Your Own Content and Documents as Training Material for Chatbots

In today’s business world, companies possess vast amounts of valuable information—from product documentation and customer service logs to internal policies. Yet these knowledge treasures often remain trapped in silos. INNOCHAT solves this problem by transforming your existing company content into an intelligent, searchable knowledge base that can be used by AI agents. The result: your employees, suppliers, business partners, and customers receive precise, up-to-date answers based on your own trusted company data—without having to recreate your entire documentation.

Intelligent Knowledge Utilization – How INNOCHAT Turns Company Content into AI Reference Material

Every company knows the challenge: important information is scattered across different systems—on the website, in PDF manuals, in internal wikis, or in training videos. Employees spend valuable time searching for the right answers while customers wait in frustration for support.

INNOCHAT turns these fragmented information sources into a cohesive, intelligent system. What’s special is the simplicity: you upload your existing materials or connect INNOCHAT to your data sources—the rest happens automatically. The software analyzes your content, understands its meaning and relationships, and makes it usable for AI agents¹.

Flexible Document Management System for Tailored Solutions

Not every employee needs access to all company information. A customer service representative needs different information than someone in HR. That’s why INNOCHAT offers three proven content models:

1. The role-based approach works like an intelligent permission system. Each AI agent receives access only to the information relevant to its specific task. For example, the support assistant accesses FAQ documents and troubleshooting guides, while the sales assistant works with product catalogs and price lists. The HR assistant, in turn, uses only personnel policies and employee handbooks. This separation not only increases the security of sensitive data but also improves answer quality, since the AI is not distracted by irrelevant information².


2. The holistic access model is ideal for smaller companies or when comprehensive knowledge sharing is desired. Here, all AI agents have access to the entire knowledge base. This makes it possible to recognize cross-cutting contexts and answer complex queries that touch multiple areas of the company.


3. The hybrid model combines the best of both worlds. A shared base knowledge repository is available to all assistants—such as company values, general product information, or opening hours. In addition, each assistant receives specific extensions for its area of responsibility. For particularly complex queries, an escalation mechanism can be activated to grant temporarily expanded access.

Intelligent Content Processing in Detail

The true strength of INNOCHAT lies in the intelligent processing of your content. Imagine you have a 200-page product manual. The system automatically divides it into meaningful sections—the “chunks”³. Each of these sections is then tagged with keywords, similar to how a librarian categorizes books. This intelligent tagging enables the system to find the relevant information in a flash when a customer asks a question.

A practical example: a customer asks about the warranty period of a specific product. The AI agent doesn’t search the entire manual but instead pinpoints the section on warranty terms. This happens through a combination of keyword search and semantic understanding—the AI understands, for instance, that “warranty” and “guarantee” are related concepts⁴.

Living Knowledge Management

Company knowledge is not static—it evolves constantly. New products are added, policies change, prices are adjusted. INNOCHAT accounts for this dynamic nature with a well-designed content lifecycle management.

You can add new sources at any time, update existing ones, or remove outdated information. The system can even automatically monitor your website and update the knowledge base when changes occur. For companies with existing databases or CRM systems, INNOCHAT offers interfaces to connect these external sources directly⁵. This keeps your AI knowledge base always up to date without manual effort.

RAG Technology, Simply Explained

Behind the scenes works a technology called RAG (Retrieval-Augmented Generation)⁶. Think of RAG as a particularly clever assistant who first finds the relevant books in your company library (retrieval), reads them, and then formulates a tailored answer (generation).

The decisive advantage: the AI does not invent answers but bases its statements on your actual company data. Every answer can be traced back to its source, which builds trust and facilitates quality assurance.

INNOCHAT optimizes this process for each individual assistant. A technical support assistant, for example, needs detailed, precise answers and therefore uses a powerful language model. A simple FAQ assistant, on the other hand, works with a faster, more resource-efficient model—saving costs and improving response speed⁷.

Practical Use Cases

A mid-sized manufacturing company uses INNOCHAT with three specialized assistants: the customer service assistant answers questions about products and delivery times, the technician assistant helps with maintenance work with access to technical documentation, and the HR assistant supports employees with questions about vacation policies or expense reimbursement. All three use the same technical infrastructure but work with different knowledge domains.

A retail company, by contrast, relies on the holistic approach: a single, comprehensively informed assistant answers both customer questions about products and internal employee questions about processes. This flexibility makes INNOCHAT a solution that adapts to your specific needs—not the other way around.

Conclusion

INNOCHAT transforms your existing company content into a living, intelligent knowledge base that supplies your AI agents with precise, current, and trustworthy information. The flexible architecture adapts to your security requirements and organizational structures, while intelligent content processing ensures that every query is answered quickly and correctly.

Getting started couldn’t be easier: begin with a pilot project in a manageable area—such as customer service or internal IT support. Upload your most important documents, configure your first AI agent, and experience for yourself how static information becomes dynamic, usable knowledge.

Want to see how INNOCHAT brings your company content to life? Contact us for a non-binding demo and discover how to make optimal use of your corporate knowledge.

References

1. Zhao, W. X., et al. (2023). “A Survey of Large Language Models.”
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2. Moseley, D., Nishio, M., Perez Rodriguez, J., Saarikivi, O., Toub, S., Veanes, M., Wan, T., Xu, E. (2023). “Derivative Based Nonbacktracking Real-World Regex Matching with Backtracking Semantics.”
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3. Liu, N. F., et al. (2023). “Lost in the Middle: How Language Models Use Long Contexts.”
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4. Karpukhin, V., et al. (2020). “Dense Passage Retrieval for Open-Domain Question Answering.” Proceedings of EMNLP 2020.
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5. Borgeaud, S., et al. (2022). “Improving language models by retrieving from trillions of tokens.” ICML.
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6. Lewis, P., et al. (2020). “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” NeurIPS 2020.
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7. OpenAI. (2023). “GPT-4 Technical Report.”
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