In short: Deyan7 connects AI to the systems your company already works with: ERP, CRM, document management, ticketing and email. We build an orchestration layer through which AI agents read data across systems and, with your approval, create drafts directly in your systems. No migration, no system replacement, no waiting for the next ERP upgrade. You see first results with your sample data after a few weeks, and the solution is usually in production after three to four months.
What we implement
Without access to your systems, AI remains a better text generator. There is always a person between the AI and the business process, copying, pasting and cross-checking. That is exactly the work we take out. Typical use cases:
- Creating quotes from enquiries: the AI reads the enquiry, matches line items to your products in the ERP and creates the quote as a draft. Our article on quote automation explains how this works in detail.
- Handling customer enquiries: enquiries in the CRM or ticketing system are pre-qualified, answered or prepared as a draft reply, and the status is kept up to date.
- Making document management knowledge usable: employees ask questions in natural language and get answers with a reference to the source document.
- Research across system boundaries: email threads, tickets, documents and master data are pulled together where this takes hours of manual searching today.
- Reconciling data between systems: wherever information is currently transferred from one system to another by hand.
How the connection works
We don't make every system AI-capable on its own. We build an orchestration layer that sits between the language model and your systems. The model decides which information it needs. The orchestration knows which system holds that information and how to retrieve it. The result is one agent that uses ERP, CRM, document management and ticketing together, instead of five AI islands for five systems.
Not every system is built to be accessed from outside. In that case there are three routes:
- Read-only database access: enough for most analyses and requires no change to the system.
- An interface provided by your IT: the cleanest route, because your IT controls what is accessible.
- A visual agent as a bridge: the agent operates the system through its interface until a better connection is available.
The technical background is covered in our article How to Integrate AI into Existing ERP, CRM, and DMS Systems
Start with reading, extend to writing
Integration doesn't mean the AI writes to your systems from day one. We work in three stages:
- Read: the AI gathers information from your systems. That alone often saves many hours of research.
- Create drafts: the AI prepares quotes, replies or records. A person reviews and confirms before anything goes out.
- Controlled autonomy: for recurring, low-risk actions, approval can be automated once quality is measurably high. Irreversible actions stay with people.
Autonomy grows with trust, not with promises.
How an integration project works
- System map in the first conversation: which systems are in use, where does information flow by hand, which process offers the biggest lever?
- First version with sample data: we start with exports and sample documents, without touching your systems. You see early on what the AI can do with your real data.
- Connecting system by system: then we connect the real systems step by step. Knowledge tools like Confluence or Jira are often connected within hours; older ERP systems with sparse documentation take longer.
- Parallel operation and go-live: the AI works alongside your existing workflows, and the current process remains as a fallback. Three to four months to production is typical.
- Operation and further development: maintenance, adjustments to system changes and model changes.
Data protection and control
AI agents only get access to the data and actions they need for their task. Where possible, personal data is pseudonymised before it reaches a language model. Models can run in German data centres, and training on your data is contractually excluded. Details in our article on GDPR-compliant AI.
What it costs
Integration into existing systems usually falls into the mid-range of EUR 30,000 to 40,000 for initial development. For grown legacy environments with many special cases it is EUR 40,000 to 60,000. The biggest factor is not the language model but your infrastructure: a dedicated cloud environment we can work in is much faster and cheaper than going through VPN tunnels and approval loops. More in our article on AI project costs.
Who this is for
- Mid-sized companies with a grown system landscape in which information between ERP, CRM and document storage is moved by hand today.
- Companies in the middle of an ERP rollout that don't want to wait one to two years for AI.
- Sales, service and back-office teams whose experts spend too much time looking things up, copying and cross-checking.
If your goal is a custom AI solution beyond integration, see our overview of custom AI development.