Computer vision + AWS cloud
AI warehouse vision and inventory detection platform
An asynchronous AWS computer-vision pipeline that processes warehouse image missions, detects barcodes, text, licence plates, and bin boundaries, then reconciles the results with inventory records.
Platform foundation
The challenge
Manual review of large warehouse image batches was slow, inconsistent, and difficult to audit. The workflow needed dependable orchestration across image storage, queueing, model inference, detection, matching, and result publishing, with no silent failures when a batch was retried.
The solution
We worked on a serverless pipeline where every image mission moves through explicit stages with its own state. Inference runs asynchronously so large batches never block a request, and each stage records enough evidence for an operator to audit the outcome later.
End-to-end workflow
How the product moves work forward
- 01
Ingest images
Accept warehouse image missions into cloud storage and register a job with its metadata.
- 02
Orchestrate jobs
Serverless functions queue work, acquire distributed locks, and guard against duplicate processing.
- 03
Run inference
SageMaker asynchronous endpoints process large image batches without blocking the caller.
- 04
Detect and read
Barcode, QR, text, and licence-plate detectors extract structured values from each frame.
- 05
Reconcile inventory
Multi-image bin analysis matches detections to inventory and publishes structured API results.
Detailed capability map
What was designed into the platform
Roles and permissions
The right workspace for every participant
Permissions support the real operating model instead of giving every user the same controls.
Warehouse operator
Captures image missions and reviews the detections raised for a bin or location.
Inventory controller
Investigates reconciliation discrepancies and confirms or corrects stock records.
Platform engineer
Monitors pipeline health, inference cost, retries, and queue depth.
Operational value
- Replaces manual review of large image batches
- Keeps inference asynchronous so batch size does not break the workflow
- Makes every detection traceable back to its source image
- Surfaces stock discrepancies instead of hiding them in a report
Growth opportunities
- Confidence-based human review queues
- Real-time streaming from handheld devices
- Damage and packaging condition detection
- Warehouse heatmaps and dwell analytics
Build on this experience
Have a workflow that needs its own product?
Tell us what your team does manually today. We will help identify the most useful first version.