Computer vision

Computer vision covers a range of tasks: classification (is this a damaged parcel?), detection (where are the defects on this panel?), segmentation (which pixels are the tumour?), optical character recognition and layout analysis (what does this invoice say and where?), and face or document verification. Modern systems use convolutional or transformer-based models, often fine-tuned from pre-trained weights on a few hundred to a few thousand labelled examples from the client's own environment, because lighting, cameras and document formats differ from the public datasets.

In business settings two families dominate. Document vision extracts structured data from scans, PDFs and photos: KYC documents, invoices, bank statements, claim forms. Physical vision inspects goods and spaces: quality control on production lines, shelf compliance in retail, damage assessment for logistics and insurance. Both need a labelling effort, an accuracy target set per field or defect class, and a confidence threshold that routes uncertain cases to a human.

It is not a solved commodity that works out of the box on any image. Generic APIs are a reasonable start for common documents and objects; accuracy on your specific forms, products and camera conditions comes from your data. It is also distinct from multimodal LLMs, which can describe images well but are not the right tool for high-volume, high-precision extraction or inspection.

• Operations head: it removes manual re-keying and manual inspection, which are slow, error-prone and impossible to scale at peak.
• Compliance officer: for KYC and claims, extraction accuracy and the audit trail of what was read from which document are the controls that matter.
• CTO / Head of Engineering: the project is mostly data labelling, threshold tuning and exception handling; the model itself is a small share of the work.
• CFO: cost per document or per inspection, including human review of exceptions, is the number to compare against the current process.

Computer vision exists because a large share of business information arrives as pixels: photographed documents, scanned forms, camera feeds from a warehouse or line. Humans reading and inspecting them is slow, inconsistent and does not scale to peak volumes, and rules cannot parse a crumpled, skewed photo. Models trained on real examples can, with measurable accuracy. The trade-off is the labelling effort and the need for exception handling: no model is perfect, so the design must route low-confidence cases to people and feed their corrections back, or the system quietly accumulates errors.

• Extracting fields from Aadhaar, PAN and address proofs for an NBFC's onboarding with exceptions routed to reviewers.
• Reading supplier invoices and matching them to purchase orders in a manufacturer's ERP.
• Inspecting garments or components for defects on a production line before packing.
• Assessing vehicle damage from customer photos for an insurer's claims triage.
• Verifying proof-of-delivery photos and detecting damaged parcels in a logistics network.
• Checking planogram and shelf compliance from store photos for a retail chain.

SkillA specialist ML skill area you hire for or partner on. Eazyware delivers document and inspection vision under AI and ML development, with per-field accuracy targets, confidence-based human review and a labelling process the client owns.

Eazyware service that covers it: AI/ML Development. Starting prices are on the pricing page.