AI assistant with Your own company data
CodeGuides develops AI chatbots that access your own documents, knowledge bases and data sources, with configurable access rights, attribution and integration into your system environment.
- RAG architecture: answers from our own documents and sources
- Configurable access rights and data sharing
- Integration into existing web apps, intranets and tools
Bring specific requirements and we will check the data situation and integration options.
- RAG architecture with own data
- Source per answer
- Access rights configurable
- Integration into your systems
This is what a productive AI chatbot with its own company data looks like
Selected projects and customers
Why simple chatbots not enough
An AI chatbot without a connection to your own data will not 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 hits, not answers, employees have to read and interpret themselves.
Without citing the source, it is difficult to check whether the answer is correct, which slows down productive use.
Without a configured access concept, internal data can unintentionally become accessible to all users.
An isolated chatbot is of little help, it has to be available where the work takes place.
AI without verified data base invents answers, RAG only answers what is in approved sources.
An AI chatbot that knows your own content and answers comprehensibly.
CodeGuides develops AI assistants with RAG architecture: answers based on your shared documents, with source information and configurable access rights.
This is how RAG works, Retrieval-Augmented Generation
RAG is the architecture that enables AI chatbots with their own company data. This is how a request works:
User question
User asks free question in the chatbot
Vector search
Semantic search in indexed sources
Recall passages
Relevant text sections from approved sources
LLM formulated
AI generates answer from retrieved passages
Answer + sources
Answer with sources, transparent and verifiable
Data sources
Which data sources can the AI chatbot use?
The AI assistant connects to the data sources you share, structured, indexed and 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
Concrete use cases that are technically feasible and economically sensible.
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, access rights per team and source citation.
- Faster information search
- Fewer internal queries
Product data assistant for sales
Sales teams manually search technical product information, specifications and compatibility in PDFs and internal systems.
AI assistant with access to product catalogs, data sheets and technical documentation, with precise answers and source citation.
- Faster quotation processing
- Fewer queries to the technical department
Customer Support Assistant
Incoming customer inquiries are processed manually. Standard questions about products, delivery times and service tie up capacity.
AI assistant on the website or in the customer portal answers standard questions based on released product information and FAQ.
- 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.
Internal chatbot with access to current guidelines, manuals and compliance documents, with versioned source references.
- 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.
Internal assistant provides contextual answer suggestions for agents based on released product data and historical tickets.
- Faster ticket processing
- More consistent response quality
Onboarding assistant for new employees
New employees have many questions about processes, tools and internal procedures, thereby tying up capacity in 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 yours 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
What documents and systems are in place? Evaluate quality, format 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
Systematic testing with real questions, optimize 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
Document import, indexing, embedding and vector database, we build the entire infrastructure behind the chatbot.
Access rights & role concept
Fine-grained access concepts: Which group of users sees which document sources, appropriate to 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 productive operations.
An AI chatbot is not a plug-and-play product. The crucial difference lies in the clean data pipeline, well-thought-out access concept and integration into the actual working environment, which is exactly our strength as a software development partner.
Who is an individual one for AI chatbot useful?
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
The AI chatbot is a building block, here are other AI offers from CodeGuides.
AI agency overview
All CodeGuides' AI services at a glance, from consulting to productive 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
Individual software and web app development as a basis for AI integration.
Learn moreReady to develop an AI chatbot with your own data?
We check your data situation, clarify access rights and integration options, and give 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 first searches shared documents and data sources and uses the information it finds as context for its response. This means it answers questions based on your own company content, not just based on general AI training.
How much does it cost to develop an AI chatbot?
A first MVP with RAG architecture and your own documents typically costs between 15,000 and 45,000 euros, depending on data volume, access rights complexity and system connection. In the initial consultation you will receive a concrete assessment.
Which documents can the 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 develop with a data protection-oriented architecture: data storage in Germany or the EU, clear access rights, no sending of internal data to external services without a legal basis. We recommend additional legal examination.
Can the chatbot be integrated into our existing software?
Yes. We integrate the AI assistant into web apps, intranets, CRM systems or provide it as an API. Integration is a central part of development.
What happens if the chatbot doesn't find an answer?
The chatbot clearly states that it cannot find a suitable source for this question in the shared documents instead of inventing an answer. Transparency is a core feature of the RAG approach.
How are access rights controlled?
We develop access concepts that define which user areas or teams have access to which document sources. No user sees more than what is approved.
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 also use existing AI services (OpenAI, Azure OpenAI) and we will build the infrastructure around them. Vector database, document pipeline and backend are developed by us. Please note the data protection regulations.
CodeGuides is an app and AI agency from Germany for Flutter apps, custom software, AI automation and local AI infrastructure. Consulting and implementation come from a single source: We evaluate use cases technically, build the first pilot and scale it up to company-wide operation, GDPR-compliant and with hosting in Germany.
Request AI chatbot
Describe your use case. In the initial consultation we check the data situation and feasibility.
Arrange a meeting
45 minutes, technical assessment of your chatbot project.
Was passiert danach?
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 are the users (employees / customers)?
- Where should the chatbot be integrated?