Since 2010
Building Software
Cameras In. Decisions Out.
Computer Vision Development Services
We build computer vision systems that detect, inspect, and measure in production: quality inspection, safety monitoring, and video analytics, with accuracy validated on your data before build is committed.
- 120+
- AI Engineers
- 15+
- Years Building Software
- 9,000+
- Projects Delivered
- 25+
- Countries Served
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
insight
What makes vision systems hard, and how we handle it
Vision demos are easy; production is hard. Lighting changes, cameras drift, edge cases arrive in week three, and an accuracy number from a clean dataset tells you nothing about your factory floor or your store. We validate feasibility on your actual footage before quoting, and we define accuracy per class with error costs, because a missed defect and a false alarm are not the same problem.
Delivery includes the surrounding system: camera and edge deployment, review queues for uncertain cases, and retraining workflows when the world drifts. For the wider AI engineering context, see our AI development services.
What our computer vision services cover
From proof of concept on your footage to monitored production systems.
Visual quality inspection
Automated defect detection for manufacturing lines, with per-defect accuracy reporting and human review for the uncertain band.
- ✓Defect classification
- ✓Uncertainty routing
- ✓Line integration
- ✓Accuracy per class
Object detection and tracking
Detecting and tracking objects in video for safety, logistics, and analytics use cases, tuned to your scene.
- ✓Custom class training
- ✓Multi-object tracking
- ✓Zone and rule logic
- ✓Event alerting
Video analytics
Understanding footfall, flow, and behaviour from existing camera estates, with privacy safeguards designed in.
- ✓Footfall and flow metrics
- ✓Dwell analysis
- ✓Anonymised processing
- ✓Dashboard delivery
OCR and document vision
Reading text from documents, labels, and screens with validation against your data schemas.
- ✓Field extraction
- ✓Validation rules
- ✓Low-confidence queues
- ✓Downstream integrations
Edge deployment
Optimised models running on on-site hardware where latency, bandwidth, or privacy rules out the cloud.
- ✓Model quantisation
- ✓Edge hardware selection
- ✓OTA update flows
- ✓Offline resilience
Model retraining workflows
Continuous improvement loops that turn production edge cases into labelled data and better models.
- ✓Hard-example mining
- ✓Labelling pipelines
- ✓Regression evals
- ✓Versioned deployments
What every engagement includes
Every computer vision project ships with the following, agreed in writing before the first sprint begins.
Discovery workshop
A structured workshop maps your goals, users, and constraints into a scoped computer vision roadmap with a fixed written quote before any sprint starts.
Named senior team
You get named, senior engineers with CVs you can review, not a rotating bench. The people in the kickoff are the people who ship.
Sprint demos every two weeks
Working builds are demoed live each cycle with CI running on every merge, so progress is visible in software, not slideware.
100% source-code ownership
All computer vision source, configurations, designs, and documentation transfer to you on delivery with no licensing strings or vendor lock-in.
60 days of post-launch support
Engineering time is on standby for two months after go-live to triage defects, tune performance, and stabilise the release at no extra cost.
12 months of free updates
Compatibility updates, security patches, and platform upgrades ship free for a full year after handover.
What you can build with computer vision
Systems our clients run in production, each validated on their own footage.
Production line inspection
Automated defect detection integrated with line controls, catching defects human inspection misses at speed.
Workplace safety monitoring
PPE and zone-compliance monitoring on existing cameras, alerting supervisors to genuine risks rather than noise.
Retail analytics
Footfall, queue, and shelf analytics from existing store cameras, processed with privacy safeguards.
Document and label processing
Reading and validating documents, labels, and meters at volume, feeding structured data to your systems.
Vehicle and asset tracking
Detection and tracking of vehicles and assets across sites for logistics and security operations.
How a computer vision Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your vision goals, users, and constraints in a structured workshop, ending in a fixed written quote and an architecture risk list.
✓ step 01 complete - 02
Solution design
The computer vision architecture, integrations, and delivery plan are designed and reviewed before build work starts, so scaling later is not a rewrite.
✓ step 02 complete - 03
Foundation sprint
Repositories, CI, environments, and a walking skeleton are set up first. Something real runs on a staging URL before feature sprints begin.
✓ step 03 complete - 04
Sprint delivery
Two-week sprints with live demos every cycle. computer vision work is reviewed, tested, and merged continuously, so scope surprises surface early.
✓ step 04 complete - 05
Hardening and launch
Performance, security, and accessibility checks run against agreed budgets, followed by a staged vision launch with monitoring from hour one.
✓ step 05 complete - 06
Support and handover
60 days of included support, 12 months of free updates, and documentation that lets your internal team own the system with confidence.
✓ step 06 complete
The stack behind our vision builds
Default choices, stated with reasons.
Models
Modern detection and segmentation architectures fine-tuned on your data, with model size matched to the deployment target.
Data tooling
Annotation workflows with quality checks and hard-example mining, because labelled data quality caps achievable accuracy.
Deployment
Cloud or edge depending on latency and privacy constraints, with quantised models and monitored inference in both.
Evaluation
Per-class accuracy, error cost analysis, and production drift monitoring, reported in metrics your quality team can audit.
How intelligence flows through the system
What the model reads, and what it is allowed to change.
Capture layer
Ingests frames from cameras or document scanners with health monitoring.
Reads
Camera streams, scanners, image stores
Changes
Frame buffers and capture logs
Vision models
Detect, classify, and measure according to task configuration.
Reads
Frames and calibration data
Changes
Detection records and events
Action layer
Routes events, alerts, and uncertain cases to the right place.
Reads
Confidence scores, rule definitions
Changes
Alert queues and line-control signals
What's included, standard
The baseline on every computer vision engagement. Not an upsell tier, just the floor.
Fixed written quote
The price agreed at discovery is the price you pay, with change handled through explicit scope changes rather than silent drift.
Solution architecture document
System design, data flows, and technology decisions written down so your team can audit and own the result.
Dedicated project manager
One accountable point of contact with real authority over the delivery, not a message relay between time zones.
QA on every build
Automated and manual testing on each computer vision release candidate, with defects triaged before anything reaches production.
Security review
A checklist-based security pass covering authentication, data handling, and dependency vulnerabilities before launch.
CI/CD and environments
Pipelines, staging, and preview environments configured from week one, so releases are routine rather than dramatic.
Analytics and monitoring
Instrumentation wired before launch so you can see real user behaviour and system health from day one.
Documentation and handover
Code comments, runbooks, and architecture notes written for your next developer rather than for our convenience.
Feasibility study on your footage
An accuracy baseline measured on your actual images or video before build is committed.
Case studies that speak volume
Etihad Airways
A high-traffic consumer platform our team ships and operates at national-carrier scale, the same bar every build is held to.
1M+
Monthly Users
40%
Engagement Lift
PragPlay
A consumer platform from the same engineering system, proving the infrastructure core holds under real-time load with strict uptime demands.
500K+
Players
99.9%
Uptime
Marvel Studios
A high-volume consumer experience our team ships at global-release scale, the same discipline applied to every launch.
10M+
Streams
65%
Completion
/11
Why teams pick us for computer vision
The differences live in engineering habits, not pitch decks.
Feasibility before the quote
We measure baseline accuracy on your real footage before pricing the build, so the number you see is achievable, not aspirational.
Error costs made explicit
A missed defect and a false alarm cost different things. We tune thresholds to your economics and document the trade-off.
Edge and cloud fluency
We deploy where the constraints point, with quantised models and update workflows for on-site hardware.
Drift is expected
Retraining workflows ship with the system, because cameras and lighting drift and models that cannot learn will decay.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with 120+ AI engineers on the bench.
Computer vision development FAQs
$ moxace launch --nowReady to Put Vision in Production?
Talk to our computer vision team about a feasibility-first build with measured accuracy and full source ownership.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
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