AI assistant with Your own company data
CodeGuides develops AI chatbots that access your documents, knowledge bases and data sources, with configurable access rights, source citations and integration into your existing systems.
- RAG architecture: answers from our own documents and sources
- Configurable access rights and data sharing
- Integration into existing web apps, intranets and tools
Bring us your specific requirements, and we'll assess your data and integration options.
- RAG architecture with own data
- Source per answer
- Access rights configurable
- Integration into your systems
What a production-ready AI chatbot using your company data looks like
Selected projects and customers
Why simple chatbots not enough
An AI chatbot that isn't connected to your data can't answer company-specific questions.
Standard FAQ bot
Predefined answers to known questions
AI chatbot with RAG
Answers from our own documents and data sources
ChatGPT and Co. do not know any internal manuals, product data or company-specific rules.
Full-text search returns results, not answers. Employees still have to read and interpret them.
Without a source citation, it's difficult to verify an answer, which can slow adoption.
Without a defined access control policy, internal data could unintentionally be made available to all users.
A standalone chatbot isn't much help; it needs to be available where the work happens.
AI without verified data can make up answers. RAG grounds responses in approved sources.
An AI chatbot that understands your company’s content and provides answers you can verify.
CodeGuides develops AI assistants using RAG architecture to answer questions based on your approved documents, with source citations and configurable access rights.
This is how RAG works, Retrieval-Augmented Generation
RAG is the architecture behind AI chatbots that use your company data. Here's how a request works:
User question
User asks an open-ended question in the chatbot
Vector search
Semantic search in indexed sources
Retrieve passages
Relevant text sections from approved sources
LLM formulates the answer
AI generates an answer from the retrieved passages
Answer + sources
Answer cites its sources for transparency and verification
Data sources
Which data sources can the AI chatbot use?
The AI assistant connects to the data sources you approve, organizing and indexing them with configurable access rights.
Documents
Knowledge base
Systems & APIs
AI chatbot
RAG architecture
Access rights configurable
Web & Intranet
Software products
Communication
What AI chatbots are for can be used productively
Use cases that are technically feasible and make business sense.
Internal knowledge assistant
Employees spend hours searching through manuals, guidelines and old project documents.
RAG-based AI assistant in the intranet, with configured document sources, team-specific access rights and source citations.
- Faster information search
- Fewer internal queries
Product data assistant for sales
Sales teams manually search PDFs and internal systems for technical product information, specifications and compatibility details.
AI assistant with access to product catalogs, data sheets and technical documentation, providing precise answers with source citations.
- Faster quotation processing
- Fewer questions for the technical team
Customer Support Assistant
Customer inquiries are handled manually. Routine questions about products, delivery times and service take up valuable capacity.
An AI assistant on the website or in the customer portal answers common questions based on approved product information and FAQs.
- Less manual standard requests
- 24/7 availability for common questions
Compliance and Policy Wizard
Employees regularly ask about internal guidelines, compliance requirements and legal requirements.
An internal chatbot with access to current policies, manuals, and compliance documents, with versioned citations to its sources.
- Faster answers to policy questions
- Always current information base
Support Team Assistant
Support agents manually search for answers in documentation and rephrase standard answers each time.
An internal assistant provides agents with contextual answer suggestions based on approved product information and historical tickets.
- Faster ticket processing
- More consistent response quality
Onboarding assistant for new employees
New employees often have questions about processes, tools, and internal procedures, taking up time for experienced teams.
AI assistant with access to onboarding documentation, FAQs and process guides, available 24/7.
- Less onboarding queries
- Faster onboarding
Do you have a specific use case? We check the data situation and technical feasibility.
This is how we develop your chatbot AI chatbot
From the data situation to the productive chatbot, structured and without a long lead time.
Clarify use case & goal
Which questions should the chatbot answer? Who are the users? What is the concrete added value?
Check data sources
Which documents and systems do you have? We assess their quality, formats, and accessibility.
RAG concept & architecture
Specify embedding model, vector database, access concept, integration and data protection architecture.
Develop prototype
MVP with limited data range. You test with real questions and real users.
Test response quality
We test systematically with real questions, optimizing source coverage and answer accuracy.
Integrate & expand productively
Integration into your system environment. Monitoring, feedback loop and expansion of additional data sources.
Why CodeGuides for your AI chatbot
An AI chatbot is more than a language model. It needs clean backend infrastructure, data pipelines and access concepts.
Backend & data pipeline
We build the entire infrastructure behind the chatbot, including document import, indexing, embeddings, and a vector database.
Access rights & role concept
Fine-grained access controls determine which user groups can access which document sources, in line with your organizational structure.
System integration
The chatbot must be accessible where your employees work: intranet, web app, CRM or as an API.
Sources & transparency
Each answer shows which source it comes from. This creates trust and makes mistakes recognizable.
Data protection by design
Data storage possible in Germany or the EU. Data protection is taken into account architecturally from the start.
Monitoring & Quality Assurance
Logging, feedback mechanisms, and response-quality monitoring for production operations.
An AI chatbot is not a plug-and-play product. The key lies in a clean data pipeline, well-designed access controls, and integration into your actual work environment—exactly where we excel as a software development partner.
Who is a custom AI chatbot for? Is an AI chatbot right for your business?
An individual AI chatbot with its own data makes sense when generic AI tools cannot access your content.
Well suited
- Companies with many internal documents, manuals or product data
- Teams with frequently recurring information requests
- Support teams working with extensive documentation
- SaaS providers who want to integrate an assistant into their product
- Companies with clear requirements for access rights and data protection
Less suitable
- Companies without digitized document stocks
- Very small amounts of documents that could also be mapped as a simple search
- Without internal contact person for document maintenance and data maintenance
Unsure? In the initial consultation, we check whether and how a chatbot makes sense for your use case.
More AI services from CodeGuides
An AI chatbot is one part of the picture. Explore CodeGuides' other AI services.
AI agency overview
An overview of all CodeGuides AI services, from consulting to production implementation.
Learn moreChatGPT & OpenAI integration
Integrate ChatGPT and OpenAI API into existing software, including open source models.
Learn moreAI automation
Automate manual processes with AI: documents, emails, tickets and workflows.
Learn moreAI consulting for companies
Use case analysis and technical feasibility check before development.
Learn moreAI process automation
Concrete use cases and experiences for medium-sized companies.
Learn moreSoftware development
Custom software and web app development as a foundation for AI integration.
Learn moreReady to develop an AI chatbot with your own data?
We assess your data, clarify access rights and integration options, and provide an honest assessment of feasibility.
Frequently asked questions about the AI chatbot
What is a RAG chatbot?
RAG stands for Retrieval-Augmented Generation. With RAG, the chatbot searches approved documents and data sources first, then uses the information it finds as context for its response. This lets it answer questions based on your company's own content, rather than relying solely on general AI training.
How much does AI chatbot development cost?
An initial MVP with RAG architecture and your own documents typically costs between 15,000 and 45,000 euros, depending on the data volume, access-control complexity, and system integrations. You'll receive a specific assessment during the initial consultation.
Which documents can an AI chatbot use?
PDFs, Word documents, Markdown, HTML pages, structured databases and systems connected via interfaces. We develop the document pipeline to suit your database.
Can an AI chatbot be implemented in a GDPR-compliant manner?
Yes. We use a privacy-focused architecture, with data stored in Germany or the EU, clear access controls, and no transfer of internal data to external services without a legal basis. We also recommend an additional legal review.
Can the chatbot be integrated into our existing software?
Yes. We integrate the AI assistant into web apps, intranets, and CRM systems, or provide it through an API. Integration is a central part of development.
What happens if the chatbot doesn't find an answer?
If the chatbot can't find a suitable source in the approved documents, it says so clearly instead of making up an answer. Transparency is a core feature of the RAG approach.
How are access rights controlled?
We design access controls that specify which user groups or teams can access which document sources. Users only see information they're authorized to access.
Can we start and then expand?
Yes, we recommend an MVP with a limited data range. After proven operation, further sources, integrations and user areas can be added.
Do we need our own AI infrastructure?
No. If you don't want to build your own infrastructure, you can use existing AI services such as OpenAI or Azure OpenAI. We'll build the supporting infrastructure, including the vector database, document pipeline, and backend. Be sure to follow applicable data protection requirements.
CodeGuides develops apps for consumers and businesses, with a primary focus on B2C apps. Our in-house team in Germany provides product planning, Flutter development and long-term operations, alongside custom software, AI automation and local AI infrastructure.
Request AI chatbot
Describe your use case. In the initial consultation we check the data situation and feasibility.
Schedule a meeting
45 minutes, technical assessment of your chatbot project.
What happens next?
Chatbot project check
This is what you should bring with you to the interview:
- Which questions should the chatbot answer?
- What data sources and documents are there?
- Who will use the chatbot (employees or customers)?
- Where should the chatbot be integrated?
Project questions and practical answers
How should AI support automation handle customer requests?
Connect the assistant only to approved knowledge and permitted customer data. It should cite sources where appropriate, acknowledge uncertainty and hand cases to staff when necessary. Restrict actions such as refunds or account changes behind authorization and confirmation. Test against real support categories, monitor failed handovers and assign responsibility for updating the knowledge base.