Machine Learning and Computer Vision: Forecasts from your data, decisions from your images.
We build models for forecasting, computer vision, document processing and natural language processing, and integrate them into your live systems.
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Machine Learning and Computer VisionIllustrative interface, sample data
In short
Machine learning and computer vision solutions use models that learn from your historical data or camera images to automate decisions such as demand forecasting, failure prediction, quality inspection and document reading. CoreTechs starts every model with a feasibility study on your own data, then trains it and integrates it into your production line, ERP or business applications. Once live, the model's accuracy is monitored and it is retrained as your data changes.
Scope
Forecasting
- Demand and sales forecasting
- Customer churn analysis
- Price optimization
- Failure prediction (predictive maintenance)
Computer vision
- Quality control on production lines
- Object and license plate recognition
- Shelf and stock analysis
- Occupational safety equipment detection
Document processing
- Automatic data extraction from invoices, waybills and ID documents (OCR + AI)
- Document classification
Natural language processing
- Sentiment analysis of customer reviews
- Request and complaint classification
Deliverables
- Feasibility report
- Trained model
- Integration into your live system
- Accuracy and performance report
- Model monitoring
Who is it for?
- Manufacturers that inspect quality by eye at the end of the line and want fewer defective products reaching customers
- Retail, distribution and e-commerce companies that want stock, purchasing and capacity planning based on a more reliable demand forecast
- Operations that want to use sensor and maintenance data to anticipate machine downtime
- Finance and operations teams that manually key in data from invoices, delivery notes and ID documents
- Customer experience teams that want to classify reviews, requests and complaints automatically
- Sites that want to monitor safety equipment use, shelf layout or license plates with cameras
How we work
Feasibility
We define the decision to be automated and the success metric, and assess whether your existing data is sufficient. We run a short trial on your own data and report the achievable accuracy and the risks.
Data collection and labeling
Where needed, we specify site conditions such as camera position, lighting and resolution, and prepare images and records under clear labeling rules. Label quality is verified with your domain experts.
Model development
We choose the right method for the problem, train the model and test it on data it has never seen. We analyze where wrong predictions cluster and improve the model accordingly.
Integration
We connect the model to your production line software, ERP, business application or expose it as an API. For computer vision, we can run the model on edge devices on site.
Pilot and rollout
We first run the model on a limited line, store or process alongside the current method and compare results. Once results meet expectations, we expand the scope.
Monitoring and retraining
We monitor live accuracy and shifts in data distribution. When products, seasons or site conditions change, we retrain the model on new data.
How long does it take?
In machine learning projects, data is the main factor driving the timeline: how much usable historical data exists, whether it is labeled and how variable site conditions are. For computer vision, camera and hardware installation also affect the schedule. After a free 30-minute intro call we usually start discovery within a week, and confirm the exact timeline in the proposal that follows feasibility and discovery.
Phases
- Intro callA free 30-minute call to discuss the problem, your existing data and the decision you need.
- FeasibilityA short trial on your own data and a report showing achievable accuracy.
- Discovery and fixed proposalWe clarify data preparation, labeling, hardware and integration scope, then share a fixed timeline and price.
- Development and pilotModel development, integration and parallel running on site, in two-week sprints.
- Launch and handoverGo-live, model monitoring setup, team training, and transfer of source code and intellectual property.
What drives the cost?
Cost is usually driven less by the model itself than by data preparation, on-site setup and integration. A feasibility study makes these items visible before you commit to a large budget.
| Factor | Effect on cost |
|---|---|
| Data preparation and labeling | Clean, labeled data speeds up development; labeling images or records from scratch increases effort significantly. |
| Problem type and accuracy target | Separating obvious defects and catching very small, rare ones require different amounts of data and modeling work. |
| On-site hardware | Cameras, lighting and edge devices are a separate cost in computer vision projects; this shrinks if existing cameras can be used. |
| Runtime environment | A model running in the cloud and one running in real time on the line need different infrastructure and optimization. |
| Number of integrations | Writing predictions automatically into an ERP, MES, inventory system or business application means separate development and testing for each system. |
| Monitoring and retraining frequency | Where product ranges and conditions change often, the model needs more frequent updates, which is reflected in the maintenance scope. |
Technology options
Modeling
- Python
- PyTorch
- scikit-learn
- Time series forecasting methods
Computer vision
- OpenCV
- Object detection and segmentation models
- Edge deployment
Document and language processing
- OCR
- Field extraction with large language models
- Text classification
Data and service layer
- PostgreSQL
- SQL Server
- REST APIs
- Docker
Infrastructure
- AWS, Azure or Google Cloud
- On-premise servers
- Kubernetes
Example solution scenario
Computer vision quality inspection on a production line
Representative scenario, not a real client project.
- Profile
- A manufacturing plant where packaging and label checks are done by eye at the end of the line.
- Need
- On a fast-moving line, defects such as shifted labels, missing print and damaged packaging could slip through. Inspection quality varied by shift and fatigue, and defective products occasionally reached customers.
- Approach
- During feasibility, we used images collected from the existing line to test whether defects could be reliably detected by camera.
- We fixed camera position and lighting to standardize image conditions.
- Together with the quality team, we defined defect classes and labeled the images.
- We ran the model on an edge device on the line and connected it to the reject mechanism and the quality records system.
- For a period, we ran the model in parallel with manual inspection and reviewed every case where they disagreed.
- Technologies
- Python
- PyTorch
- OpenCV
- Edge device
- PostgreSQL
- Quality dashboard
- Outcome
- Inspection now follows the same criteria on every shift, and staff focus only on the products the model flags. Recording defects by type and time also started to be used to fine-tune the machine settings causing them.
Frequently asked questions
How much data do we need to build a model?
The amount depends on the problem and the accuracy you need, which is why we start with a feasibility study. We run a short trial on your existing data and report whether it is enough, and if not, how much and what kind of data should be collected.
Do we need new cameras for computer vision quality inspection?
Not always. If the resolution, angle and lighting of your existing cameras are adequate, we can work with them. If defects are very small or the line is very fast, we include camera and lighting recommendations in the feasibility report.
What happens if the model makes a wrong decision?
No model is right every time, so we measure the error rate before launch and agree on an acceptable threshold with you. For critical decisions, we route cases where the model is uncertain to human review. Live accuracy is monitored continuously.
Where are our data and images processed, and what about data protection?
Models can run on your on-premise servers, on edge devices on site or in the cloud environment you prefer. If images include people, we design in measures such as face blurring, retention periods and access permissions. A non-disclosure agreement is signed before the project starts.
Can a machine learning forecasting model connect to our ERP?
Yes. We push predictions into your ERP, inventory or planning systems through API or database integration, so demand forecasts and failure alerts appear on the screens your teams already use.
What do cost and timeline depend on?
The main factors are whether data is ready, labeling needs, on-site hardware and the number of integrations. We usually start discovery within a week of the intro call and confirm a fixed timeline and price in writing after feasibility and discovery.
Do models go stale, and do they need maintenance?
Yes. As products, seasons and site conditions change, accuracy can drop. We set up monitoring of live performance and retrain the model on new data when needed, and can take this on as part of a maintenance and support package.
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