AI Solutions
Cameras that actually tell you something.
Defect detection, counting, OCR and safety monitoring — deployed on the edge where latency and bandwidth matter.
Sound familiar?
- “Quality inspection is manual, so it's sampled rather than complete.”
- “You have hundreds of cameras and nobody watching the footage.”
- “Reading meter photos, invoices or number plates is done by hand.”
Capabilities
What it does.
Defect detection
Surface, dimensional and assembly defects identified on the line.
OCR & extraction
Text from documents, meters, number plates and labels.
Counting & tracking
Objects, people and vehicles counted with movement tracking.
Safety monitoring
PPE compliance and restricted-zone entry alerting.
Edge deployment
Runs on-site, so it works without bandwidth and responds in milliseconds.
Continuous improvement
Misclassifications fed back as training data.
How we deploy it
The path from pilot to production.
Most AI projects die between a good demo and a working deployment. This is the sequence that avoids that.
Assess the images
Lighting, angle and resolution decide feasibility more than model choice. We look before promising.
Label a pilot set
A few hundred labelled examples establishes whether accuracy is achievable.
Prototype and measure
Precision and recall reported honestly, including where it fails.
Deploy to the edge
On-site inference for latency, bandwidth and privacy.
Monitor and retrain
Conditions change — new products, different lighting — and accuracy decays without retraining.
Data & privacy
Where your data goes, plainly.
This is the first thing enterprise procurement asks about AI, and most vendor pages avoid answering it.
- Edge deployment means footage never leaves your premises.
- Faces can be blurred or excluded entirely where identification isn't required.
- Retention limited to what the use case genuinely needs.
- DPDP Act obligations apply to identifiable footage — signage and consent handled.
What it costs to run
Edge hardware runs roughly ₹40,000–₹2,00,000 per camera position depending on the model's demands. Cloud inference suits low-volume batch work; anything continuous belongs on the edge.
Technology
What we build it with.
- PyTorch
- YOLO
- OpenCV
- TensorRT
- NVIDIA Jetson
- Python
- AWS
- Tesseract
We're not tied to a model vendor. Systems are built so a provider can be swapped as capability improves or prices move.
Industries
Where it applies.
For well-lit, consistent imaging with good examples of each defect, 95%+ is achievable. Subtle or rare defects are harder. We run a paid feasibility assessment on your actual images before quoting a build — accuracy claims without seeing your data are worthless.
Often industrial cameras with controlled lighting, because consistency matters more than resolution. Sometimes existing CCTV is sufficient. The imaging setup usually determines success more than the model.
Yes, and it usually should. Edge devices run inference on-site with millisecond latency and no bandwidth cost — only results are sent upstream.
A few hundred examples per defect class to start, more for subtle distinctions. We can begin with a small pilot set to prove feasibility before you invest in full labelling.
Start here
Tell us the process, not the technology.
The best AI projects start from an expensive manual task, not from a decision to use AI. Describe the task and we'll tell you honestly whether this is the right tool.