Make knowledge and service available digitally—with AI assistants and chatbots
We build assistants that know your products, your processes and your language. For customer support, internal knowledge bases and automated email processing. With your data, in your environment and GDPR-compliant.
Three typical applications
1. Customer support bot with real context
Instead of "Please contact a member of staff," the assistant provides specific answers from your manual, knowledge base and ticket history—with source citations. It automatically escalates to a human when confidence is low or a topic falls outside the scope.
2. Internal knowledge assistant
Your employees ask the assistant instead of searching through ten SharePoint folders. "What is the travel expense policy for overnight stays in Munich?"—the answer includes a link to the original document. Onboarding time for new employees drops dramatically.
3. Automated email processing
The assistant classifies incoming emails, creates response drafts and enters structured data in your CRM or ERP. Employees review and send—instead of typing everything themselves.
What sets us apart from ChatGPT & similar tools
| Aspect | Generic ChatGPT | L3D1 Custom Assistant |
|---|---|---|
| Knows your data | No | Yes—via RAG |
| Answers with source citations | Rarely | Required |
| Data sovereignty | OpenAI / USA | Your cloud / on-premise |
| Integration into your tools | Limited | Full integration |
| Customizable guardrails | No | Yes |
| Auditability | No | Complete |
Technology stack
- LLMs: GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3, Mistral, Qwen—selected according to the task and hosting preference
- Vector databases: Qdrant, Weaviate, pgvector
- Frameworks: LangChain, LlamaIndex, proprietary orchestration
- Frontend: Web widget, Microsoft Teams, Slack, mobile app, voice interface
Frequently asked questions
What is the difference from ChatGPT?
Our assistants know your data, follow your policies and integrate into your systems. ChatGPT is a generic tool.
How is the assistant trained with our data?
Through RAG (Retrieval Augmented Generation): Your documents are indexed, and the assistant retrieves them at runtime to answer with the right context. Your data stays with you.
How long does implementation take?
The first production version is typically ready after 4-8 weeks.
Which models does it run on?
GPT-4o, Claude 3.5, Gemini, Llama 3, Mistral or Qwen—depending on the use case. On-premise deployment is also possible.
How do we prevent hallucinations?
Source citations for every answer, confidence thresholds, guardrails against out-of-scope topics, and regular evaluation against a test dataset.
Which questions should your assistant answer?
Tell us about your use case in 30 minutes. We will give you an honest assessment of what is technically possible today—and what is not.