In short: Deyan7 builds AI systems that read, filter and structure large volumes of documents for you: tenders and bills of quantities, contracts, legal and standards documents, technical documentation. Instead of a summary, your experts get a structured assessment with requirements, deadlines and references to where each point appears in the document. We build it around your criteria, connect it to your systems and run it as a one-off analysis or as continuous monitoring.
What companies use AI document analysis for
- Evaluating tenders: does the tender fit our portfolio? Which suitability criteria, deadlines and hidden conditions does it contain? The AI pre-sorts, sales decides.
- Analysing bills of quantities: line items are recognised and matched to your own products or services as the basis for a quote. Combined with your ERP, this becomes an AI integration that prepares the quote directly.
- Reviewing contracts: obligations, deadlines, responsibilities and deviations from your standards are extracted.
- Monitoring regulations: new laws, amended regulations and standards are checked continuously. You learn what has changed and what it means for your obligations.
- Making expert knowledge searchable: technical documentation and expert sources become an assistant that answers questions with citations.
Structure, not summary
Most AI tools summarise documents. That is useful, but it doesn't solve the actual problem. The value isn't in compressing 400 pages into two, but in turning running text into actionable data: which requirements must be met? Which deadlines apply? Which condition sits in the appendix instead of the overview?
That is why our solutions work in several stages, just like an experienced expert:
- Filter: incoming documents are matched against your portfolio or requirements profile. Anything unsuitable is sorted out with a short justification.
- Analyse: relevant documents are worked through in detail, including appendices, footnotes and cross-references between documents.
- Structure: the result is an assessment a decision-maker can grasp in a few minutes: recommendation, key points, open requirements, references.
Where documents are already well structured, for example standardised forms or XML, we use rule-based extraction instead of AI. The two approaches complement each other. How multi-stage analysis works in detail is described in our article How Does AI Analyse Hundreds of Pages in Minutes Instead of Hours?
Quality you can measure
AI document analysis doesn't have to be perfect to pay off. If the pre-selection is right, the expert completes the rest in a fraction of the previous time. What matters is that you know how good the system is.
At the start of the project we therefore build a reference data set: ten to twenty typical documents for which the correct result is known. Every new version is measured against this set. You see progress as a number, not as a feeling. Where accuracy falls short, the cause is rarely the model and usually the sources: poor formatting, copy-protected PDFs, inconsistent document structures.
How a project works
- First conversation: which documents, in what volume, against which criteria? Who decides today, and what does that cost?
- Criteria and reference data set: your expert defines what "relevant" means and provides ten to twenty sample documents with known results.
- First version after a few weeks: the system analyses your sample documents, and accuracy is measured and reviewed together.
- Iteration and integration: edge cases are handled, new documents are fetched automatically, and results land where your team works, for example in the inbox, the document management system or the ERP.
- Production: initially in parallel to the existing process, typically fully productive after three to four months.
One-off analysis or continuous monitoring
Some tasks are one-off: reviewing an extensive set of contracts, working through a large tender. Others run continuously: evaluating and prioritising new tenders every day, regularly detecting changes in legal and standards documents. Both can be covered on the same foundation, and many companies need both.
What it costs
A clearly scoped document classification typically falls into the EUR 15,000 to 25,000 range for initial development. With integration into existing systems, for example to match line items directly in the ERP, projects usually cost EUR 30,000 to 40,000. The full picture including running costs is in our article on AI project costs for mid-sized companies.
Who this is for
- Companies that regularly bid on tenders and spend days today screening and evaluating them.
- Sales teams in industry and trades that translate enquiries and bills of quantities into quotes.
- Legal, compliance and quality departments that need to keep track of contracts, standards and regulations.
- Specialist publishers and consultancies that want to make their expert sources usable as searchable knowledge.
Standard software is the right call when a ready-made platform covers exactly your document types and criteria. Then we recommend it. For an overview of how we work, see our page on custom AI development.