Computer Vision MVP Development: AI That Sees, Counts and Inspects

Detection, classification, OCR and video analytics MVPs, tested on your own images. Fixed price from $3K for a POC, MVPs from $10K in 5–8 weeks.

Codersarts builds computer vision MVPs for startups and businesses that want AI to understand images and video. We build object detection, image classification, segmentation, counting and measurement, defect inspection, OCR and document reading, face-free people analytics and real-time video analysis, then wrap the model in a web or mobile app with uploads or camera input, results, review tools and dashboards. Every project starts by testing feasibility on your own images: a Computer Vision POC costs $3,000–$6,000 and takes 2–3 weeks, a Computer Vision MVP with a trained or fine-tuned model, labeling, evaluation and an app costs $10,000–$20,000 and takes 5–8 weeks, and real-time video or edge deployment starts at $20,000. We choose between hosted vision APIs, multimodal LLMs and custom models such as YOLO based on accuracy, cost and privacy, report precision and recall on held-out data, add human review for low-confidence results, and deliver code, model weights and training pipelines with full IP transfer.

We build computer vision MVPs that detect, classify, count, measure and read from images and video, wrapped in an app your team or customers can use. Feasibility POC from $3,000 in 2–3 weeks, full MVP from $10,000 in 5–8 weeks, with full IP transfer.

Book a Free Vision AI Scoping Call

✅ Tested on your own images first

✅ Detection, classification, OCR and video analytics

✅ Accuracy reported on unseen data

✅ Human review for low-confidence results

✅ Web, mobile, cloud or edge deployment

✅ Model weights and pipelines are yours

Quick Answer

A computer vision MVP is the first working version of a product that uses AI to understand images or video, such as detecting objects, spotting defects or reading text, inside an app people can use.

At Codersarts, a feasibility POC costs $3,000–$6,000 (2–3 weeks), a full MVP costs $10,000–$20,000 (5–8 weeks) and real-time video or edge projects start at $20,000.

Every project starts on your own images, so you know it works before investing in the full build.

Key Facts

Price	POC $3,000–$6,000 · MVP $10,000–$20,000 · Video/edge from $20,000
Timeline	2–3 weeks (POC) · 5–8 weeks (MVP)
Tasks	Detection, classification, segmentation, OCR, counting, measurement, video analytics
Models	Hosted vision APIs, multimodal LLMs, YOLO and other custom models
Input	Uploads, phone camera, CCTV and IP cameras, video files
Deployment	Cloud, mobile or edge devices
Quality	Precision and recall on held-out data, human review queue
Ownership	Code, model weights, pipelines and labeled data

Computer Vision Use Cases We Build

Use case	What the AI does
Quality inspection	Spots defects, scratches or missing parts on a production line
Inventory and counting	Counts products on shelves, pallets or in warehouses
Document and ID reading	Extracts text and fields from invoices, forms and labels
Retail and eCommerce	Tags products, finds similar items, checks listing photos
Construction and property	Reads site photos, measures progress, flags safety issues
Agriculture	Detects crop disease, counts plants or fruit
Healthcare and wellness	Analyzes images for screening support, with clinical review
Video analytics	Counts footfall, monitors zones and detects events without identifying people

How Much Does a Computer Vision MVP Cost?

Scope	What you get	Timeline	Fixed price
Computer Vision POC	Feasibility tests on your images with 1–2 approaches, accuracy report, data and cost recommendations	2–3 weeks	$3,000–$6,000
Computer Vision MVP	Trained or fine-tuned model, labeling workflow, evaluation, web or mobile app with uploads or camera input, results, review queue, dashboard	5–8 weeks	$10,000–$20,000
Real-Time Video / Edge	Live camera streams, on-device or edge inference, alerts, multi-camera dashboards	8+ weeks	From $20,000

What moves the price

Factor	Lower cost	Higher cost
Task	Classify a whole image	Detect, segment or measure many objects
Data	Labeled images exist	Collection and labeling needed
Speed	Results in seconds	Real-time video
Deployment	Cloud	Edge devices or offline mobile
Accuracy bar	Assistive, with human review	Fully automated decisions

Vision API, Multimodal LLM or Custom Model?

Approach	Best when	Trade-offs
Hosted vision API	Standard tasks such as OCR, labels and faces-free detection	Fast to build; per-image cost; limited customization
Multimodal LLM	Flexible questions about images, few examples	Very quick to prototype; higher cost per image at volume
Custom model (e.g. YOLO)	Specific objects, high accuracy, volume, offline or private data	Needs labeled data; lowest running cost at scale

The POC tests the cheapest approach that can meet your accuracy target before we commit to a custom model.

How Do You Measure Accuracy?

Held-out test set the model never sees during training

Precision and recall per class, so you know both misses and false alarms

Confidence thresholds tuned to your cost of errors

Human review queue for low-confidence results

Edge cases tested: lighting, angles, blur and unusual items

Retraining loop using reviewed results to improve the model

How Long Does It Take to Build?

Week	Milestone (Computer Vision MVP)
1	Scope, sample images, success metrics
2	Labeling workflow and starter dataset
3–4	Model training or fine-tuning, first accuracy report
5–6	App with uploads or camera, results and review queue
7	Evaluation on unseen data, threshold tuning
8	Pilot, deployment and handover

Privacy and Responsible Use

Your images stay in your cloud unless you approve otherwise

No face recognition by default: people analytics count and track zones without identifying individuals

Data minimization: blur or crop sensitive regions where possible

GDPR-ready storage, retention and deletion

Human oversight for decisions that affect people

Who Owns the Code, and What Do You Receive at Handover?

You own everything. On the final milestone payment you receive:

Full source code in your GitHub or GitLab, with commit history

Deployed infrastructure in your own cloud account

Architecture and API documentation

Model weights, training pipelines and labeled data

Runbooks for deployment and monitoring

A recorded handover session with your developers

30 days of post-launch bug fixes

No licence fees, no proprietary platform, no lock-in.

Frequently Asked Questions

How much does a computer vision MVP cost?

$3,000–$6,000 for a feasibility POC on your images, $10,000–$20,000 for a full MVP with a trained model, labeling, evaluation and an app, and from $20,000 for real-time video or edge deployment.

How long does it take?

2–3 weeks for a POC and 5–8 weeks for an MVP. Real-time video and edge projects take 8 weeks or more.

How many images do we need?

It depends on the task. Hosted vision models and multimodal LLMs can work with very few examples; custom models usually need a few hundred to a few thousand labeled images per class. The POC tells you how much data your case needs.

Do we need to label our data?

Usually some labeling is needed. We set up the labeling workflow, label a starter set and train your team or use AI-assisted labeling to keep it affordable.

How accurate will it be?

We agree a target with you and report precision and recall on images the model has never seen. Low-confidence results go to a human review queue instead of being trusted blindly.

Should we use a vision API or a custom model?

Start with hosted vision APIs or multimodal LLMs when they are accurate enough, because they are faster and cheaper to build. Train a custom model when you need higher accuracy, lower running cost at volume, offline use or data privacy.

Who owns the model?

You do. Code, model weights, training pipelines and labeled data are yours, with 100% IP transfer on final payment.

Build Your Computer Vision MVP

Send us a few sample images or a short video and tell us what the AI should detect or read. We'll come back within 48 hours with an approach, a fixed price and a timeline.

Book Your Free Vision AI Scoping Call →