In short: Deyan7 is a specialist provider of custom AI development for mid-sized companies. We build AI solutions that work inside your existing systems: AI agents for recurring processes, expert systems with source citations, and automated document and quoting workflows. You see a first working version after a few weeks, and the system is usually in production after three to four months. You own the code and the solution, with no licence fees.
What we build
We don't write AI strategy papers. We build software that runs in day-to-day operations and measurably takes work off your team's plate. Typical projects:
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AI agents for recurring processes: handling enquiries, preparing quotes, asking customers follow-up questions, reconciling data between systems. The agent does the routine work, people review and decide. Our article on quote automation shows what this looks like in sales.
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Expert systems for specialist knowledge: knowledge assistants that answer questions based on curated expert sources, with a citation for every statement. Expert knowledge becomes available to customers or employees around the clock. More in our article on expert chatbots for specialist domains.
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Document analysis: systems that read, filter and structure tenders, bills of quantities or contracts. Details on our AI document analysis page.
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Customer support automation: AI agents in your ticketing system that answer enquiries or draft replies for the support team.
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Integration into existing systems: the AI works with data from ERP, CRM and document management, without migration. More under AI integration for ERP, CRM and DMS.
What sets us apart from a typical AI agency
One person responsible from problem definition to operation. There are no hand-offs between consultants, developers and prompt teams. An AI Fullstack Engineer talks directly to you and your domain experts and delivers the solution end to end: frontend, backend, AI, infrastructure, integration and testing.
Expertise stays with people. On your side, an Agent Operator takes the lead on the subject matter. This person reviews results, sharpens the instructions given to the AI and decides whether an output is reliable. No technical skills are required, deep knowledge of the process is.
Productive, not experimental. We don't run month-long feasibility studies. Whether a project is feasible and how much effort it takes can usually be assessed in 60 to 90 minutes. After that we build a first productive version straight away, test it with real data and improve it in weekly cycles.
Model-independent. Our solutions work with different language models, for example from OpenAI, Anthropic or Google. When a better model becomes available, you swap it in without rebuilding the system.
Honest when AI is not the answer. If off-the-shelf software solves your problem, or a better process achieves more than AI, we say so. How to recognise a reliable partner is covered in our guide to choosing an AI service provider.
How a project works
- First conversation and assessment: together we look at your process and your systems. Afterwards you know whether AI makes sense, what effort is realistic and which prerequisites are missing.
- First version with real data: based on sample data, a working system is ready after a few weeks for your experts to try and evaluate.
- Iteration in weekly cycles: edge cases are handled, systems are connected, and quality is measured against fixed test cases instead of estimated.
- Production: the system first runs in parallel to the existing process. Only once the quality convinces does it take over step by step. Three to four months to production is typical.
- Operation and further development: maintenance, security updates and model changes. Your IT team can take over operations if you prefer.
What you need on your side
- A decision-maker with a mandate who owns the project and opens doors internally.
- A domain expert with time: around four hours per week at the start, one to two hours after the initial phase.
- Access to sample data and systems, without months of approval processes.
- Openness to GDPR-compliant cloud infrastructure. What that looks like in practice is explained in our article on AI and data protection.
You don't need a perfect data set. Modern language models process heterogeneous sources directly. What matters is that the information in your sources is correct and up to date.
What it costs
Across our projects with mid-sized companies, three price ranges for initial development have emerged: EUR 15,000 to 25,000 for clearly defined use cases, EUR 30,000 to 40,000 for solutions integrated into existing systems, and EUR 40,000 to 60,000 for complex projects with distributed expertise and grown infrastructure. On top of that come manageable running costs for infrastructure, language models and maintenance.
The full breakdown, including running costs and payback, is in our article What Does an AI Project Actually Cost for a Mid-Market Company?
Who this is for
- Mid-sized companies with knowledge-intensive processes: quoting, document review, customer enquiries, internal knowledge search.
- Specialist publishers, consultancies and engineering firms that want to turn their expertise into a digital product or an internal assistant.
- Software and SaaS companies that want to bring AI features into their own product or their support.
- Companies whose first AI project didn't work out: usually it wasn't the technology but the starting point. How to find the right one is covered in our article on the first AI use case.