AI in your business, where it actually helps
We look at your processes on site, apply AI in a few fitting places and run it afterwards, with one point of contact.
- A direct line to your engineer
- Operation including hardware replacement
- Planned and built for your business
Most AI projects in small and mid-sized businesses fail not because of the model but because of unclear processes and scattered documents. That is why we start with your process, not with a tool.
How does NDVDL introduce AI in a business?
First we look at your processes and documents on site and pick a small first step, such as reading receipts, searching your own documents or pre-sorting emails. After a pilot we implement it, run it and keep looking after it, with EU hosting or on your own premises if you wish. If a project is premature, we say so openly.
Where AI can help in a business
Six areas we see in businesses. Each comes with what has to be in place first and where the limit is.
Reading receipts and documents
Invoices, delivery notes and orders are read and the fields are passed on to your system.
- Prerequisite
- The documents are digital and the target system has an interface.
- Limit
- Poorly legible scans and unusual layouts need to be checked by a person.
Preparing quotes
From an enquiry and earlier quotes, a draft is created that your sales team only needs to review.
- Prerequisite
- Earlier quotes and the pricing logic are findable and up to date.
- Limit
- Prices and commitments are still approved by a person.
Knowledge search in your own documents (RAG)
Staff ask in plain language; the answer draws on manuals, protocols and contracts and names its source.
- Prerequisite
- The documents are filed clearly and kept up to date.
- Limit
- If the filing contradicts itself, the answer is just as uncertain, so the cited source should be checked.
Pre-sorting emails
Incoming messages are sorted by topic and reply drafts are prepared.
- Prerequisite
- There are clear categories and responsibilities in the team.
- Limit
- Drafts are not sent automatically; sensitive and decisive matters stay with people.
AI agents with a human in the loop
An agent calls tools in your systems via MCP, such as stock or CRM, and prepares work steps.
- Prerequisite
- The systems have an interface and the agent only gets tightly limited rights.
- Limit
- Anything that takes effect outside, such as an order, email or booking, only happens after approval and is logged.
Quality inspection by image recognition
Cameras check parts or processes for deviations right at the line.
- Prerequisite
- Capture conditions stay constant and there are sample images of good and bad parts.
- Limit
- Whether it works for your part and your lighting only a pilot can show.
How we work
- 01
Look at the process on site
We look at how work really gets done at your company: where time goes into retyping, searching and asking around, and what data exists. Often the first finding is tidier documents, not yet AI.
- 02
Small pilot
We pick one narrowly defined use case and test it with your real data. If it turns out a project is premature, we say so openly.
- 03
Implementation
We connect the solution to your existing systems, define who checks and approves, and brief the people involved.
- 04
Operation and support
We run the solution, keep models and connections up to date and remain your point of contact, including for the servers and network underneath.
Data protection: where your data runs
Which option fits depends on the data being processed. We decide that together with you, based on your actual documents.
Cloud service from an external provider
Requests go to the provider's servers. This is quick to set up and suits content that involves no personal data or trade secrets.
Personal data and trade secrets belong here only once the provider's contract and location have been checked.
EU hosting
The model runs at a provider with a data centre in the EU, covered by a data processing agreement. A middle path if you do not want to run your own server.
The provider's contract, location and sub-processors have to fit.
On-premise
The model runs on hardware that you own. Requests and data do not leave your network.
It needs suitable servers and ongoing operation, which we take care of.
This is a technical overview, not legal advice. Whether a specific use is permitted is something to clarify with your data protection adviser; we supply the technical details for that.
Image recognition: cameras and image analysis
Object Detection
- Real-time object detection
- Multi-object tracking
- Anomaly detection
Image Analysis
- Image classification
- Semantic segmentation
- Facial recognition & analysis
Sensor Integration
- Camera system integration
- Multi-sensor fusion
- Edge computing solutions
Data Analytics
- Visual data analytics
- Behavioral analysis
- Predictive analytics
Image recognition in practice
- 01
Industrial Quality Control
Automated visual inspection and defect detection
- 02
Security & Surveillance
Intelligent video surveillance and alerting
- 03
Retail Analytics
Customer flow and behavior analysis in retail
- 04
Autonomous Vehicles & Robotics
Computer vision for autonomous systems
What image recognition brings
Enhanced Security
Automatic detection of hazards and anomalies
Process Automation
Reduction of manual inspection tasks
Data-Driven Decisions
Insights through visual data analysis
Real-time Monitoring
Permanent monitoring with instant notification
Scalability
Flexible expansion to any number of cameras
Cost Efficiency
Optimized resource use through automation