Updated October 2026 · Written and maintained by the Progression Agency strategy team
A computer vision software development company designs, trains and deploys software that reads images or video and turns them into a decision: count the cartons, find the scratch, read the label, flag the empty shelf. Progression builds these systems for manufacturers, retailers, warehouse and logistics operators, construction, agriculture and field-service firms, and software companies that need a vision feature inside their own product. Each project starts from your own images and ends with a model wired into the system where someone acts on its answer. Progression Agency is based in New York City and works with clients across the United States and worldwide.
On this page · 16 sections
- What does a computer vision software development company actually build?
- Our computer vision development services, stage by stage
- Computer vision solutions by industry: what goes in, what comes out, who acts
- Computer vision for manufacturing: visual inspection software and defect detection
- Computer vision for retail: shelves, stockrooms and product images
- How do you build a computer vision system? Our process and what each stage produces
- How many images are needed for object detection?
- How accurate is image recognition, and how should you judge it?
- Edge computer vision or cloud: where should the model run?
- Cameras, lighting and calibration come before the model
- Build or buy: cloud vision APIs, packaged inspection systems or custom software
- Data ownership, privacy and the uses we decline
- How much does computer vision cost?
- How to hire computer vision developers, and how to compare computer vision development companies
- What AI assistants say about vision vendors, and what gets a company cited
- Related software and AI services
The short answerA vision system is worth building when three things exist: a decision a person already makes by looking, representative images of that decision, and a place to send the result. We test feasibility on your photos first, write a labeling plan, train a baseline model, set its threshold on what each mistake costs you, then integrate and deploy it on a device, a site server or the cloud with drift monitoring. Success is measured on held-out images and then on the line, the shelf or the dock. Price comes after scope: each build is quoted after a written scope.
Vendor and framework details are described from each provider’s own documentation, and the Illinois statute from its official text, as read in October 2026; legal points are general information, not legal advice. No build is priced before its written scope.
What does a computer vision software development company actually build?
It builds software that looks at a picture and answers a narrow business question about it, reliably enough that someone acts on the answer. The job is rarely the model alone; it is the camera setup, the labeled data, the model, the integration and the monitoring that keep the answer trustworthy.
Six questions vision software answers
Almost every project reduces to one of six tasks: count, classify, locate, measure, inspect or read. Naming the task early matters because each one needs different labels. Counting and locating need a box or outline drawn around each object; classifying needs one tag per image; reading needs the text transcribed.
Computer vision developers versus a general software team
Computer vision developers spend most of their time on data: choosing which images to label, writing rules so two labelers agree, and finding the cases the model gets wrong. A general software team builds the app, API and dashboards around the model. A vision project needs both, and the proposal should say who does which part.
Where our other AI pages pick up
Pulling fields out of invoices, IDs and forms is covered on our AI app development page, and adding a ready-made model to software you already run is on AI integration services. If you are still deciding whether AI is the right tool at all, start with AI consulting. This page is about systems whose input is a camera or a photo.
Our computer vision development services, stage by stage
We offer the full path, from a first test on your own images to a monitored model in production, and you can stop after any stage. Each of our computer vision development services ends with something you keep, so a stop still leaves you with usable work.
| Service | What you receive | When you need it |
|---|---|---|
| Feasibility study | A labeled sample, a test model and a written go or stop recommendation | Before any budget is committed |
| Computer vision proof of concept | A working model on your images with an honest error report | To show stakeholders the idea works |
| Data and labeling program | Labeling rules, a labeled set and an agreement check between labelers | When images exist but no labels |
| Custom model development | Trained detection, classification, segmentation or reading models | When off-the-shelf services miss your objects |
| Integration and app work | APIs, operator screens, alerts and writes into your systems | When results must reach people or software |
| Edge or cloud deployment | Packaged model, device setup or cloud service, with logging | When the model goes live |
| Monitoring and retraining | Drift reports, a review queue for doubtful cases and model updates | For as long as the system runs |
Custom computer vision development services, not a resold API
Custom computer vision development services mean the model is trained on your parts, products and conditions, and the code and data are yours. Where a cloud API already answers the question well, we recommend it and wire it in rather than train a new model.
Object detection development services
Object detection finds each item in an image and draws a box around it, which makes counting, locating and tracking items through a process possible. It is the most common starting point for warehouse, retail shelf and production line projects.
Image recognition development and classification
Image recognition development covers models that put a whole image, or a crop from a detector, into a category: grade A or B, product SKU, ripe or unripe. Classification is often cheaper to label than detection, so we use it whenever the business question allows.
OCR development for labels, serials and gauges
OCR development reads printed or stamped text from photos: lot codes, serial plates, shipping labels, meter faces. Reading text on curved, dirty or reflective surfaces is a vision problem first and a text problem second, so camera and lighting come before the model.
Computer vision app development
Computer vision app development puts the model in front of users: a tablet app that photographs a pallet and returns a count, a phone app for field crews, or a web dashboard for a quality lead. Our app development and cloud app development teams build these screens and services.
Computer vision solutions by industry: what goes in, what comes out, who acts
Good use cases share a pattern: a repeated visual check, done many times a day, where a missed case is expensive. The table lists the computer vision solutions we scope most often, with the input, the output and the person or system that acts on it.
| Industry | Input | Output | Who acts on it |
|---|---|---|---|
| Manufacturing | Line camera images of each part | Pass or fail with the defect marked | Reject gate, operator or quality lead |
| Packaging | Images of sealed cases or bottles | Fill level, cap and label checks | Line controller |
| Warehouse and logistics | Dock or forklift camera frames | Carton and pallet counts, label reads | WMS receiving record |
| Retail | Shelf photos from staff phones or fixed cameras | Empty facings and planogram gaps | Store associate task list |
| Construction | Site and progress photos | Installed item counts, progress against plan | Project manager report |
| Agriculture | Field, greenhouse or drone imagery | Plant counts, damage or ripeness grades | Grower or spray schedule |
| Facilities and safety | Fixed camera frames of work areas | Missing hard hats or blocked exits | Supervisor alert, where lawful |
| Software products | Photos your users upload | Tags, crops, reads or quality checks | Your app or API |
Computer vision for logistics and warehouse receiving
At the dock, vision can count cartons, read shipping labels and compare what arrived with the advance ship notice. The result only matters if it lands in the receiving record, which is why the logistics software side of the project is scoped with the model.
Computer vision for construction and field service
Progress photos already pile up on every job. A model can count installed fixtures, check that a photo shows the required item before a ticket closes, or sort images by trade. Our construction software development page covers the project systems these results feed.
Computer vision for agriculture
Field and greenhouse imagery supports plant counts, pest or damage spotting and grading at packing. Lighting outdoors changes by the hour, so agricultural datasets need images across seasons and weather. See also agriculture software development.
Safety and PPE monitoring, where the law allows
Detecting a missing hard hat or a blocked exit is an object question, not an identity question, and we design it that way: alerts about a zone, never a named worker. Cloud services list this too; Amazon describes personal protective equipment detection in Rekognition among its features. Workplace monitoring rules vary by state, so your counsel reviews the design before cameras go live.
Computer vision for manufacturing: visual inspection software and defect detection
In a plant, computer vision for manufacturing usually means visual inspection software: a camera at a fixed station, consistent lighting, and a model that passes or rejects each part and shows why. The value comes from catching defects people miss late in a shift and from the record it leaves behind.
How is computer vision used in manufacturing?
Common jobs are surface defect detection, assembly verification (is every screw, clip and label present), dimensional checks, reading lot codes for traceability, and counting. Each runs at a station where a part stops or passes at a known speed.
Defect detection software and the cost of each mistake
Defect detection software can be tuned to reject more or fewer parts. Rejecting a good part wastes material and line time; passing a bad one can reach a customer. We price both mistakes with your quality lead, then set the threshold where the total cost is lowest, instead of chasing a single accuracy number.
Computer vision quality inspection versus rule-based machine vision
Rule-based machine vision solutions measure edges, colors and positions with fixed rules and suit parts that look identical every time. Deep learning models suit variable surfaces such as wood grain, castings or textiles, where the defect looks different on every part. Many lines use both: rules for measurement, a trained model for appearance.
Computer vision solutions for manufacturing records
Every inspection produces an image, a verdict and a timestamp. Stored against the lot or serial number, that becomes a quality record. Our manufacturing software development team connects it to the nonconformance and traceability records you already keep.
Have a visual check people do by eye all day?Send twenty photos of the good and bad cases and describe the decision made on each one. We will reply with a written feasibility scope for a first model on your own images.
Computer vision for retail: shelves, stockrooms and product images
Computer vision for retail works best on products and fixtures, not on shoppers. Shelf photos show empty facings, misplaced items and missing price tags; stockroom images support counts; product photos can be tagged and checked before they go online.
Computer vision solutions for retail shelves and planograms
Staff photograph a bay, or fixed cameras capture it on a schedule, and the model compares what it sees with the planogram. The output is a task list for an associate, ranked by sales impact, which is what makes the system worth running.
Computer vision in retail ecommerce catalogs
For online catalogs, models can check that product photos meet size, background and cropping rules, tag attributes such as color and pattern, and flag duplicates. Our retail software development page covers the systems those results sit in.
What we do not build for retail
We do not build shopper identification, demographic profiling or emotion analysis. Counting how many people pass an entrance can be done without identifying anyone, and when a client needs it we design it that way, with images discarded after counting.
How do you build a computer vision system? Our process and what each stage produces
We build in six gated stages, and each one ends with a written artifact and a decision to continue or stop. The order matters: most failed vision projects skipped the feasibility test or the labeling rules, not the model.
| Stage | Artifact you receive | Decision at the end |
|---|---|---|
| 1. Feasibility | Test results on a labeled sample of your images | Is there a visual signal worth modeling? |
| 2. Data audit and labeling plan | Gap list, labeling guide with examples, sampling plan | How many images, of which cases, labeled how? |
| 3. Baseline model | Model, error report on held-out images, list of failure types | Fix data, change camera or proceed? |
| 4. Threshold and acceptance | Cost table for each mistake type and the agreed operating point | What score counts as ready for a pilot? |
| 5. Integration | API, screens, alerts and writes into your systems | Does the result reach the person who acts? |
| 6. Deployment and monitoring | Deployed model, logging, drift report and retraining schedule | When does the model get retrained? |
Stage 1: feasibility on your images, not ours
Public datasets and demo videos prove nothing about your parts or shelves. We start with a few hundred of your own images, label them, and test whether a model separates good from bad at all. If it cannot, you have spent little and learned a lot.
Stage 2: the labeling guide
Two people labeling the same image should draw the same box and pick the same class. A written guide with pictures of edge cases makes that happen. We measure agreement between labelers, because a model cannot be more consistent than its labels.
Stage 3: a baseline model and an error review
The first model is deliberately simple. Its value is the error review: which images it gets wrong, and why. Errors usually point to a data or camera fix that is cheaper than a bigger model.
Stage 6: drift, review queues and retraining
New suppliers, new packaging, a moved light or a dirty lens all change what the camera sees. We log confidence scores, send doubtful cases to a human review queue, and retrain on a schedule agreed in the scope. Our DevOps practice handles the deployment pipeline.
How many images are needed for object detection?
There is no universal number; it depends on how many classes you need, how varied they look and how rare the bad cases are. Published guidance gives useful anchors, and the feasibility stage tells you the real figure for your problem.
Google’s data preparation guide for AutoML object detection sets a minimum of 10 images per label, each with at least one bounding box, recommends about 1,000 annotations per label, and says the model works best when the most common label has no more than 100 times the images of the least common one. It also says training images should be as close as possible to the images the model will see in use, across angles, resolutions and backgrounds. We treat those figures as a starting point for AutoML models, not a promise for a custom one.
Why rare defects decide the dataset size
If a defect appears once in a thousand parts, collecting enough examples of it takes time. Options include running the camera for weeks before training, staging defect samples with your quality team, or starting with a detector for the common defects and adding rare ones later.
How many images for deep learning when you have very few?
Starting from a model already trained on general images, then fine-tuning on yours, cuts the number you need. Augmentation, such as flips, crops and brightness changes, helps further, but it cannot invent a camera angle or lighting condition you never captured.
Who labels the images
Your experts label the hard cases, because only they know what a borderline defect is. Routine boxes can be drawn by trained labelers working from the guide. We set up the labeling tool, the guide and the agreement checks either way.
How accurate is image recognition, and how should you judge it?
Accuracy depends on your images, your classes and where you set the threshold, so a vendor quoting a percentage before seeing your data is guessing. The honest answer is a test on images the model has never seen, reported in measures that match your costs.
| Measure | Question it answers | When it matters most |
|---|---|---|
| Precision | Of the cases the model flagged, how many were real? | When false alarms stop a line or waste staff time |
| Recall | Of the real cases, how many did the model catch? | When a missed defect reaches a customer |
| Confusion matrix | Which classes get mixed up with which? | When several defect or product types look alike |
| Mean average precision | How well are objects found and boxed across thresholds? | Comparing detection models during development |
| Character or field error rate | How often is text read wrongly? | OCR of serials, labels and gauges |
| Throughput and latency | How many images per second, and how fast is each answer? | Moving lines and live video |
How accurate are your detection models?
We do not quote an accuracy figure in advance. We agree the test set and the target with you, report precision and recall on it, and show the misses as images so your team can judge them. That report is part of every baseline and pilot.
Testing on the line, not only on a laptop
A model that scores well on saved images can still struggle with motion blur, glare or a new shift’s habits. A supervised pilot, where people check what the model flags and what it passes, is the final test before it makes decisions alone.
Edge computer vision or cloud: where should the model run?
Run it at the edge when decisions must be instant, bandwidth is scarce or images should stay on site; run it in the cloud when volumes are bursty, models change often or there is no budget for devices at each location. Many systems do both.
Latency and bandwidth
A reject gate on a fast line needs an answer before the part passes, which rules out a round trip to a distant server. Several high-resolution cameras streaming all day can also cost more in bandwidth than a small computer beside the line.
Privacy and data residency
Processing on site means images need not leave the building, which can simplify contracts and privacy reviews. When only results, not images, are sent to the cloud, the data footprint is much smaller.
Portability between devices
Models can be exported to an interchange format such as ONNX, which describes itself as an open format for representing machine learning models across frameworks, runtimes and hardware. That keeps the choice of device open later. Our embedded software and IoT development teams handle device-side work.
Do you offer on-premise deployment?
Yes, where the scope calls for it. A model can run on a device beside the camera, on a server in your building or in your own cloud account. The written scope states where it runs, who maintains the hardware and how updates are delivered.
Cameras, lighting and calibration come before the model
Most image problems are solved with light and mounting, not with a bigger model. A consistent picture makes a smaller dataset enough; an inconsistent one makes any dataset too small.
| Item | What to decide | Common mistake |
|---|---|---|
| Resolution | Pixels across the smallest defect you must see | Choosing a camera before measuring the defect |
| Lighting | Diffuse, back or angled light, shielded from windows | Relying on ceiling lights that change by shift |
| Mounting | Fixed distance and angle, vibration isolated | Brackets that drift after cleaning |
| Trigger | Sensor or controller signal that fires each capture | Free-running video that catches parts half in frame |
| Calibration | Reference target imaged on a schedule | No check after a lens or light is replaced |
| Storage | Which images are kept, for how long, and where | Keeping everything, or keeping nothing for retraining |
Can computer vision work with existing CCTV cameras?
Often, for coarse tasks such as counting pallets, spotting a blocked aisle or confirming a truck is at a door. Security cameras are usually mounted high, wide and compressed, so fine inspection or label reading needs a dedicated camera closer to the object. The feasibility test uses your actual footage to decide.
Phones and tablets as cameras
For retail shelves, field photos and receiving checks, staff devices work well if the app guides the shot: distance, angle and a warning when the image is blurred. That guidance does more for accuracy than any later model change.
Tried a cloud vision API and it missed your parts?Share the images it got wrong and what it returned. We will tell you whether custom training, better images or a different camera position is the cheaper fix.
Build or buy: cloud vision APIs, packaged inspection systems or custom software
Buy when a general service already recognizes what you need or a packaged inspection system fits your station; build when your objects, defects or workflow are specific to you. We test the cheaper option first and recommend it when it is good enough.
| Option | Wins when | Limits to check |
|---|---|---|
| General cloud vision API | Generic labels, text reading or object finding are enough | Your parts and defects are not in its labels |
| Cloud service with custom training | You have labeled images and want a managed service | Per-image fees at volume; service roadmap changes |
| Packaged inspection system | A standard station, such as label or presence checks | Fixed features; hard to connect to your own software |
| Custom model and software | Your objects, defects or workflow are unusual | Needs data, labeling and ongoing ownership |
What the big cloud services offer today
Google lists label detection, object localization, text and document text detection and logo detection among the Cloud Vision API features, and says identifying specific individuals is not supported. Amazon describes Amazon Rekognition as pay-as-you-go with custom labels for objects specific to a use case. Microsoft’s Azure AI Vision overview covers OCR and image analysis and notes that the Image Analysis 4.0 service is deprecated and will be retired on September 25, 2028.
Why a service retirement date matters to buyers
That Azure notice is a reminder that managed services change. If a product depends on one, the design should keep your images, labels and evaluation set independent of the vendor, so moving to another service or to a custom model is a project, not a rebuild.
Which computer vision models do you use?
The one that fits the task, device and license terms. Common open building blocks include OpenCV, whose repository carries the Apache-2.0 license, and the PyTorch ecosystem, which its foundation describes as supporting computer vision work. We compare candidate models on your test set and record the license of every component in the handover.
For the wider trade-off between custom and packaged software, see custom software versus off the shelf.
Data ownership, privacy and the uses we decline
From the start of the project the code, data and cloud accounts are yours, including the labeled images and trained model files. We design vision systems around objects and processes, and we decline work whose purpose is to identify or profile people.
Who owns the images, labels and model
As on all our software development work, intellectual property is assigned to you in the contract and accounts are in your name. That includes the labeling guide and the evaluation set, which are often worth more than any single model version.
Biometric privacy laws: the Illinois example
Illinois’s Biometric Information Privacy Act defines biometric identifiers to include a scan of face geometry, while excluding photographs. It requires a private entity to inform people in writing and obtain a written release before collecting biometric identifiers, to publish a retention and destruction policy, and it lets an aggrieved person recover $1,000 per negligent violation or $5,000 per intentional or reckless one. Other states have their own rules, and your counsel decides what applies.
What we do not build
Face recognition, biometric identification, emotion detection, covert tracking of individuals, and anything aimed at minors are outside our scope. So is medical diagnosis software: the FDA page on software as a medical device describes software intended for medical purposes that works on its own. Healthcare systems that are not diagnostic devices are covered on our healthcare software page.
Designing for privacy on a site
Privacy is easier to design in than to add later. The defaults we propose:
- Point cameras at the work, not the workforce
- Blur or crop people before storage when they appear incidentally
- Keep only the frames needed for review and retraining
- Set a retention period for stored images and delete on schedule
- Send results, not images, to the cloud where the design allows
- Restrict who can view stored images, and log access
- Post notices where your counsel advises
How much does computer vision cost?
We price each project only once a written scope exists, because the price depends on your images, classes, accuracy target and deployment, none of which is known before the feasibility stage. What we can tell you up front is what moves the cost and how to keep it down.
| Factor | What pushes the cost up | How to keep it down |
|---|---|---|
| Number of classes | Many defect or product types to separate | Start with the two or three that cost you most |
| Rare cases | Defects that appear once in thousands of parts | Stage samples or collect for longer before training |
| Labeling effort | Outlines around irregular shapes, expert-only labels | Use boxes or whole-image tags where the decision allows |
| Image quality | Glare, motion blur, changing light | Fix lighting and mounting before labeling |
| Speed | Answers needed in milliseconds on a moving line | Slow the station or inspect a sample where acceptable |
| Deployment | Many sites, devices or offline operation | Pilot at one site, then roll out the proven setup |
| Integration | Writes into several systems with custom formats | One integration first, others after the pilot |
Is computer vision expensive to run once it is built?
Running costs are hosting or device hardware, cloud fees if a managed service is used, storage for retained images, and periodic review and retraining. Cloud services such as Rekognition charge as you use them, so a busy line can cost more over time than a one-off edge device.
Computer vision development cost versus project cost
The development cost is the build; the project cost adds cameras, lighting, labeling time from your experts and the change to the work process around the model. Our proposals list both, so the business case is not surprised later.
How much does image recognition cost compared with detection?
Whole-image classification is usually the cheapest to label and train; detection costs more because every object needs a box; pixel-level outlines cost the most. Choosing the simplest task that answers the business question is the biggest saving.
Six things make a vision project quick to scope. If you have them, the written scope can be specific from the first draft:
How to hire computer vision developers, and how to compare computer vision development companies
Hire computer vision developers through a project team, such as a computer vision software development company, when you want a delivered system, or add engineers to your own team when you already have product and data leadership. Compare computer vision development companies on how they test feasibility and report errors, not on demo videos.
| Model | Suits | What to ask for in writing |
|---|---|---|
| Fixed-scope project | A defined system with a clear acceptance test | Stages, artifacts, acceptance metric and stop points |
| Staged discovery then build | Unclear feasibility or data | A priced feasibility stage with a go or stop report |
| Team extension | Your engineers own the product and need capacity | Named roles, hours and how knowledge is handed over |
| Ongoing monitoring and retraining | Systems already in production | Review queue, drift reports and retraining cadence |
Our dedicated developer and software development outsourcing pages explain how team extension and full delivery work in general. For a first release with a narrow scope, the MVP development approach applies to vision features too.
| Requirement | How to check it |
|---|---|
| Starts with your images | Ask what they will test in the first stage and on whose data |
| Writes labeling rules | Ask for a sample labeling guide and how agreement between labelers is measured |
| Reports errors honestly | Ask for an example error report with images of misses and false alarms |
| Sets thresholds on your costs | Ask how they choose between fewer false alarms and fewer misses |
| Plans for drift | Ask what is logged in production and when retraining happens |
| Gives you ownership | Check the contract assigns code, data, labels and model files to you |
| Declines risky uses | Ask what they will not build and how privacy is reviewed |
| Recommends buying when it is enough | Ask when they last advised a client to use an off-the-shelf service |
Choosing among computer vision companies with US delivery
Buyers looking for computer vision development companies in USA time zones usually want overlap for working sessions, US contracts and a team that can visit a site when needed. Progression works remotely across the United States and worldwide, with site visits scoped where the camera setup requires them.
Red flags in a vision proposal
Treat any of these as a reason to slow down before signing:
- A guaranteed accuracy figure before anyone has seen your images
- No priced feasibility stage before the full build
- No mention of labeling rules or agreement between labelers
- A demo built only on public datasets or stock video
- No plan for cases the model is unsure about
- No statement of who owns the labels and trained model files
- Silence on privacy, or willingness to identify individuals by default
Need a vision feature inside your own product?Describe the feature, the devices your customers use and where the images live. We will scope the model, the app work and the deployment in one written proposal.
What AI assistants say about vision vendors, and what gets a company cited
Operations and product leaders now put vendor questions to AI assistants, whether Google AI Overviews, Gemini, ChatGPT, Copilot, Claude or Perplexity: which firms build custom inspection software, which computer vision software development company suits a packaging line, or whether a camera can count pallets. The assistants answer from pages that explain specifics, not from slogans.
How buyers phrase the question
Typical prompts name the task and the setting: a vendor for defect detection on a packaging line, how many images a model needs, whether edge or cloud suits a warehouse, what computer vision costs. A service page that replies to each of those directly, one plain sentence at a time, is what gets quoted.
Sources the assistants draw on
Vendor documentation for cloud services, standards and law texts for privacy questions, directories and comparison lists for vendor names, and service pages with clear process descriptions, tables and FAQs. Claims without detail are rarely repeated.
What a vision vendor or product company should publish
A page per use case with input, output and constraints; an honest data and accuracy section; a privacy statement naming what is not built; and a contact path. Our AI agent development and software consulting pages follow the same pattern for their topics.
Related software and AI services
Vision systems sit inside wider software, so a computer vision software development company has to build the pieces around the model too. These pages cover them.
- AI apps, document reading and language features
- Adding AI models to software you already run
- Deciding whether AI fits a problem
- Firmware and device software
- Connected sensors and device fleets
- Quality, traceability and plant systems
- Store, inventory and ecommerce systems
- Warehouse, yard and transport software
- Cloud services and APIs
- Vision features inside a SaaS product
- Adding engineers to your team
- Custom software development
Get a written scope for your vision project
Send a sample of your images, the decision you want automated and where the answer should go. We will send a free proposal from our computer vision software development company with the stages, artifacts and stop points, quoted after a written scope.
Getting found in search
AI, AEO and what is changing
Paid media and lead generation
Websites and design
Choosing and working with an agency
Software and app development
Website design by industry and type
Web development, platforms and hosting
Social, content and brand
By industry and by situation
Frequently asked questions
What do you get from hiring a computer vision software development company?
How much does computer vision cost for a first project?
Is computer vision expensive to keep running?
How much does image recognition cost compared with object detection?
How is computer vision used in industry outside manufacturing?
What problems does computer vision solve best?
What kinds of companies use computer vision?
How many images are needed for object detection?
How accurate is image recognition on real production images?
Can computer vision use the CCTV cameras we already have?
Can the model run on our own servers instead of the cloud?
Which computer vision models and libraries do you use?
How do I hire computer vision developers for a short project?
What should I prepare before contacting computer vision development companies?
Who owns the trained model and the labeled data?
Do you build face recognition or biometric identification systems?
What is the difference between machine vision and computer vision?
Should we use a cloud vision API instead of a custom model?
What happens when the model starts making more mistakes?
What is a computer vision proof of concept?
How do you build a computer vision application end to end?
Does Progression take on vision projects for companies outside New York?
Have a visual check people do by eye all day?Send twenty photos of the good and bad cases and describe the decision made on each one. We will reply with a written feasibility scope for a first model on your own images.
Get a free marketing proposal
Tell us what you are trying to grow and we will come back with a plan, not a pitch deck. Same-day reply on weekdays.