Most visual data in your business goes unanalysed. Cameras capture footage nobody watches. Documents pile up waiting for manual entry. Quality checks are done by people standing at a line, catching maybe 80% of defects on a good day. We build computer vision systems that process visual data automatically, real-time object detection, document extraction, quality inspection, and video analytics, for production environments where accuracy and throughput actually matter.
Manual visual inspection missing defects your team can't catch at production speed?
Camera footage and scanned documents generating data nobody can process at scale?
The short answer
RaftLabs builds custom computer vision systems for production environments in the US, UK, and Australia. Object detection, OCR, quality inspection, and video analytics trained on your data. Industrial systems reach 95%+ defect detection accuracy. Starts at $25,000, fixed price.
Updated July 2026
Trusted by
AI development, by the numbers
01
AI products shipped in 24 months
20+
02
from kick-off to production-ready AI product
12 weeks
03
rated by clients on Clutch
4.9/5
04
years shipping software and AI products
9+
Computer vision that runs in production, not just demos
Every computer vision demo looks impressive on clean, well-lit, carefully chosen images. Production systems deal with motion blur, variable lighting, partial occlusion, document scans at an angle, and conditions that weren't in the training data.
The hard part isn't getting a model to 85% accuracy on a benchmark. It's getting to 95%+ on your specific products, your specific documents, your specific environment, and keeping it there as conditions change.
We've shipped OCR systems processing thousands of industrial documents a month and AI systems analysing patient monitoring data. That's the production-grade computer vision we build.
Capabilities
What we build
Object detection and classification
Detection and classification of objects, defects, or anomalies in images and video using YOLO or Vision Transformer architectures, chosen for your accuracy and latency needs. Models are trained on your annotated data with transfer learning, then deployed as a real-time API or on-device for edge use. Below-threshold detections route to a human review queue instead of producing a confident wrong answer. We've processed thousands of industrial images per day in production with variable lighting and scan quality.
Document OCR and extraction
Structured data extraction from documents that don't fit a fixed template: invoices from 50+ suppliers, medical forms, shipping labels, insurance certificates. Layout-aware models like LayoutLM and Azure Document Intelligence understand document structure, correctly pairing labels with values across multi-column layouts. Pre-processing fixes the skew, contrast, and noise issues that break naive OCR. Each extracted field gets a confidence score, and low-confidence fields are flagged for human review. We've shipped OCR systems with 95%+ straight-through processing rates.
Quality inspection systems
Automated visual quality control for manufacturing lines where manual inspection is a bottleneck or accuracy varies with inspector fatigue. Defect detection models are trained on your actual production rejects, not synthetic data. Inference runs on edge devices at the inspection station with sub-100ms latency, no round-trip to a central server. Pass/fail output with defect type and confidence feeds your operator interface and MES for automated reject routing. We optimize hardest against missed defects, the metric that matters.
Video analytics
Analysis of video streams for operational intelligence: people counting, zone occupancy, vehicle detection, queue length estimation, and behavior monitoring. Object tracking with Deep SORT or ByteTrack follows individuals across frames, the difference between "person detected" and knowing someone spent 8 minutes in a zone. Works on live RTSP feeds and recorded footage. Alerts arrive via webhook, Slack, or dashboard, and structured event data integrates with your operations platform so staff never review footage manually.
Medical image analysis
Computer vision for clinical and healthcare applications, built to the accuracy and validation standards medical use demands. Classification for X-ray and scan findings, segmentation for clinical review, and anomaly detection for patient monitoring streams. Pipelines handle DICOM input, and models are validated against expert-labelled datasets with sensitivity and specificity reported at your clinical decision threshold. Findings export as HL7 FHIR resources or structured reports. Our remote monitoring AI cut clinical decision latency by 20% in ICU workflows.
Custom model training and fine-tuning
Custom model training on your domain data when pre-trained models can't reach the accuracy your use case requires. We plan data collection to fill training-set gaps, set up annotation pipelines with inter-annotator agreement checks, and run training with full experiment tracking. Transfer learning from foundation models like ViT and ResNet cuts labelled-data needs by 60-80% versus training from scratch. The model keeps improving as new production examples arrive.
Every project follows the same four phases. Scope is locked and price is fixed before development starts.
Week 1
01
Discovery and scope
We map the visual problem, the environment, and the accuracy requirements. You leave week 1 with a written scope document and a fixed-price quote. No development starts without your sign-off.
Weeks 2-3
02
Data audit and model design
We assess your existing visual data, identify annotation gaps, and select the model architecture. Decisions made here determine final accuracy. The approach is locked before training starts.
Weeks 4-12
03
Train, integrate, and QA
Model training on your domain data, integration with your target system, and parallel QA. Working inference at a staging endpoint by end of sprint one. Bi-weekly demos with accuracy metrics at each review.
Weeks 12+
04
Deploy and monitor
Production deployment with monitoring activated on launch day. Accuracy drift alerts and model retraining included for 8 weeks post-launch. New production examples improve the model over time.
Why us
Why teams choose RaftLabs
01
Senior engineers build what they scope
The engineers who assess your problem also build the solution. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week 1 ships in week 12.
02
Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
03
9 years and 100+ products shipped
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record across AI, SaaS, mobile, automation, and enterprise platforms across healthcare, fintech, logistics, and manufacturing.
04
Compliance built in from the start
HIPAA, GDPR, SOC 2 -- compliance requirements are scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant AI monitoring systems for US healthcare clients and GDPR-compliant products for European markets.
The stack we build computer vision on
We are not tied to one framework or one model family. We select the architecture that hits your accuracy and latency targets, then document every choice so any competent ML team can retrain and maintain it. The technologies we reach for most often:
Layer
Technologies we use
Where it fits
Frameworks
PyTorch, TensorFlow, OpenCV, Keras
Model training, image processing, and inference pipelines
Optimised inference, edge deployment, and experiment tracking
Cloud and edge
AWS, Google Cloud, Azure, NVIDIA Jetson
GPU-backed training, scalable serving, and on-device inference
The rule holds at every layer: no proprietary tooling that locks you in, and no stack we cannot hand to your team on day one.
What computer vision development costs
We price by project, not by the hour. After a scoping session you get a fixed quote with a defined scope, timeline, and price, so you know the number before development starts.
Project type
Cost range
Focused system, one use case, model training on your data, inference pipeline, and integration to one target system
$25,000–$60,000
Multi-use-case platform, real-time video processing, exception workflows, and multiple output integrations
$60,000–$150,000
Cost is driven by the complexity of the visual task, the amount of training data required, and the inference throughput needed. What pushes it up: variable real-world conditions that demand more domain-specific training data, strict compliance such as HIPAA and GDPR, and high-throughput real-time processing. We scope every project before pricing it.
Computer vision development is the process of building software that can interpret and act on visual data, images, video, and documents. This includes training or fine-tuning models to recognise specific objects, defects, or text in your domain, and building the pipeline that ingests visual data, runs inference, and delivers structured output to your systems. Unlike a generic computer vision API, a custom system is trained on your specific products, documents, or environment, and integrated into your existing workflow. We build computer vision systems for document extraction, quality inspection, object tracking, and video analytics.
Accuracy depends on data quality, consistency of conditions, and how well the model is trained for your specific use case. For controlled industrial environments (consistent lighting, known product types), defect detection systems reach 95%+ accuracy. For document OCR on clean digital files, accuracy is 97-99%. For variable conditions (outdoor footage, inconsistent lighting, mixed document formats), accuracy improves with domain-specific training data. We run a discovery phase to assess your specific conditions and set realistic accuracy targets before development starts.
Both, depending on what achieves the target accuracy most efficiently. For many use cases, fine-tuning a pre-trained foundation model (like YOLO, EfficientDet, or a vision transformer) on your domain data is faster and more cost-effective than training from scratch. For highly specialised domains, unusual defect types, proprietary document formats, or very specific object classes, custom model training gives better results. We assess the tradeoff during scoping and recommend the approach that gets you to production accuracy in the available timeline.
We've built vision systems for document processing (invoice OCR, form extraction, ID verification), manufacturing quality control (defect detection on production lines), logistics (label reading, package dimension estimation), healthcare (medical image processing, patient monitoring), and retail (shelf monitoring, customer flow analysis). The extraction and detection requirements differ significantly by industry, we design the model and pipeline around your specific use case.
A focused computer vision system, one use case, model training on your data, inference pipeline, and integration to one target system, typically runs $25,000--$60,000. Multi-use-case platforms with real-time video processing, exception workflows, and multiple output integrations run $60,000--$150,000. Cost is driven by the complexity of the visual task, the amount of training data required, and the inference throughput needed. We scope every project before pricing it.
Yes. We sign NDAs before any technical discussion. Computer vision projects often involve proprietary product data, manufacturing processes, or clinical imagery. Confidentiality is standard from the first call. We have shipped systems for clients across healthcare, manufacturing, and logistics in the US, UK, and Australia where data sensitivity is high.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Computer Vision Development Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
