[ Reading documents, counting things, spotting faults ]
Document capture, quality inspection, counting and monitoring — built for the light, the lens and the mess you actually have, not for a clean dataset. Most vision projects fail on image conditions long before they fail on the model.
Document capture, quality inspection, counting and monitoring — built for the light, the lens and the mess you actually have, not for a clean dataset. Most vision projects fail on image conditions long before they fail on the model.
Send us two hundred photos from your actual setupYour lighting, your camera angle, your glare and your dust. A model trained on stock images meets reality once and stops being used.
Missing a defect and stopping a good batch cost different amounts. The threshold is set from your numbers, not left at the default.
On-device where bandwidth, latency or privacy demand it; central where model updates matter more. That decision drives everything else.
Low-confidence frames and documents go to a person, and their decision becomes training data. Accuracy improves because the loop is closed.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Invoices, lab reports, IDs, forms and handwritten notes turned into structured records — including the photographed, skewed and creased ones that stock OCR gives up on.
Surface defects, missing components, label and packaging checks on the line, with the threshold set from what a miss actually costs you.
People, vehicles, stock and shelf state counted from existing cameras, aggregated into numbers a manager can act on rather than footage nobody watches.
A few hundred from your actual setup before anything is promised. Glare, blur, angle and occlusion decide feasibility, and they are visible in an afternoon.
Often the cheapest accuracy gain is a light, a mount or a lens — not a better model. We would rather move a camera than spend six weeks compensating for it.
A written labelling standard and agreement checks between labellers. Inconsistent labels put a hard ceiling on accuracy that no amount of training removes.
Fine-tune a detection or OCR model on your data, then set the confidence threshold from the relative cost of a false positive and a miss in your process.
Quantised to the edge device, or served centrally with the frames buffered. Either way it survives a network drop without losing the day's work.
Uncertain cases reviewed by a person, those decisions collected, and the model retrained on them. Conditions drift with the seasons and the shift pattern.
Computer vision projects rarely fail because the model was not good enough. They fail because the camera was pointed at a reflective surface, or the light changes at four in the afternoon, or the labelling was inconsistent so the model learned two contradictory things. The model is the easy part now; the conditions are the project.
So we ask for a few hundred real images before quoting, and the first recommendation is frequently a lamp and a bracket rather than a bigger model. It is a cheaper way to get the accuracy, and it is the sort of thing you only suggest if you have watched one of these go wrong.
A fixed mount, consistent light and the right resolution routinely beat weeks of training on bad frames.
If two people label the same image differently, no model can be more consistent than they were. The standard comes first.
Low-confidence cases route to review, and the review becomes the next training set. That loop is what makes accuracy climb after launch.
Layout-aware models read an invoice they have never seen before, which removed the per-vendor template maintenance that made these systems expensive to own.
For rare or unstructured documents, asking a multimodal model in plain language now beats training a specialised model — though for high volume the specialised model is still far cheaper per page.
A small on-device accelerator now handles detection at full frame rate, which removes the bandwidth cost and the privacy problem of streaming everything to a server.
For faults that occur a few times a month, generated and augmented examples have become a practical way to train a detector without waiting a year for samples.
For a fine-tuned detector on a well-defined object, a few hundred well-labelled examples per class is often enough to start, and consistency matters more than volume. Document extraction with a layout-aware model can need very few. Rare defects are the hard case, and there we usually augment or generate examples rather than wait for them to occur.
Let’s talk about your computer vision project. No obligation, just a conversation.
Next service
AI-Powered Business Transformation