Computer vision software development services that work on real cameras.
Hoop Interactive builds custom computer vision and image recognition into mobile and web products: facial analysis, object detection, OCR and visual search. We test accuracy on your own images first, then build the pipeline and the app around it.
Computer vision works in the lab. Your users don't live there.
Demos use clean, well-lit images. Real users send blurry photos from cheap cameras in bad light, and expect a result in seconds. Most computer vision projects fail on that gap, not on the model itself.
Our approach: a paid feasibility study on your images, then a pipeline and app built for real-world conditions.
- Different cameras, different results
- We test across devices and lighting before promising accuracy.
- Too slow on a phone
- Heavy work moves to a backend, or runs as an optimised on-device model.
- A reading nobody can use
- Raw detections become scores, recommendations and actions.
- Not enough training data
- We start with pre-trained models, and label data only where it pays off.
What we build with computer vision.
Custom computer vision software development for specific, measurable tasks inside your product.
Facial analysis
Face detection, landmarks and shape or feature analysis for beauty, styling and wellness apps.
Image recognition & classification
Sort, tag and categorise images automatically, from product photos to uploads.
Object detection & counting
Find, locate and count items in images or video, such as stock, parts or defects.
OCR & document extraction
Read text from IDs, invoices, receipts and forms, and send clean data to your systems.
Visual search
Let shoppers search with a photo and find similar products in your catalogue.
Quality inspection
Spot damage, defects or missing parts from photos taken in the field or on site.
Mobile & on-device vision
Camera features in Flutter apps, with models optimised for phones.
Vision + AI assistants
Combine image analysis with an AI chat that explains results and answers questions.
Vision API, classic pipeline, or custom model?
Not every vision problem needs a custom-trained model. We pick the cheapest approach that hits your accuracy target.
| Approach | Best for | Trade-off | Examples |
|---|---|---|---|
| Vision APIs & multimodal AI (fastest to launch) | OCR, general labelling, moderation, quick prototypes | Cost per image at volume, images leave your systems | Google Cloud Vision, AWS Rekognition, GPT-4o vision |
| Classic image-processing pipeline (how Facelyzer works) | Measurable, well-defined tasks like face shape or dimensions | Needs careful tuning for lighting and camera differences | OpenCV, face landmarks, contour and geometry analysis |
| Custom-trained models (highest accuracy) | Specialised objects, defects or domains APIs don't know | Needs labelled data and more time to train and test | PyTorch, YOLO-style detectors, segmentation models |
From camera to useful result.
A computer vision feature is a pipeline. Each stage is designed and tested on its own so failures are easy to find and fix.
Flutter
FastAPIFacelyzer: computer vision that has to work on any phone.
One face photo returns a face-shape reading with match percentages, styling tips and an AI style chat.
COMPUTER VISION · OPENCV · FLUTTER
5 face shapes scored from a single photo
We built the Flutter app, the OpenCV analysis backend and the feature-extraction pipeline, tuned for different phone cameras and mobile performance.
Read the case studyHow we differ from a typical computer vision development company.
Many CV firms are built for enterprise surveillance and factory programmes. We build vision into products.
| What matters | Typical CV vendor | Hoop Interactive |
|---|---|---|
| Starting point | Long discovery, then a big build. | A fixed-price feasibility test on your own images. |
| Accuracy | Lab benchmarks on clean data. | Measured on real devices and real lighting. |
| Approach | Custom model for everything. | API, classic pipeline or custom model, whichever is cheapest to hit the target. |
| Deliverable | A model file and an API. | The model inside a working mobile or web product. |
| Privacy | Raised late, if at all. | Consent, retention and on-device options designed in. |
| Launch | Not their concern. | App store and marketing support from our growth team. |
From feasibility to production.
You see real accuracy numbers before committing to the full build.
Feasibility study
We run candidate approaches on a sample of your images and report accuracy, speed and cost.
1–2 weeks · Accuracy reportData plan
What images are needed, how they'll be collected, labelled and stored with consent.
Week 2 · Data & privacy planPipeline build
Pre-processing, detection, feature extraction and scoring, tested stage by stage.
3–6 weeks · Working vision APIProduct integration
Camera flow, results screens and actions in your mobile or web app.
2–4 weeks · Feature inside the productField test
Testing on real devices, users and lighting conditions, then tuning.
1–2 weeks · Field accuracy reportMonitor & improve
Tracking confidence, failures and drift, and retraining as new data arrives.
Ongoing · Monthly quality reviewComputer vision development cost and timelines.
Prove it works on your images first, then build. Prices are in USD and exclude cloud API, GPU and hosting costs, which are billed to your own accounts and forecast in the feasibility study.
- Investment
- from $3,000
- Timeline
- 1–2 weeks
- Test
- On your sample images
- Compared
- 2–3 approaches
- Output
- Accuracy, speed & cost report
- Investment
- from $15,000
- Timeline
- 6–10 weeks
- Build
- Vision pipeline & API
- Included
- Mobile or web integration
- Support
- 30 days of post-launch fixes
- Investment
- from $30,000
- Timeline
- 10–20 weeks
- Build
- Custom-trained models
- Included
- Data labelling & augmentation
- Support
- Monitoring & retraining
Where computer vision earns its keep.
Practical vision features we can build into your product.
Beauty & styling
Face and feature analysis, try-on and personalised recommendations.
Ecommerce & retail
Visual search, product tagging and catalogue quality checks.
Insurance
Damage photos, document capture and claim evidence checks.
Health & fitness
Posture and progress tracking, with no diagnostic claims.
Property & home services
Before-and-after evidence, surface condition and job verification.
Logistics & operations
Label and document reading, counting and proof of delivery.
Explore more AI services.
Computer vision development pairs naturally with these services from Hoop Interactive.
AI Development
The broader practice this service sits under.
ExploreMachine Learning
Prediction models beyond image recognition.
ExploreGenerative AI Development
LLM features that pair with vision results.
ExploreAI Agent Development
Agents that act on what the vision pipeline sees.
ExplorePython Development
The FastAPI backends our vision pipelines run on.
ExploreFlutter Development
Mobile apps with on-device camera features.
ExploreMobile Apps
The broader mobile development practice.
ExplorePredictive Analytics
Forecasts built on the data vision pipelines capture.
ExploreComputer vision development questions
About our computer vision software development services. Ask us anything else.
A feasibility study on your own images starts from $3,000. A computer vision MVP inside a mobile or web app starts from $15,000, and a production system with custom-trained models, monitoring and integrations from $30,000.
A feasibility study takes 1 to 2 weeks. A focused computer vision MVP usually takes 6 to 10 weeks; we delivered the core Facelyzer app and its OpenCV analysis backend in two months.
It depends on the task. Classic image processing and pre-trained models can work with a few hundred example images. Custom-trained detection or classification models usually need thousands of labelled images per class, which we help you collect, label or augment.
We measure accuracy on your own images in the feasibility study, including bad lighting and different cameras, and agree a target before the full build. You know the expected accuracy before you commit to development.
Cloud vision APIs and multimodal AI models are fastest for common tasks such as text extraction or general labelling. Custom models are better for specialised tasks, tighter accuracy, lower cost at volume or when images cannot leave your systems. We often start with an API and move to a custom model as volume grows.
Yes. Lighter models can run on-device with TensorFlow Lite or Core ML for speed and privacy, while heavier analysis runs on a backend. Facelyzer uses an OpenCV backend with a Flutter app tuned for mobile use.
Face and body images can count as biometric data under laws such as Illinois BIPA, GDPR and state privacy laws. We design for explicit consent, minimal retention, encryption and on-device processing where possible, and recommend you confirm requirements with your legal counsel.
Yes. We can build the vision pipeline as a separate API your current web or mobile app calls, so your existing product changes as little as possible.
Yes. Code, trained model weights, labelled datasets and pipelines belong to you and live in your accounts.