Computer vision

Computer Vision Development Services

We build systems that turn camera feeds, photos and scans into events, counts and structured data your software can use. Consulting, data, model training, edge deployment and support, handled by one team. Need one model for one problem? See custom computer vision models.
Book a strategy callSee case studies
  • Trained on your images and video
  • Cloud, on-prem or edge
  • You own the IP
cam-02_loading-dock.mp4
Processing
LOADING BAYCAM-02 · 14:32:07RTSP stream · overhead view

1/4Frames arrive from the cameras you already have, a recorded video or a drone feed. Nothing is labelled yet.

AI models delivered
150+
API requests served
2M+
AI engineers and specialists
50+
GitHub stars on AS-One
580+

Our services

Computer vision software development, stage by stage

Take the whole project to us, or just the part you need. Each service has its own page with the detail.

  • Custom computer vision models

    One model for one well-defined task, trained on your data: detecting a defect, reading a label or tracking an object.
    Detection · Classification · OCR · Video analytics
    Custom models

  • Model training

    Dataset curation, annotation, training, evaluation and retraining for detection, segmentation and classification models.
    YOLO · Vision Transformers · Transfer learning
    Model training

  • Edge deployment

    Models optimised to run on NVIDIA Jetson, mobile phones and on-site servers, with low latency and no constant connection.
    Jetson · TensorRT · ONNX · CoreML
    Edge deployment

  • Computer vision consulting

    Feasibility checks, model selection and architecture design before you commit budget to a build.
    Strategy · Feasibility · Architecture
    Consulting

  • Managed computer vision services

    Monitoring, retraining and infrastructure for models in production, so accuracy holds as conditions change.
    Monitoring · Retraining · Scaling
    Managed services

  • Hire computer vision developers

    Experienced computer vision engineers who join your team for a project or a longer engagement.
    Dedicated · Part-time · Project-based
    Hire engineers

How it works

A vision system is more than a model

Most of the work, and most of the cost, sits around the model: collecting and labelling data, integrating with your systems and keeping accuracy up after launch. We plan for all of it from day one.

Seven-stage computer vision system: image and video sources, data storage, annotation, model training and evaluation, inference engine, deployment layer, and monitoring and retraining
The full pipeline, from cameras to retraining. More in our cost breakdown of a computer vision system.
  1. Stage 1

    Scope the problem and the success metric

    We look at your cameras, sample images and the decision the system has to support, then agree measurable targets such as recall on a class, latency per frame or cost per inference.
    Feasibility and accuracy targets
  2. Stage 2

    Collect and label the data

    Footage is sampled across lighting, angles and seasons, then labelled to written guidelines with a review pass. Data and labelling are often the largest single cost, so we only label what the model needs.
    Versioned, labelled datasetBounding boxesMasksKeypoints
  3. Stage 3

    Train and evaluate models

    We start from pre-trained architectures and fine-tune on your data, then test on held-out footage from your own environment rather than a public benchmark.
    Model that meets the agreed targetsYOLOVision TransformersSAM
  4. Stage 4

    Optimise for the target hardware

    Quantization, pruning and distillation shrink the model, and TensorRT or ONNX export speeds up inference on the GPU, Jetson or phone it will actually run on.
    Benchmarked on production hardwareTensorRTONNXCoreML
  5. Stage 5

    Build the pipeline around it

    Video ingestion, tracking, business rules and outputs: the parts that turn detections into events, counts, alerts and records in your systems.
    API, dashboard or alertsRTSP streamsREST APIsWebhooks
  6. Stage 6

    Monitor and retrain

    Once live, we watch accuracy and drift, collect hard examples from production and retrain when conditions change.
    Accuracy that holds after launch

Core capabilities

What our computer vision systems do

The tasks we build most often, each with a project where we have done it.

  • Object detection and tracking

    Find people, vehicles, animals or products in each frame and keep a stable ID on each one across the video.
    Player and ball tracking for MiniStats
    Soccer analytics case study

  • Visual search and similarity

    Turn images into embeddings and match them against a catalog in a vector database, for product lookup or recommendations.
    150K+ SKUs matched in under a second
    Sneaker retrieval case study

  • Drone and aerial analysis

    Detect small objects from altitude in thermal and RGB footage, then classify them when the drone moves closer.
    98% thermal detection accuracy at 100m
    Drone monitoring case study

  • Image classification and tagging

    Assign labels from your own taxonomy to every image, from room types and property features to listing compliance issues.
    50+ property features tagged per photo
    Property image case study

  • Segmentation

    Pixel-level boundaries for rooms on floor plans, regions in medical scans or defects on a surface.
    Walls and rooms segmented on floor plans
    Choosing segmentation models

  • OCR and document vision

    Locate documents and labels in an image, read the text and validate it, including rotated text on engineering drawings.
    98% extraction accuracy on passports
    Passport KYC case study

  • Pose estimation

    Track body keypoints and joint angles to flag unsafe lifting, count movements or analyse technique.
    Unsafe bends and twists flagged in real time
    Posture safety case study

  • Mobile and on-device vision

    Models quantized for phones and embedded devices, so detection keeps working without a connection.
    <30ms on-device inference on iPhone
    Find My Ball case study

  • Generative AI for images

    Fine-tuned diffusion pipelines for virtual staging, inpainting and synthetic training data for rare cases.
    Rooms staged while keeping real architecture
    AI virtual staging

In production

Three systems, three kinds of input

Video analytics

Soccer match analytics from a single video

For MiniStats we built a multi-stage pipeline: a YOLOv8 detector trained on annotated match footage finds players and the ball, the NorFair tracker keeps their IDs, clustering assigns players to teams, and event logic turns ball movement into passes and intercepts.
  • Player and ball detection and tracking
  • Team identification without facial recognition
  • Passes, intercepts and possession derived automatically
Read the case studySports computer vision
Youth soccer match with tracked players labelled by ID, ball trajectory lines and a ball possession panel showing 21% and 79%
Tracked player IDs, ball paths and possession from MiniStats match footage.
Aerial and thermal

Drone monitoring across large areas

A two-stage aerial pipeline: thermal imaging at 100m detects heat signatures across wide areas, then an RGB stage at 50m classifies species, age and gender. It gives conservation teams real-time data on animal density and movement.
  • 98% thermal detection accuracy at 100m
  • 92% RGB species classification accuracy at 50m
  • Small objects detected from altitude
Read the case study
Low-altitude drone image of a herd of zebras on dry grassland, each animal inside a detection box with a confidence score
Low-altitude RGB detections on a zebra herd.
Visual search

Identify a sneaker from one photo, out of 150K+ SKUs

For AlwaysLegit, a sneaker detector isolates the shoe from busy expo backgrounds, feature models turn it into an embedding of shape and colour, and approximate nearest neighbour search in a vector database returns the matching SKU in under a second.
  • Detection removes background clutter first
  • Embeddings capture panel layout and colour
  • Sub-second retrieval across the whole catalog
Read the case studyImage search in e-commerce
Visual search demo: an uploaded photo of a blue and white sneaker matched to a catalog product, with detection and search times of 0.04 and 0.58 seconds
Demo interface: photo in, matching SKU and processing times out.

Our work

More computer vision we have shipped

On-device detection on a phone, listing photo tagging, species identification in live safari streams, posture analysis, passport OCR and 3D reconstruction from X-rays.

All case studies

Industry applications

Where computer vision earns its keep

It pays off where visual work is repetitive, costly or error-prone. Not sure yours qualifies? Read is computer vision worth it for SMEs.

  • Real estate and PropTech

    Property image classification, room type detection, condition analysis, virtual staging and listing compliance.
    Real estate computer vision

  • Construction and floor plans

    Room segmentation, door, window and symbol detection, quantity takeoffs, code compliance and drawing review.
    Floor plan analysis

  • Safety and security

    PPE and posture monitoring, restricted zone alerts, intrusion detection and perimeter monitoring from existing cameras.
    Safety monitoring

  • Sports analytics

    Player tracking, ball trajectories, event detection and tactical heatmaps from match video.
    Sports computer vision

  • Healthcare

    Imaging analysis such as 3D reconstruction from X-rays and automated bone angle measurement for screening.
    Healthcare AI

  • Retail and e-commerce

    Visual product search, automated product tagging, shelf monitoring and recommendations from images.
    Visual recommendations guide

  • Manufacturing

    Defect detection, component inspection and assembly verification on the production line.

  • Logistics and warehousing

    Worker safety, forklift analytics, loading verification, inventory tracking and package counting.

  • Agriculture and wildlife

    Drone-based livestock and wildlife detection, crop monitoring and species identification.

Deployment

Run it wherever the cameras are

Edge, cloud or your own servers: we choose based on latency, bandwidth, privacy and running cost, not habit.

  • At the edge

    On NVIDIA Jetson, industrial PCs or phones, for sites and vehicles without reliable internet.

  • In the cloud

    On your AWS, GCP or Azure account, or on an API we host and manage for you.

  • On-prem and air-gapped

    Containerised builds on your own servers when images cannot leave your network.

  • Hybrid

    Detection at the edge, with aggregation, dashboards and retraining in the cloud.
Edge AI vs cloud AIOn-prem vs cloud deploymentPrivate AI deployment

What we benchmark before go-live

  • Accuracy on your own validation set

    Precision, recall and F1 against targets agreed at kickoff.

  • Throughput on target hardware

    Frames per second on the GPU, Jetson or server you will run in production.

  • End-to-end latency

    p95 response time through the full API or streaming pipeline.

  • Cost per inference

    Hardware and cloud cost at your expected volume.

How we hit the targets

  • Quantization, pruning and knowledge distillation
  • TensorRT acceleration and ONNX export
  • Batching and async inference for throughput
  • Jetson tuning, see our YOLO on Jetson tutorial

Open source

We maintain AS-One, a computer vision framework on GitHub

AS-One puts detection, tracking, segmentation, OCR and pose estimation behind one Python API. We built it with Augmented Startups, and we use the same building blocks to prototype client systems quickly.

  • YOLOv5 to YOLOv9 detectors behind a single interface
  • ByteTrack and DeepSORT tracking with a standard API
  • SAM segmentation, OCR and pose estimation
  • PyTorch, ONNX and CoreML runtimes

580+ stars on GitHub

View AS-One on GitHub (opens in a new tab)Read the AS-One guideAS-One case study
Video walkthrough of the AS-One framework.

Getting started

Prove it on your own footage first

Start small with real images from your cameras, then scale what works.

  1. Step 1: Scoping call

    30 minutes

    Share sample images or footage and the decision you want automated. We tell you what is feasible and what data it needs.

    • NDA on request

  2. Step 2: Proof of concept

    4–6 weeks

    A working model on your data, measured against the targets we agreed, on hardware close to production.

    • Accuracy targets agreed up front

  3. Step 3: Production

    Ongoing

    Integrated with your systems, deployed where you need it, monitored and retrained as conditions change.

    • You own the IP

Planning a budget? Read how much computer vision development costs in 2026, or start with an AI opportunity audit.

FAQ

Frequently asked questions

What teams usually ask before starting a computer vision project.

Want an engineer's view first? Talk to a computer vision consultant.

  • Computer vision development services cover designing, training and deploying AI models that understand images and video, such as object detection, image classification, OCR and video analytics. Unlike off-the-shelf tools, custom models are trained on your data, which usually gives better accuracy on your specific problem.

Related

Keep exploring

Case studies

Guides from our engineers

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