AI solutions for business
We implement AI where it shortens specific work: with documents, data and knowledge scattered across the company. If your problem does not need AI, you will hear that from us at the start.
People retype what a machine can read
The team spends hours reading documents, re-entering data and searching for information in files, messages and systems. The work is repetitive and prone to error, and hiring more people is a poor way to scale it.
- Data from invoices, contracts, specifications and product sheets re-entered by hand
- Company knowledge in documents that are hard to reach
- The same questions put again and again to the same people
- Concern about data confidentiality in public AI tools
AI in the process, under human control
We start by assessing whether the problem requires AI at all and what effect is realistic. We implement the model together with the whole process around it: data preparation, verification of results, handling of uncertain cases and a human approval path.
Before we start, we agree the rules for data processing: where the data goes, who has access to it and how long it is stored.
What you get
- An assessment of where AI delivers a measurable effect and where it is a waste of money
- A solution with quality control of results and human verification
- Clear rules for data processing and confidentiality
What we implement
- Document automation
- Classifying, reading and routing documents to the right process.
- Data extraction
- Structured data extracted from PDF files, scans and supplier documentation.
- Internal assistants
- Search and answers based on company documents, with references to sources.
- Integration with systems
- Results go straight into the CRM, ERP or another system. Nobody copies them by hand.
- Quality evaluation
- Test sets and accuracy measurement before deployment and during operation.
- Feasibility workshop
- A short analysis: whether the use case makes sense technically and economically.
Example stack
We choose the architecture to match the requirements for confidentiality and quality of results. Typical elements:
- Models
- Language models available via API
- Self-hosted models
- Documents
- OCR
- PDF parsing
- Search
- Vector databases
- Semantic search (RAG)
- Integration
- API
- Job queues
- Python
- TypeScript
Common questions, short answers
Is our data secure?
We agree how data is processed before work begins and write it into the contract. Depending on the requirements, we use services with contractual guarantees regarding data, or models run on infrastructure that you control.
Does AI make mistakes?
Yes. That is why we design the process with control over results: confidence thresholds, human verification in uncertain cases and quality measurement on test data.
Where should we start?
With one well-defined use case. A feasibility workshop makes it possible to assess the expected effect before the full solution is built.
Do you develop your own AI-based products?
Yes. We are working on DEVNIVO CIRQ, a platform for extracting material data and life-cycle data from supplier documentation. The product is in development.
Let us check whether AI makes sense here
Describe the work you want to automate. We will assess whether AI is the right tool for it.