Our computer vision developers build systems that extract structured meaning from images and video, including object detection, defect inspection, OCR, and facial analysis, using OpenCV, PyTorch, and YOLO-family architectures. They work across manufacturing, retail, healthcare, and media, taking a model from a labeled dataset to real-time inference running in production, not just a notebook demo.
- 7+ years of experience building production computer vision systems
- 100+ vision models shipped across detection, segmentation, and OCR use cases
- End-to-end delivery from dataset annotation to edge deployment
We are a product-focused software development company serving clients globally
With a dedicated computer vision engineering team delivering across time zones. Our computer vision developers have built detection, recognition, and video analysis systems across manufacturing, retail, healthcare, and media, working directly with founders and product teams across the US and UK time zones on systems that run in real production environments.
- ISO/IEC 20000-1:2018 certified, structured delivery on every engagement
- AWS Partner, Clutch Global 2024, 4.9/5 client rating
- Computer vision delivery across manufacturing, retail, healthcare, and media globally
How We Onboard Your Computer Vision Developer
Structured and fast, with no extended discovery phase before your developer starts working with your actual image or video data.
Share Your Data & Use Case
Tell us what you're trying to detect, classify, or extract from images or video, and what data you already have. We map this to the right vision specialization immediately.
Developer Matching
We shortlist developers based on the specific vision tasks detection, segmentation, OCR, or generative and their experience with your data type and deployment target.
Trial and Alignment
Run a paid trial sprint before committing. The developer reviews your dataset, checks annotation quality, and validates a baseline model against a real sample first.
Active Development
From model training to inference optimization and deployment, the developer works inside your sprint cadence, in your timezone, with your tools.
Computer vision Work We've Delivered
Facial Tracking for AI Dubbing
Midgenie needed to make dubbed video content feel natural across languages; traditional dubbing left mouth movements visibly out of sync with translated audio, breaking immersion and making localized content look obviously re-recorded rather than native to the language.
We built the lip-sync layer using facial landmark detection and tracking to align mouth movement with translated speech frame by frame, on top of AI-generated audio and video content, turning a visibly mismatched dub into a viewing experience that holds up across languages.
Best suited when you need to locate and track specific objects across frames in an inventory on a shelf, defects on a production line, or people/vehicles in a video feed. This profile works with YOLO, Detectron, and similar detection architectures.
The right choice is when the task is judging or categorizing images rather than locating objects within them, quality inspection, medical image triage, or content categorization. This profile focuses on classification accuracy and handling class imbalance in real datasets.
Hire this profile for tasks that involve generating or transforming visual content rather than just analyzing it, facial landmark tracking, lip-sync, video synthesis, or style transfer, built on the same generative system architecture used across other gen AI work.
Real-World Data Over Benchmark Accuracy
A model that scores 95% on a clean academic dataset can fail badly on your actual camera feeds; poor lighting, motion blur, occlusion, and unusual camera angles rarely show up in benchmark data. Our developers validate against your real conditions from the first sprint, not just a held-out test split from a public dataset. That's the difference between a model that demos well and one that holds up on a factory floor or a live video stream.
Inference Speed as a First-Class Requirement
A vision model that's accurate but too slow to run in real time is not a usable system for most production cases. Our developers treat latency and throughput as design constraints from the start, using TensorRT, ONNX, or quantization where needed, rather than a problem to solve after the model is built and accuracy is locked in.
Annotation Discipline Before Model Work
Most computer vision failures trace back to inconsistent or low-quality labeled data, not the model architecture. Our developers set clear annotation guidelines, spot-check labeler output, and catch class imbalance or edge-case gaps before training begins. Getting this step right is what determines whether the eventual model generalizes to new footage or just memorizes the quirks of the training set.
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Real-time detection and tracking of objects, people, or defects across video and image streams using YOLO and Detectron-family models.
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Classification models for quality inspection, content categorization, and triage tasks, tuned for class imbalance in real production data.
Pixel-level segmentation for tasks that need precise object boundaries, such as medical imaging or defect area measurement.
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Text extraction and document understanding from scanned forms, receipts, and ID documents, including handwritten and low-quality scans.
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Frame-by-frame analysis for surveillance, foot-traffic counting, and behavior pattern detection across continuous video feeds.
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Facial landmark detection and tracking for verification, lip-sync, and expression analysis use cases, built with privacy constraints in mind.
Video synthesis, style transfer, and AI-generated visual content built on GAN and diffusion-based architectures that rely on the same visual encoding techniques used across generative vision work.
Model quantization and optimization for real-time inference on edge devices like NVIDIA Jetson or mobile hardware.
Automated visual inspection systems for manufacturing lines, trained to catch defects that are easy for the human eye to miss.
A Dedicated Computer Vision Developer, Fully Embedded in Your Workflow
A full-time computer vision developer embedded in your team, working exclusively on your image or video data, in your time zone, within your sprint cadence. Suited for ongoing vision product development.
Full-Cycle Ownership
The developer owns the full vision pipeline, from dataset annotation and model training through inference optimization and production deployment.
Sprint Integration
Works within your existing workflow, attends standups, contributes to planning, and delivers models on your team's cadence rather than on the side.
Model Context Continuity
Same developer, same context, every sprint; dataset quirks, failure modes, and model history stay with one person instead of being relearned.
Toolchain Alignment
Works inside your existing annotation tools, containerized training infrastructure, and deployment pipeline, with no separate workflow to reconcile.
Computer Vision Across Industries
Industry context changes which failure modes matter most, a developer who has worked in your vertical understands the data quirks before the first model gets built.
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Built for Every Stage of Your Computer Vision Maturity
Startups
Early-stage teams need a developer who can validate a vision concept quickly on limited data, get a working prototype in front of users, and set up an annotation process that doesn't need to be rebuilt as the dataset grows.
Mid-Size Businesses
Growing teams need developers who can scale an existing vision model to more use cases, improve robustness against real-world edge cases surfaced in production, and start optimizing for inference cost at higher volume.
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Enterprise Level Businesses
Enterprise computer vision requires working within governed data environments, documented model validation processes, and coordination with data engineering, compliance, and security teams on regulated or safety-relevant vision systems.
They have strong expertise in the latest technologies and provide excellent guidance in using them effectively.
CODE B launches the products quickly, and their solutions have excellent architecture and are scalable.
CODE B is proactive in coming up with solutions.
Aside from getting the job done, they’re able to provide their expertise and share their opinion.
They’re a very bright team that requires minimal levels of communication or time investment to be very effective.
Their constant communication was a key aspect of the success.
They completed the project within the timeline we gave them, and they did it within budget.
Had a great experience working with the team and in times of crisis, CODE B team was always there to support us.
The way that they have supported us by giving us one of their developers to work directly with our development team.
Our overall experience has been very positive.
They are friendly and reliable.
The ability to deliver on time impressed us the most.
They’re excellent at what they do and come up with solutions for various problems.
CODE B will work overtime to resolve issues, which is a difficult trait to find.
Code B’s communicative.
I’ve had a great experience working with CODE B
The main positive point of working with CODE B team is their analyzing skills.
They are receptive and try to adjust to meet our requirements.
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