Read what's in the image, the video, and the scanned PDF.
Vision-language models in 2026 are good enough that 'extract X from this image' is no longer a research project. We build production pipelines for document understanding, product imagery, manufacturing QA, and logistics - using VLMs when they fit, classical computer vision when it's cheaper, and both when the problem deserves it.
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Hours per day spent reading what nobody has time to read.
A meaningful slice of business operations runs on images and PDFs nobody has time to read carefully - invoices, contracts, product photos, shipment manifests, security footage, customer-uploaded receipts. The work eats hours per day; outsourcing it adds latency without solving the cost long-term.
Off-the-shelf OCR catches the easy cases. Modern VLMs catch the rest, if you build the pipeline carefully - with verification, edge-case handling, and clear escalation when the model is not sure.
Six things every vision pipeline gets.
Discovery
Sample your real images / documents / video. Identify the variation that breaks naive approaches.
Pipeline design
VLM-only, classical CV, or hybrid - which combination fits your cost, latency, and accuracy target.
Implementation
Typed extraction schema, confidence scoring, human-in-the-loop for low-confidence outputs.
Validation harness
Golden set of 50-500 examples; regression testing on every model update.
Production deployment
Async queue, monitoring, audit log of every decision.
Monthly review
What edge cases are coming through, where to tune, when the model needs a refresh.
Why pure-OCR and pure-VLM both fail - and the hybrid that ships.
Either extreme has a recognisable failure mode. The hybrid pipeline is what survives in production.
- OCR-only - brittle on layout variation, fails on handwriting, mixed scripts, low-quality scans
- OCR-only - no semantic understanding, no reasoning about what the document means
- VLM-only - too expensive at production volume; cost-per-page does not scale
- VLM-only - confidence is hidden inside the answer, hard to gate on
- No audit trail; impossible to investigate when an extraction goes wrong months later
- Classical CV handles the high-volume / easy cases at low cost
- VLM handles the edge cases - rare, expensive, but bounded
- Confidence score per output gates human review
- Cost stays bounded as volume scales
- Audit log + confidence score per decision - debuggable months later
Real vision pipelines in production.
Invoice + receipt extraction
Line items, totals, VAT, party details. Match against POs. Flag anomalies. Books close faster.
Contract intelligence
Pull dates, parties, obligations, renewal terms from PDFs in any layout. Speeds legal review by 3-5×.
Product photo QA
Flag mislabelled SKUs, check that the photo matches the catalogue description, generate alt text + descriptions.
Manufacturing QA
Visual defect detection on production-line photos. Often a hybrid VLM + classical pipeline.
Logistics manifests
Read shipment paperwork, match against ERP records, flag discrepancies before goods leave the warehouse.
Security / monitoring
Video summarisation, event detection, alert generation from a monitored feed.
Every frame of 119 TB of footage, labelled - and searchable by plain-English prompt.
A global events company had a decade of footage - keynotes, interviews, drone passes, b-roll - that nobody could search through. We fine-tuned a vision-language model on the agency's recurring speakers, stage layouts, and branding so it recognised the people and places that mattered. Then we processed the 119 TB archive at 8 frames per second sustained throughput - about 28 days of continuous processing - and built a plain-English search interface on top of the per-frame metadata. Crowd footage stayed inside a strict GDPR boundary: identity is only attached to a frame when the person is on the consent register; everything else is searchable by clothing, posture, scenery, on-screen text, and dominant palette - never by face. New uploads go from drop to fully indexed in under an hour.
Three things people always ask.
What about handwriting?
What about live video?
Can it run on our hardware?
Bring us the documents nobody has time to read.
30-min diagnostic call walks through what you process today and what a hybrid pipeline would cost. Free; written summary either way.