Computer vision engineers who build image recognition, object detection, OCR pipelines, and real-time video analytics for production environments, not research demos.
Senior CV engineers with PyTorch, TensorFlow, and production deployment experience
Flexible models, part-time, full-time, or dedicated team
Start in days, not weeks
In short
RaftLabs provides computer vision engineers for hire who build image recognition systems, object detection pipelines, OCR and document intelligence, and real-time video analytics for production deployment. Python, PyTorch, and TensorFlow expertise with experience training, fine-tuning, and deploying models to cloud and edge environments. Available part-time, full-time, or as a dedicated team with full source code ownership. Engagements start within days.
Recognition
OCR accuracy below 90% in production and the pre-trained model is not cutting it?
Need computer vision engineers who understand training pipelines and production deployment, not just pre-built model wrappers?
We work with manufacturers, healthcare companies, and logistics operations that need computer vision systems built for production, not proofs of concept.
Manual visual inspection is slow, inconsistent, and expensive. We build computer vision systems that inspect products on the production line, detect defects at the pixel level, and flag anomalies faster than any human inspector. The system runs on your hardware, integrates with your MES, and has an accuracy threshold you agree to before we ship.
Medical imaging generates data that clinical teams cannot review fast enough. We build classification and detection models for radiology images, pathology slides, and clinical photography that assist your clinicians rather than replace them, with the explainability and auditability that healthcare requires.
Shipping labels, delivery receipts, customs forms, and bills of lading are still processed manually in most logistics operations. We build OCR and document intelligence systems that extract structured data from these documents with the accuracy level your operations workflows need.
Video analytics for retail analytics, queue detection, people counting, and security monitoring. We build real-time video analysis pipelines that run at the edge or in the cloud, depending on latency requirements and data sovereignty constraints.
OCR pipelines that go beyond character recognition. We extract structured data from forms, receipts, invoices, and contracts, handling rotated documents, low-resolution scans, and mixed layouts. We combine traditional OCR engines like Tesseract with vision-language models for the document types that need it.
Object detection models using YOLO, Detectron2, and transformer-based architectures. We handle the full pipeline: dataset annotation, model training, evaluation against your test cases, and deployment to your inference environment.
Defect detection systems for production lines. We work with your quality team to define what a defect looks like, collect and annotate training images from your actual production environment, train and validate the model against your false positive and false negative requirements, and deploy to your inspection hardware.
Real-time video analysis at 30fps or higher. People counting, object tracking, queue length detection, and anomaly detection. We design for the frame rate, latency, and hardware your deployment environment requires, whether that is a cloud GPU or an edge device.
Classification and segmentation models for medical images, trained on annotated datasets and evaluated with the statistical rigour that clinical applications require. We document model performance, confidence calibration, and failure modes so your clinical team knows where the model is reliable and where it is not.
Pre-trained vision models need fine-tuning to perform well on your specific domain. We manage the full training workflow: dataset preparation, augmentation strategy, training runs, hyperparameter tuning, and evaluation against held-out test sets. We deliver trained model weights and the code to retrain when your data changes.
Senior CV engineers, flexible engagement, full source code ownership.
Most computer vision work stops at the model. We take it to production: containerised inference with Docker, ONNX export for cross-platform deployment, TensorRT optimisation for edge hardware, and monitoring that alerts when model accuracy drifts.
A model is only as good as its training data. We collect, annotate, and quality-check training datasets from your actual production environment. This is the step that separates a model that works in a demo from one that works on your factory floor.
Part-time for model evaluation, full-time for a complete CV system build, or a dedicated team for an ongoing computer vision programme. We match the model to your current project phase.
Senior computer vision talent without local ML engineer costs. No recruitment timeline, no benefits overhead, no notice period when you need to adjust the team.
We document model performance with precision, recall, F1 score, and confusion matrices across your test cases. For production deployments, we add confidence thresholds and human-review fallbacks so the system knows what it knows.
Some computer vision use cases need low latency at the edge. Others need cloud GPU scale. We design inference architecture for your specific latency, cost, and data sovereignty requirements, not a one-size-fits-all cloud deployment.
Tell us the visual inspection problem you need to solve, the accuracy level you need, and the environment it needs to run in. We'll match you with the right computer vision engineers.
A 30-minute call to understand your use case, the data you have available, and whether a pre-trained model can be fine-tuned or whether you need to build a training pipeline from scratch.
A clear proposal with team composition, timeline, data requirements, and cost, before any commitment.
Engineers onboard in days. Data collection and annotation planned. Model training under way within the first two weeks.
Pricing
Dedicated senior engineers from $6,000/month (minimum pod $12,000/month).
Senior CV engineers available in days. Fixed cost or monthly retainer. Full source code ownership.
Both. We use PyTorch for research-style work and model development, where the dynamic computation graph makes iteration faster. For production deployment, we use ONNX to export models to a framework-agnostic format, which allows TensorRT optimisation for NVIDIA hardware and CoreML deployment for Apple edge devices. TensorFlow is used where the production environment requires it, such as TensorFlow Serving or TensorFlow Lite for mobile.
It depends on the task. For fine-tuning a pre-trained model on a specific object class, a few hundred annotated images is often enough. For training from scratch on a novel domain with high accuracy requirements, you may need tens of thousands. We assess your existing data, the task complexity, and the accuracy floor you need, then give you an honest number before the project starts. We also design data collection strategies if you do not have enough yet.
Yes. We have deployed models to NVIDIA Jetson devices, Raspberry Pi, and industrial edge compute hardware. Edge deployment requires model optimisation that is different from cloud deployment: quantisation to reduce model size, TensorRT for NVIDIA hardware, and careful profiling against your actual frame rate and latency requirements. We document the performance characteristics before and after optimisation.
We manage the annotation workflow using tools like Label Studio, Roboflow, or CVAT, depending on the annotation type and team size. For manufacturing defect detection and medical imaging, we involve your domain experts in the annotation process rather than using general-purpose annotation services, because the quality of annotations directly determines the quality of the model. We include inter-annotator agreement metrics in our quality reporting.