- Synthetic Data. We train computer vision models to detect product-specific defects such as surface flaws, weld issues, and missing components. It can all be done in real time at full speed. When real defect samples are limited, we can use synthetic data to expand training coverage and improve model performance.
- Production Validation. Before launch, we validate the model in your production environment. We ensure it works on the test bench and makes accurate calls on the factory floor. Multi-camera validation helps reduce blind spots on complex parts.
- Edge-First Deployment. We compress the models so they run directly on the camera without needing to send all the footage to the cloud. This delivers sub-50ms responses while keeping footage inside your network.
- Data Annotation. We handle data collection, cleaning, and labeling, which are often the most time-consuming parts of model training. We keep the training set accurate, representative, and current as your products and processes evolve.
- System Integration. We connect the vision system to the production software, machines, and tools your operators already use. After we’re up and running, we’ll keep an eye on how the model is doing and let you know if there’s any drift; we can even do scheduled retraining to keep it accurate over time.
Computer Vision Development Services
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IMPROVE OPERATIONAL EFFICIENCY
Cut defects and waste by 70% with real-time vision systems and autonomous workflows -
DEPLOY EDGE AI
Run high-accuracy vision models with sub-50ms inference and automated lifecycle management -
ELIMINATE MODEL DRIFT
Preserve model accuracy with automated monitoring and retraining
Why choose Devox Software?
What We Offer
Comprehensive Data Readiness
Advanced synthetic data generation helps create balanced, high-quality training sets. End-to-end annotation pipelines build highly accurate models from day one.
Uninterrupted Operational Precision
Implementing continuous drift monitoring sustains model accuracy across seasonal shifts and facility updates. Automated retraining helps the system stay accurate and reliable as conditions change.
Low-Latency Edge AI
Compressing neural networks for direct on-device execution achieves immediate, sub-50ms response times for high-speed environments. Local edge inference keeps proprietary footage inside your private network.
Autonomous Workflow Execution
Vision systems integrated with ERP and MES platforms can trigger operational actions automatically. Upgrading manual tracking creates self-managing workflows and optimizes resource allocation across the facility.
What We Deliver
Our Computer Vision Development Services
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Custom Computer Vision Development
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Catching Defects in Real Time
- Live sorting. We process images from high-speed cameras to identify surface, weld, and assembly defects in real time. We can even automatically sort defects, reducing waste and rework by 40-70%.
- Defect Location. We use pixel-level segmentation to tell exactly where the problem is and how big it is so your operators can go fix it on the spot. Operators can address issues immediately instead of waiting until the end of the line.
- On-Device Inference. Our models are compressed to run directly on the camera and deliver an answer in under 50ms. It’s fast, and it keeps all your sensitive footage inside your network, so you don’t have to worry about compliance on regulated lines.
- Synthetic Data. It can be difficult to collect enough real examples of each defect type. We use synthetic data to give the model balanced coverage across edge cases.
- Drift Monitoring. We merge images from multiple cameras to get a single view of the whole part, and we monitor the model’s performance over time. If performance starts to decline, the system alerts the team and triggers retraining.
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Predictive Quality Assurance
- Process Drift Detection. The system tracks visual trends across each run to detect tool wear, misalignment, and surface degradation before parts begin to fail. That gives operators a warning and the chance to make mid-run adjustments before problems escalate.
- Failure Forecasting. Condition monitoring on tooling and equipment is able to score each station by how high the risk of failure is and predict when the next breakdown is likely to hit, turning unplanned stops into scheduled maintenance.
- Root-Cause Analytics. We link process variables like speed, temperature, and material batch to emerging quality risks to find the cause of recurring failures. And by doing that, we pin down the actual cause so that process engineers can fix the source of the problem once and for all, rather than just treating the symptoms.
- Yield Forecasting. With all this trend data, we can project the expected quality and scrap rates for upcoming runs and material batches, allowing planning to commit to realistic output before a shift even starts.
- Process Health Dashboard. All these predictions feed into a live view of line health that shows emerging risks and sends alerts to the systems that operators already monitor. And for leadership, it provides a clear overview of the whole plant’s quality trajectory and how it’s trending, with thresholds all pulled together for each station.
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Edge AI for Logistics
- Asset Tracking. We use ceiling and gate cameras to follow pallets, parcels, and forklifts across the floor in real time, tracking their location and dwell time without having to manually scan anything. Warehouse managers can see where stock is and where delays occur, reducing the time they spend searching for misplaced loads.
- Safety Monitoring. On-site models detect missing PPE, restricted-zone entry, and forklift-pedestrian proximity, then send real-time alerts to floor supervisors. Because we process the footage locally, we can keep all the worker data inside the network.
- Damage Inspection. We use cameras to inspect the freight for crushed corners, torn packaging, and seal breaks and flag any damaged units for claims at the point of receipt. Timestamped evidence helps reduce liability disputes later in the claims process.
- Parcel Sorting. Our omnidirectional readers can capture barcodes and labels at full belt speed and route each parcel to the right lane and catch any unreadable or mislabeled items before they ship off wrong. We don’t need to add any extra scan stations to manage that, even during peak volume.
- Load Counting. The system counts cartons, pallets, and SKUs against the manifest. Short shipments and overages are flagged on arrival, reducing manual counts and improving inventory accuracy.
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Document AI
- Document Capture. Models read printed and handwritten text, tables, and checkboxes from scans and phone photos, holding accuracy on low-resolution, skewed, or faded pages where plain OCR breaks.
- Field Extraction. Instead of dumping raw text, models pull the fields that matter—invoice totals, dates, PO numbers, and party names—and map them straight to your schema. Finance and ops get structured records ready for the system rather than a wall of characters.
- Validation Checks. Extracted values are cross-checked against business rules and reference data, flagging mismatched totals, missing signatures, or out-of-range entries for human review. Error rates drop while staff touch only the exceptions instead of every document.
- Multi-Format Handling. A single pipeline handles invoices, IDs, labels, contracts, and medical forms, recognizing the layout for each document type without requiring a separate tool. Adding a new form variant tunes the existing model rather than rebuilding from scratch.
- System Integration. Structured output writes directly into ERP, CRM, or document management through API and middleware layers, so records land where staff already work. Sensitive documents stay processed inside your environment to satisfy compliance reviews.
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Video Analytics
- Object Tracking. Models follow people, vehicles, and equipment across a live feed, holding identity through occlusion and crowding instead of losing the target when paths cross. Operations see continuous movement paths rather than disconnected frame-by-frame detections.
- Event Detection. Models recognize defined events in the stream, such as loitering, line crossing, abandoned objects, and sudden crowding, then send alerts in real time.
- Flow Analytics. Movement across a space is aggregated into heatmaps, dwell times, and path patterns, showing where people cluster and where they stall. Retail and facility managers redesign layouts based on real behavior, not guesswork.
- Multi-Camera Fusion. Feeds from many cameras merge into one continuous view, handing tracking from one lens to the next so a subject stays identified across a whole site. Coverage holds across blind spots that any single camera leaves.
- Pose Estimation. Models read body posture and movement to recognize actions, falls, fights, unsafe lifting, and gesture cues beyond what bounding boxes alone capture.
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Vision-to-Workflow Integration
- Action Triggers. A vision result can automatically trigger the next step, such as rejecting an item, stopping a line, creating a maintenance ticket, or rerouting a parcel. The model drives the process instead of leaving a human to read a dashboard and react.
- System Connectors. Purpose-built middleware links vision output to ERP, MES, WMS, and PLC over the protocols they actually speak, reading and writing where your operators already work. Records and commands flow both ways without a parallel system to maintain.
- Agentic Workflows. Multi-step responses run on their own; a flagged defect updates inventory, notifies the supervisor, and adjusts the upstream station in one chain. The system handles routine decisions that previously required manual review at every step. Low-confidence calls route to a reviewer with the frame and context attached, and the decision feeds back as labeled data.
- Real-Time Sync. Vision events stream to dashboards, alerts, and downstream systems with millisecond latency, so the floor and the back office act on the same state at the same moment.
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CV MLOps
- Drift Monitoring. Live metrics track accuracy against ground truth and flag the moment performance starts decaying, from new lighting, worn cameras, or changed products, before problematic calls reach production. Operations sees model health as a number instead of discovering decay through a defect that slipped past.
- Automated Retraining. Pipelines collect fresh edge cases from the floor, retrain on a schedule or trigger, and roll updated models out with validation gates. Accuracy remains stable across seasons and process changes without requiring a complete rebuild each time.
- Model Optimization. Networks are compressed and accelerated for the target hardware, hitting the latency budget while holding accuracy on the device you actually deploy to. Large lab models are optimized to run in real time on the production line.
- Data Management. Versioned pipelines handle collection, cleaning, labeling, and class balance, keeping the dataset representative as conditions shift. The noise and imbalance behind most stalled projects are resolved before they reach training.
- Version Control and Rollback. Every model, dataset, and metric is tracked, so a regression rolls back to the last good version in minutes, and every change stays auditable.
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Custom Multimodal Vision AI
- Visual Search. Foundation models match images against your catalog or archive by content rather than tags, returning similar products, parts, or frames from a photo or a phrase. Users find the right item without knowing how it was labeled.
- Generative Reporting. Models read a scene and write the summary, an inspection report, an incident description, and a shift digest, turning raw frames into a document a person can act on. Reports that once took an analyst an hour can be drafted in seconds for review.
- Cross-Modal Fusion. Image, video, text, and sensor data combine into one model so context from one stream sharpens another, like a label, a timestamp, and a frame read together. Decisions draw on the full picture rather than a single feed in isolation.
- Agentic Vision. Foundation models fine-tuned on your domain can reason across steps and adapt to new tasks with little custom data, so one platform can cover cases that a narrow model would need to be rebuilt for. You can add a new use case by tuning the existing system instead of starting from scratch.
Insurance
AI-Powered Architecture Discovery for a Legacy Monolith
A safe, downtime-free modernization of a mission-critical monolith mobile app through AI-assisted architecture discovery to reveal dependencies and design an incremental, audit-friendly roadmap.
Additional Info
Core Tech:
- .NET 8
- ASP.NET Core
- Azure AKS
- Azure DevOps
- OpenTelemetry
- Grafana
- Kafka
- Redis
- PostgreSQL
- Azure Key Vault
Country:
USA
LEGACY MODERNIZATION
AI Dependency Mapping in Action: Cutting Modernization Costs for a Fintech Platform by 30%
An AI-driven fintech modernization that mapped 450k lines of dependencies, cut costs by 30%, and enabled zero-downtime migration to Azure.
Additional Info
Core Tech:
- .NET Framework
- .NET 8
- C#
- SQL Server
- Azure Functions
- Azure App Services
- micro-frontends
- Terraform
- GitHub Actions
- SonarQube
Country:
USA
LEGACY MODERNIZATION
Enabling Zero-Downtime Modernization for a Customer-Facing Application
An AI-accelerated modernization of a high-traffic e-commerce platform, enabling zero-downtime migration to Azure microservices.
Additional Info
Core Tech:
- .NET Framework
- .NET 8
- C#
- SQL Server
- Azure Kubernetes Service
- Azure App Services
- microservices
- AI dependency mapping
- Terraform
- GitHub Actions
Country:
USA
Testimonials
Testimonials
Sweden
The solutions they’re providing is helping our business run more smoothly. We’ve been able to make quick developments with them, meeting our product vision within the timeline we set up. Listen to them because they can give strong advice about how to build good products.
Carl-Fredrik Linné
Tech Lead at
CURE Media
United States
We are a software startup and using Devox allowed us to get an MVP to market faster and less cost than trying to build and fund an R&D team initially. Communication was excellent with Devox. This is a top notch firm.
Darrin Lipscomb
CEO, Founder at
Ferretly
Australia
Their level of understanding, detail, and work ethic was great. We had 2 designers, 2 developers, PM and QA specialist. I am extremely satisfied with the end deliverables. Devox Software was always on time during the process.
Daniel Bertuccio
Marketing Manager at
Eurolinx
Australia
We get great satisfaction working with them. They help us produce a product we’re happy with as co-founders. The feedback we got from customers was really great, too. Customers get what we do and we feel like we’re really reaching our target market.
Trent Allan
CTO, Co-founder at
Active Place
United Kingdom
I’m blown up with the level of professionalism that’s been shown, as well as the welcoming nature and the social aspects. Devox Software is really on the ball technically.
Andy Morrey
Managing Director at Magma Trading
Switzerland
Great job! We met the deadlines and brought happiness to our customers. Communication was perfect. Quick response. No problems with anything during the project. Their experienced team and perfect communication offer the best mix of quality and rates.
Vadim Ivanenko
COO at
Optherium
United States
The project continues to be a success. As an early-stage company, we're continuously iterating to find product success. Devox has been quick and effective at iterating alongside us. I'm happy with the team, their responsiveness, and their output.
Sweden
We hired the Devox team for a complicated (unusual interaction) UX/UI assignment. The team managed the project well both for initial time estimates and also weekly follow-ups throughout delivery. Overall, efficient work with a nice professional team.
John Boman
Product Manager at
Lexplore
Canada
Their intuition about the product and their willingness to try new approaches and show them to our team as alternatives to our set course were impressive. The Devox team makes it incredibly easy to work with, and their ability to manage our team and set expectations was outstanding.
Tamas Pataky
Head of Product at
Stromcore
Estonia
Devox is a team of exepctional talent and responsible executives. All of the talent we outstaffed from the company were experts in their fields and delivered quality work. They also take full ownership to what they deliver to you. If you work with Devox you will get actual results and you can rest assured that the result will procude value.
Stan Sadokov
Product Lead at
Multilogin
United Kingdom
The work that the team has done on our project has been nothing short of incredible – it has surpassed all expectations I had and really is something I could only have dreamt of finding. Team is hard working, dedicated, personable and passionate. I have worked with people literally all over the world both in business and as freelancer, and people from Devox Software are 1 in a million.
Mark Lamb
Technical Director at M3 Network Limited
Insights
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FAQ
Frequently Asked Questions
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How do you validate the exact ROI before we commit to a full deployment?
We present your buying committee with a clear financial model during the initial discovery phase. Our team defines the required hardware, target metrics, and total cost of ownership upfront so stakeholders understand the investment needed to move from proof of concept to enterprise deployment.
By setting financial guardrails early, you keep control of the budget at each stage. Each phase moves forward only when the business case is clear and measurable.
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How do you ensure the model maintains accuracy on the actual factory floor?
We design computer vision models for real industrial environments, not controlled lab conditions. During validation, we test the system against the same factors your facility deals with every day, including changing light, glare, motion, and residue on the lenses.
Once the system is live, our MLOps pipeline monitors model health and triggers retraining when conditions change. That helps maintain accuracy through seasonal variation, process updates, and other shifts in the operating environment.
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How do we build these systems when we have limited labeled data?
We build the full data pipeline for you. Our team handles the entire process of gathering, cleaning, and annotating your visual data. We supplement your existing data with synthetic data to improve coverage across operational edge cases.
This strategy builds accurate models, even when the initial data volume is limited. The system learns the full spectrum of your product variables right from the start.
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How does the vision system interface with our existing ERP and MES platforms?
We build integration middleware that works with the protocols your ERP and MES platforms already support. The vision system can read and write data within the environments your operators already use.
This approach preserves your current software stack and supports reliable data flow across the facility. The AI layer extends your existing infrastructure without disrupting day-to-day operations.
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How do you protect sensitive operational data and align with US security frameworks?
We deploy the system at the edge so sensitive operational data remains within your private network. Processing video locally on the device reduces exposure and gives you stronger control over proprietary information. Our engineering approach aligns with U.S. security frameworks, including SOC 2 Type II, NIST CSF 2.0, and relevant SEC requirements.
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