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NDA project
Mobile feature that recognizes products through the camera in real time — without relying on cloud processing
Retail / Mobile Commerce
USA
Mobile (iOS / Android)
Turnkey Product Development
The client was building a mobile app for in-store product discovery.
The idea was simple: user points the camera → app recognizes the product → shows details, alternatives, and offers. In reality, it didn’t work that smoothly. The first version relied on cloud inference.
It worked well in demos — but failed in real stores:
Operational Pain: Users didn’t wait for recognition results → feature adoption was low
Technical Limitation: Existing ML model required server-side processing
Business Risk: A key feature of the app wasn’t delivering value → risk of losing competitive edge
Product Constraint: Recognition had to feel instant to be usable in a retail environment
Instead of trying to optimize backend latency, we moved inference directly onto the device.
We took the existing model and:
The model was embedded into the mobile app, allowing real-time predictions directly from the camera stream.
This removed dependency on network conditions entirely.
1x
ML
Engineer
1x
QA
Engineer
1x
Mobile Developer
1x
Project Manager
1x
ML
Engineer
1x
QA
Engineer
1x
Mobile Developer
1x
Project Manager
On-device ML model optimized for real-time recognition
Mobile integration with live camera input
Inference pipeline running locally on device
Performance tuning for different device types
Fallback logic for low-confidence predictions
Full breakdown is available under NDA
Discovery & Planning: 3–4 weeks
Architecture & Design: 4–6 weeks
Development: 4–6 months
Total Timeline: ~6–8 months
Discovery Phase: $25K–$40K
Design & Architecture: $40K–$70K
Development: $180K–$320K
Total Investment: $300K–$450K
Share a few details — we'll map scope, timeline, and cost
Recognition time reduced from seconds to near-instant
Removed dependency on backend inference and network quality
Higher feature adoption and longer in-app engagement during in-store usage
