The computer vision development company that starts with your dataset

Start with a cloud vision API — AWS Rekognition, Google Cloud Vision or Azure AI Vision — when the task is generic: reading printed text, detecting common objects, moderating content. They're fast to integrate and you pay per call. You move to a custom model when the task is specific to you — your defects, your products, your labels — when off-the-shelf accuracy isn't good enough, or when a per-image cost or a privacy rule rules out sending images to a third party. Often the answer is a mix.

It depends on how subtle the distinction is, but useful models often start in the low thousands of labelled images per class, and fine-tuning a pretrained model needs far less than training from scratch. Having none is a normal starting point — the first phase is a capture and annotation plan, and sometimes staged capture or augmentation covers the rare cases you can't wait to observe naturally. What kills accuracy isn't low volume so much as labels that are inconsistent between annotators.

Data first, deployment target second, accuracy bar third. If the labelled images don't exist yet, capture and annotation is frequently the largest line item; a model that runs on constrained edge hardware takes more work than a cloud endpoint; and moving from decent to reliable takes far more iteration than getting to decent. On timeline, a scored spike on the hardest case is usually a few weeks and a hardened, deployed system a few months — longer with no dataset to start from, because rushing annotation produces a model that fails on exactly the cases you built it for.

Yes, and often it should. Where bandwidth, latency or a privacy rule makes streaming video to the cloud impractical, we build for the edge from the start — exporting the model to ONNX or TensorRT and running it on devices like NVIDIA Jetson beside the camera. That keeps footage on your premises and inference fast enough for a real process. It does constrain model size, which is exactly why the deployment target is decided before the model is chosen, not after.

None honestly, before we've seen your images — anyone quoting a number blind is guessing. What we commit to is measuring it early: a scored spike on the hardest cases, using data from your real environment, tells you the achievable accuracy within the first phase. If it's below what your process needs, we'll say so rather than build it. And accuracy is two numbers, not one — the miss rate and the false-alarm rate trade against each other, and which matters more is your call.

No. Your images stay within your environment and your accounts wherever the architecture allows, and they aren't used for anything outside your project. Trained weights from models we build on your data are yours. Where a cloud vision provider sits in the design we tell you exactly which one, what leaves your premises and under what retention terms before you approve it — and for footage that can't leave at all, edge deployment keeps it on site.

When the signal isn't reliably in the image — if lighting, occlusion or camera position means the thing you care about often isn't visible, no model recovers it. When a sensor is cheaper and more reliable than a camera, use the sensor. When you need a guaranteed exact answer every time, a probabilistic model is a liability. And when you can't capture representative data, the project isn't ready. We'd rather establish that in discovery than bill you to find it out.

Our own in-house engineers in Sahibzada Ajit Singh Nagar, Punjab — 45+ of them, with over a decade of combined delivery experience, delivering for clients in 15+ countries. Nothing is subcontracted. You own the code, the repository, the trained weights and the pipeline from day one, and the person demonstrating the work each sprint is the person who built it.