Build or fine-tune or use an API?

Depends on accuracy targets, data volume, latency and cost. Off-the-shelf APIs (Google Vision, AWS Textract) for common cases, fine-tuned models for domain-specific accuracy, and full custom builds for novel use cases. We recommend in the audit.

How much data do we need?

For fine-tuning: 100-1,000 labelled examples for narrow tasks, 5,000+ for broader ones. For OCR or document AI, we can often start with pre-trained models and 50-100 annotated examples.

Can you deploy on edge devices?

Yes — models quantised to ONNX, TensorRT or TFLite for Jetson, Raspberry Pi, mobile or in-browser via WebGPU. Useful for offline, latency-sensitive or privacy-constrained workloads.

How do you handle model drift?

We monitor prediction confidence and accuracy on a sample of production data, alert on drift, and run a scheduled retraining cadence — quarterly for stable domains, monthly for fast-moving ones.

How much data do you need to train a custom vision model?

For fine-tuning on a pre-trained backbone, 100-1,000 labelled images for narrow tasks (defect detection, document classification) and 5,000+ for broader multi-class problems. We can often start with a public model and 50-100 annotated examples to validate the use case.

Can computer vision run on edge devices or only in the cloud?

Both. We quantise models to ONNX, TensorRT or TFLite for Jetson, Raspberry Pi, mobile or in-browser via WebGPU. Edge deployment suits offline, latency-sensitive or privacy-constrained workloads — cloud suits heavy models and bursty traffic.

How accurate are OCR and document AI models in production?

Modern OCR hits 95-99% on printed text and 85-92% on handwriting, depending on language and scan quality. Document AI pipelines that combine OCR with LLM-based extraction reach 95%+ on structured fields — we measure accuracy per field, not per document.

How do you handle model drift in production?

We monitor prediction confidence and a sample of production data, alert on accuracy drop, and run a scheduled retraining cadence — quarterly for stable domains, monthly for fast-moving ones. Drift is expected; what matters is catching it before users notice.

AI & Automation

Computer Vision & NLP

We build computer vision and NLP systems that work in production — OCR, object detection, document AI and text classification, trained on your data and deployable on cloud or edge. No 'we trained a model' demos — you get measured accuracy, an eval harness and a deploy plan.

📊 At a glance

CV tasks: Detection · OCR · segmentation
NLP tasks: NER · sentiment · classification
Frameworks: PyTorch · ONNX · OpenCV
Edge deploy: Jetson · mobile · browser

What is Computer Vision & NLP?

What is computer vision and NLP? Computer vision and NLP is the engineering of AI systems that interpret images, video and text, covering classification, OCR, object detection and entity extraction. ClickTake delivers computer vision and NLP services for UK enterprises, using PyTorch and HuggingFace to ship models with labelled evals and drift monitoring.

What You Get

Built around outcomes.

Every engagement ships with these deliverables baked in — not bolted on later.

✅

Trained on your data

Models fine-tuned on your real documents, images or text — not a generic ImageNet classifier that breaks on your edge cases.

⚡

OCR & document AI

Tesseract, PaddleOCR or fine-tuned Donut/LayoutLM for invoices, contracts, forms and ID documents — with structured extraction, not just text.

🚀

Object detection

YOLO, DETR or fine-tuned models for retail inventory, defect detection, safety monitoring and queue analytics.

🎯

NLP & classification

Text classification, NER, sentiment and topic modelling for support triage, content moderation and contract analysis.

🛡️

Edge deployment

Models quantised and deployed to edge devices — Jetson, mobile or browser via ONNX/WebGPU — when latency or privacy demands it.

📈

Eval harness included

Every model ships with a labelled eval set and CI test — so regressions are caught before deploy, not by a customer.

Tech stack

The production stack we ship for computer vision & nlp.

OCR & document AIObject detectionClassificationEdge deployment

Senior engineers (8+ yrs avg) own every engagement. CI/CD from day one, observability baked in, and a p99 120ms performance budget enforced in CI.

How we deliver it

A tailored 4-step process for computer vision & nlp.

Use case & data audit

We map the use case, available labelled data, accuracy targets and deployment constraints — and decide build vs fine-tune vs API.

Labelling & training

We label or curate training data, fine-tune a base model, and run it against a held-out eval set with documented metrics.

Pipeline & integration

We build the inference pipeline, integrate with your app or data warehouse, and add monitoring for drift and accuracy.

Edge or cloud deploy

We deploy to cloud (GPU or serverless) or edge (mobile, browser, Jetson) per your latency and privacy constraints — with a retraining cadence.

FAQ