep a defensible release or reject record.
Food processors and distributors
Frozen and chilled stock written off "to be safe" because evidence takes too long to gather.
Release good stock sooner and trace spoilage to its cause.
Refrigerated transport operators
Disputes with shippers over who caused a warm load, with logs and PODs in different places.
Attach logger data and delivery documents to the same shipment timeline.
Cold-storage warehouses and QA teams
Dock waits and door events that quietly push product out of range.
See patterns by dock, lane and carrier and fix the process.
Technology
How We Use Amazon Web Services
Tempquill's proposed AWS architecture is serverless end to end: logger files and documents land in Amazon S3 through Amazon API Gateway, AWS Lambda and Amazon Textract turn them into readings and fields, Amazon Bedrock drafts the incident analysis, and Amazon DynamoDB keeps every shipment and decision.
01 · Data collection
Amazon API Gateway
Receives logger files and document uploads
02 · Secure storage
Amazon S3
Stores raw logs, PDFs and photos
03 · Processing
AWS Lambda · Amazon Textract
Parses readings, reads documents
04 · AI intelligence
Amazon Bedrock
Summaries, causes, recommendations
05 · Application
Amazon DynamoDB
Shipments, incidents, decisions
- Amazon API Gateway: Provides the upload and integration endpoints that receive data-logger files and shipment documents from the web app and, later, from logger platforms.
- Amazon S3: Provides encrypted object storage for raw temperature logs, delivery notes, inspection reports and generated incident reports.
- AWS Lambda: Runs the serverless processing: parsing logger exports, checking readings against product ranges and timing each excursion.
- Amazon Textract: Extracts text, tables and key-value fields such as pallet counts, timestamps and signatures from delivery and inspection documents.
- Amazon Bedrock: Generates incident summaries, likely-cause explanations and recommended next steps from readings and extracted document fields, for QA review.
- Amazon DynamoDB: Holds structured records for shipments, products and ranges, excursions, QA decisions and the audit trail.
Pricing
Simple plans in naira.
Monthly pricing per workspace. Early-access customers are onboarded directly by the founder.
Starter
₦10,000 / month
A single depot or small fleet uploading logs manually.
- —Temperature log and shipment uploads
- —Excursion detection against product ranges
- —Shipment monitoring dashboard
- —Email alerts
Growth
₦20,000 / month
Distributors running daily refrigerated routes.
- —Everything in Starter
- —AI incident summaries and likely causes
- —Document extraction from delivery and inspection notes
- —Team review queue
Enterprise
₦40,000 / month
Multi-site operators and pharmaceutical quality teams.
- —Everything in Growth
- —Audit-ready compliance reports
- —Multiple sites, lanes and carriers
- —API access and priority onboarding
Team
The Team Behind Tempquill
SA
Sanni Sideeq Ali
Founder
Sanni founded Tempquill to give cold-chain operators a faster, evidence-based way to decide what happens to a shipment after a temperature excursion. He leads product, engineering and early conversations with distributors and quality teams.
Early access
Bring us a shipment you had to investigate.
Tempquill is building its initial product with a small group of distributors and QA teams. Tell us about your cold chain and we'll be in touch as early access opens.
Contact
Talk to the team.
sanni@tempquill.site linkedin.com/company/tempquill
Product questions, pilots, partnerships or press — the founder replies directly.