We build AI agents, RAG systems, and Computer Vision solutions that work autonomously: not just respond. Available 24/7, GDPR-compliant, integrable with 2,000+ business tools, with measurable ROI from day one.
Order processing, customer emails, reporting, CRM maintenance, your best people spend hours on tasks that require no strategic thinking. AI agents handle this work reliably, around the clock.
You want to grow, but every new task requires a new hire? AI agents scale with you, no salary, vacation, or sick days. More output with the same team.
Which AI model can I use? Where is data processed? We rely on European models like Mistral, on-premise options, and clear data protection documentation, so you can start with confidence.
An AI agent is far more than a chatbot: it understands context, makes independent decisions, and works across multiple systems, like a digital colleague available 24/7. Unlike simple if-then automations (like Zapier), an AI agent can handle exceptions, understand natural language, and process complex task chains autonomously.
Custom AI agents on a code-first basis, no template systems. Integrable with Slack, HubSpot, Jira, Shopware, SAP, Google Workspace, Microsoft 365, and 2,000+ more tools.
Retrieval-Augmented Generation (RAG) combines the language capabilities of modern LLMs with your own data. The result: an AI assistant that answers questions about your products, processes, and documents, accurately and without hallucinations.
Use cases: customer support, internal knowledge management, onboarding new employees, product consulting, document analysis.
We handle everything: data preparation, embedding strategy, prompt engineering, LLM selection, integration into your existing systems, and ongoing optimization.
When images and videos need to be analyzed: quality control in production, automated image recognition, OCR for document processing, or visual inspection of equipment and infrastructure.
We train and deploy ML models built on your specific data, not generic benchmarks. On-premise or in the cloud, depending on your privacy requirements.
We take data protection seriously, not as a footnote, but as the foundation of every architectural decision. Depending on the use case, we deploy different AI models:
Supported models: OpenAI, Anthropic Claude, Google Gemini, Mistral, Grok (xAI), we advise on which model is right for your use case.
We analyze your processes together and identify where AI has the greatest leverage. Result: prioritized AI roadmap as PDF document with top 3 use cases, ROI assessment and concrete implementation plan.
Custom solution blueprint defining data sources (CRM, ERP, email, documents), functions, and interfaces. Focus: efficiency, data protection, future-proofing.
Development by experienced AI engineers and seamless integration into your existing IT landscape. On-premises or cloud, depending on your requirements.
Intensive testing before go-live. Training your staff on the system, the key to adoption and successful usage.
After launch, we continue to support you. The agent adapts to new requirements, data volumes, and process changes. Continuous monitoring and improvement.
Concrete applications where our solutions create value.
AI agent answers inquiries via email and chat instantly, internally also vacation requests and IT helpdesk in Teams/Slack. Escalation to staff only when needed.
Automatically researches leads, fills CRM fields, prioritizes by revenue potential, and generates personalized follow-up emails. More deals with less effort.
Agent pulls data from CRM/ERP, creates proposals automatically, and sends invoices, no manual steps required. Near-zero error rate.
RAG-based assistant answers questions about products, processes, and documents, ideal for onboarding new employees and internal support.
Automated image recognition and visual inspection of equipment, products, or documents. OCR for document processing in production.
Extracts data from PDFs, updates databases, and produces automated reports, daily, weekly, or in real-time.
| Chatbot | Zapier / Make | AI Agent (GOLLE IT) | |
|---|---|---|---|
| Understands Natural Language | Yes | No | Yes |
| Context Understanding | Limited | No | Yes, cross-system |
| Independent Decisions | No | No | Yes |
| Multi-Step Task Chains | No | Limited (if-then) | Yes, autonomous |
| Exception Handling | No | No | Yes |
| Learning Capability | No | No | Yes (feedback loop) |
| Integrations | 1 Channel | 5,000+ (simple) | 2,000+ (deep integration) |
| GDPR / On-Premise | Provider-dependent | Limited | Fully configurable |
| Setup Effort | Low | Medium | Medium–High |
| ROI Potential | Low | Medium | High |
Real questions from customer conversations — structured from a buyer's perspective, not marketing fluff.
With an AI Potential Workshop (2–3 hours). We analyse your processes together and identify the use cases with the highest leverage — not every task needs AI, sometimes a simple automation is enough.
Workshop outcome: a prioritised roadmap with 3–5 concrete use cases, effort estimates per case, and a proof-of-concept proposal for the strongest lever. We deliver the PoC in 4–6 weeks on real data, not on slides.
Important: we don't sell retainer contracts. Fixed-price scope per workshop and PoC, full transparency.
The five use cases with reliable ROI in SMBs are:
Hype that rarely works in SMBs: fully autonomous multi-agent systems, custom LLM training from scratch, AI "content factories" for marketing without editorial oversight.
ChatGPT and Copilot licences are a good starting point, but they only scratch the surface. The real value emerges when AI has access to your own data — customer database, quotes, contracts, product information.
Typical picture in SMBs: licences are there, but hardly anyone uses them productively ("Shadow AI") because governance, use cases, and integration are missing. We help you extract real value from your existing licences (Custom GPTs, Copilot Agents, M365 integration) and decide together where a dedicated solution with RAG, automation, or integration delivers more.
A 2026 MIT study shows: 95 % of all GenAI pilot projects never reach production. The reasons are almost always structural, not technical: no clear business case, no data governance, no ownership in the business unit, scope that's too big.
Our countermeasure: fixed-price PoC with a clear success criterion (e.g. "80 % correctness on 500 real emails"), max 4–8 weeks, business unit ownership from day one, production-like data instead of toy examples. No "let's see what happens", but a measurable go/no-go decision at the end.
Our fixed prices:
Market context: specialised AI agencies charge €1,500–2,500 per consultant day, big consultancies like McKinsey or BCG €2,500–5,000. We deliberately work with fixed-price scopes instead of daily-rate retainers — no vendor lock-in, no surprises.
Ongoing costs (LLM API, hosting) we often reduce by 60–80 % through caching, model routing, and prompt optimisation.
Much faster than most people expect. Our typical timelines:
Simple agents (e.g. invoice extraction, email triage) can go live within 2–3 weeks. More complex solutions with deep system integration (ERP, CRM, document management) take 8–12 weeks end-to-end. First measurable ROI signals typically show 3–6 months after go-live.
Important first: "Digital Jetzt" ended in late 2023, "go-digital" in late 2024. Still active:
We know the application landscape, help select the right programme, and work with BAFA-accredited consulting partners when needed.
Yes, when done right. We rely on European hosting providers, GDPR-compliant LLM vendors (Mistral on EU servers, Azure OpenAI EU region, Claude in EU), and watertight Data Processing Agreements (DPA).
For particularly sensitive data (healthcare, law firms, finance, defence) we offer true on-premise solutions: open-source models like Llama or Mistral run on your servers, not a single byte leaves your network. The Cloud vs. On-Prem decision is made jointly based on data classification, compliance requirements, and budget — in most cases Cloud-EU is sufficient, on-prem is only required in 10–15 % of projects.
The EU AI Act becomes fully applicable on 2 August 2026. For most SMBs, three obligations are most relevant:
Fines: up to €35M or 7 % of global turnover — for SMBs the lower amount applies. Biggest pitfall: hidden AI in SaaS tools that isn't recognised as AI. We run an AI Act assessment with you and produce the required documentation.
Hallucinations can't be eliminated entirely, but we reduce them to under 2 %. Our setup:
For critical processes (legal, medical, finance) we additionally work with human-in-the-loop: the AI proposes, a human confirms.
No. 75 % of German SMBs cite lack of know-how as the main barrier — that's exactly why we exist. We take care of architecture, development, data preparation, and integration. You provide someone from the business unit as a contact person and decide what the AI should do.
After go-live, we can handle monitoring and further development in an optional operations contract, or your existing IT team takes over with hands-on training. Dedicated data scientists or MLOps teams are unnecessary in almost any SMB project — pre-trained models plus good architecture are sufficient in roughly 80 % of cases.
Via standard APIs and proven integration patterns. Typical integrations we deliver regularly:
For systems without a clean API, we build robust middleware or use workflow automation platforms (n8n, Make) as glue. Before every project, we check in the workshop which integrations are really necessary — often only a fraction of what's on the wishlist.
Honest answer: in our projects, no-one has ever been let go because of AI. AI takes over tasks, not entire roles — usually the most tedious and error-prone ones. Employees gain time for more demanding work, customer contact, and strategic topics.
Still, the concern is real: 40 % of German SMBs fear workforce resistance, 35 % expect job changes. That's why change management is part of every one of our projects: early involvement of the business unit, transparent communication with the works council, training before go-live. If you don't bring people along, you don't have an AI problem — you have a leadership problem.
As of Q2 2026
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