Computer Vision & OCR

Document intake, defect detection, ID verification, and visual inspection — built with the right blend of off-the-shelf models, custom training, and traditional CV where it’s simply faster and cheaper.

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Computer Vision & OCR

Vision systems that turn pixels into reliable decisions.

Pragmatic vision systems mixing the right tools for the job, with measurable accuracy and a clear cost story.

Common signs your team is overdue for computer vision & ocr:

  • OCR pipelines that work in demos but choke on real-world scans
  • “It worked on the training set” — accuracy collapses on edge cases
  • Models without an eval suite — quality regressions go unnoticed
  • Vendor APIs that are accurate but too expensive at scale

What we build for computer vision & ocr:

  • OCR + LLM extraction pipelines (Tesseract, Textract, Document AI)
  • Custom detection / segmentation / classification models when off-the-shelf isn’t enough
  • Active-learning loops to collect hard cases for retraining
  • Per-class accuracy dashboards and drift alerts
  • Edge deployment when latency or privacy demands it
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Capabilities

Real-world applications

Pragmatic vision systems — outcomes our clients keep coming back for.

Document intake

Invoices, contracts, forms — extract structured data with field-level confidence scores.

ID verification

Document + selfie checks for KYC/onboarding flows.

Defect detection

Spot defects on production lines with custom-trained models.

Visual catalog

Auto-tag product images, detect duplicates, generate descriptions.

How we deliver

From pixels to production

01

Sample real data

Real images, real conditions. Test off-the-shelf options before building anything custom.

02

Define accuracy

Per-class targets, false-positive vs false-negative trade-offs.

03

Build & evaluate

Iterate on data, model, and post-processing — measured against the eval set.

04

Deploy

Cloud, edge, or hybrid. Latency, cost, and privacy drive the choice.

Tools & platforms we use:

AWS Textract Azure Document AI Google Document AI Tesseract PaddleOCR YOLO PyTorch OpenCV Hugging Face

FAQ

Questions teams ask us about Computer Vision & OCR

Can we run this without sending data to the cloud?
Yes. We deploy on-prem or on-device when privacy or latency demands it. Open-source models cover most use cases.
How much labeled data do we need?
Sometimes none — pretrained models work out of the box. When fine-tuning is needed, we use active learning so you label only the examples that matter.
How long does it take to get to production?
Most projects ship a real, usable system in 3–6 weeks. Discovery is 1–2 weeks; build sprints are weekly with demos.
Will my data be used to train models?
No. We default to enterprise tiers (OpenAI, Anthropic, Bedrock, Vertex) that don’t train on your data. For sensitive use cases, we deploy open-weight models on your infrastructure.
How do you control costs?
We design cost-aware from day one — model routing (cheap model first, escalate when needed), caching, batch processing, and per-user budgets with alerts.
Can you work with our existing engineering team?
Yes. We embed alongside your team, transfer ownership progressively, and document everything we build.