AI language models integrate into your software
CodeGuides integrates ChatGPT, OpenAI API, Azure OpenAI and open source models such as Llama or Mistral into existing web apps, backends and company processes. Production-ready, with clean architecture and a clear security concept.
- OpenAI API, Azure OpenAI and open source models (Llama, Mistral, Phi)
- Own system prompts, context control and token management
- Self-hosted deployment for complete data sovereignty
Bring your existing software, we will check integration options and technical requirements.
- OpenAI API & Azure OpenAI
- Open source models (Llama, Mistral)
- Self-hosted for data sovereignty
- Productive operation
KI Integrate into existing software: ChatGPT, Claude, RAG and your own data
AI is integrated into existing software by connecting a language model to your application via a secure interface and providing it with the appropriate permissions. ChatGPT and OpenAI, Claude and open source models are possible. A RAG approach is used for answers based on your own data. CodeGuides integrates these models in a GDPR-compliant manner, including in ERP and CRM systems.
How can ChatGPT be integrated into existing software?
Via the OpenAI or Azure OpenAI interface, embedded in your application, with authentication, rate limiting and a middleware that controls inputs and outputs.
Can Claude be integrated into existing applications?
Yes. Claude can be connected analogously. The choice of model depends on quality, cost and data protection requirements, not on an individual provider.
How does a RAG chatbot work with its own data?
Your documents are indexed and for each request, appropriate excerpts are passed to the model as context. This means the chatbot responds based on your own data instead of general knowledge.
How is an AI assistant created with company data?
Through the combination of RAG, rights and role model and connection to internal sources. The assistant only sees what the respective role is allowed to see.
Can AI be connected to ERP or CRM systems?
Yes. We read and write in ERP or CRM systems in a controlled manner via interfaces, for example to classify emails and transfer data records. For process automation see AI automation.
Selected projects and customers
Why simple API connection is not enough
Incorporating an API key is easy. It is not a production-ready ChatGPT integration.
Without carefully developed system prompting, the AI responds generically and knows neither your products nor your processes. This leads to unusable results.
Without token management and budget limits, AI costs can grow uncontrollably. Productive integrations require monitoring and control mechanisms.
ChatGPT does not know any data from your CRM, ERP or database. Without contextual connection to your systems, answers remain generic.
Which user data is transmitted to OpenAI? What data is allowed to leave the company? Without a clear architecture, data protection risks arise.
Without guardrails, output validation and feedback mechanisms, AI responses can lead to errors in production.
What does the AI answer? How are requests formulated? Without logging and monitoring, quality assurance is not possible.
Integrate ChatGPT and OpenAI API into your software in a production-ready manner.
CodeGuides develops the backend infrastructure, context control, access concepts and data pipelines that a productive AI integration needs.
Here's how one works production-ready ChatGPT integration
A clean integration has several layers, frontend, backend and AI layer are clearly separated.
1. Frontend / user interface
The AI function appears where the user needs it: as a chat widget, input field, summary button or automatic suggestion, embedded in your existing application.
2. Backend and AI middleware
Your backend orchestrates the AI call: system prompt with business context, database queries for user and system data, token budget control, logging and error handling.
3. Model Endpoint (OpenAI / Azure / Self-hosted)
The call goes to OpenAI API, Azure OpenAI or a self-hosted OSS endpoint (Llama, Mistral, Phi via Ollama/vLLM), depending on the data protection requirement. Responses are returned in a structured manner, validated and passed to the frontend.
4. Monitoring and quality assurance
Logs, token usage, feedback mechanisms and quality monitoring run continuously. This means that integration in productive operation remains controllable.
ChatGPT integrationsthat we develop
Concrete use cases that we productively integrate into existing software.
AI text generation in web app
Users of a SaaS platform should be able to generate AI-supported texts, summaries or suggestions.
OpenAI integration with context-specific system prompting, user context from the database and configured output format.
- AI function as a product feature
- Faster time-to-market
AI assistant in internal tool
Employees of an internal tool should receive AI support for texts, classifications or summaries.
Integration with company-owned system context, access rights and Azure OpenAI for GDPR-compliant operation.
- Increasing productivity in everyday work
- AI with business context instead of generic answers
Automatic text summarization
Long documents, emails or reports must be summarized and structured manually.
OpenAI integration with configurable output format, language and length of the summary, as a button in existing workflows.
- Faster information acquisition
- Less reading effort
Structured data extraction from free text
Free text entries from forms, emails or tickets should be transferred into structured database fields.
ChatGPT extracts defined fields from free text and returns structured JSON, directly integrated into database pipeline.
- Less manual data entry
- Cleaner database
AI classification and categorization
Incoming data, requests or documents must be categorized and assigned manually.
OpenAI classifies content based on defined categories and returns structured classification results with confidence scores.
- Automatic categorization without manual work
- Scalable to any volume
Multilingual communication
Customer inquiries in international markets come in different languages and must be translated and answered manually.
OpenAI integration with automatic speech recognition, translation and language-specific answer generation.
- International support without language barriers
- Less manual translation work
Do you have a specific use case? We examine integration options and technical requirements.
Which Model deployment fits to you?
OpenAI Direct, Azure OpenAI or self-hosted open source models, the choice depends on data protection, costs and requirements.
| Criterion | OpenAI Direct | Azure OpenAI | Open Source / Self-hosted |
|---|---|---|---|
| Data sovereignty | Data storage USA, DPA available | EU data storage, enterprise SLA | ✓ Completely in our own infrastructure |
| High volume costs | Token-based, scales with usage | Token based, commitment options | ✓ Fixed infrastructure costs, no tokens |
| Fine tuning to your own data | Limited via OpenAI fine-tuning API | Possible via Azure ML | ✓ Full training possible |
| Model performance | ✓ GPT-4o, state of the art | ✓ GPT-4o, state of the art | Llama 3.3, Mistral, DeepSeek R1, equal depending on the task |
| Setup effort | ✓ Ready to go straight away | Azure subscription required | Infrastructure setup required (Ollama, vLLM, HuggingFace) |
| Regulated industries | Restricted | Well suited | ✓ Ideal (no external API call) |
We advise you on your selection, depending on your data protection requirements, volumes and system environment.
Open source language models as an alternative
Llama, Mistral, Phi and DeepSeek are production-ready alternatives to GPT-4, for projects where privacy, cost or customizability are critical.
Llama (Meta)
Llama 3.1 · 3.2 · 3.3
- Very strong all-round performance, Apache 2.0 license
- Available from 1B to 405B parameters
- Ideal for chat, summary, classification
- Deployment: Ollama, vLLM, HuggingFace, Azure ML
Mistral (Mistral AI)
Mistral 7B · Mixtral · Mistral Large
- European company, strong GDPR positioning
- Mistral 7B: very efficient for CPU/small GPU
- Mixtral (MoE): high quality with low resource consumption
- Multilingual, especially good for German
Phi-4 (Microsoft)
Small Language Model · MIT license
- 14B parameters, still state-of-the-art quality
- Ideal for on-premises with limited GPU capacity
- Strong in reasoning and structured output
- Low latency for real-time applications
DeepSeek R1
Reasoning · MIT license
- Chain-of-thought reasoning at GPT-o1 level
- Distilled variants (7B to 70B) for efficient deployment
- Ideal for complex analysis and decision-making tasks
- MIT license, full commercial use permitted
Qwen 2.5 (Alibaba)
Coding · Multilinguality
- Particularly strong in code generation and analysis
- Very good multilingualism including German
- Model sizes from 0.5B to 72B
- Apache 2.0 license for most sizes
Deployment options
On-Premise · Cloud · Hybrid
- Ollama,local development and small deployments
- vLLM,production-ready inference server (GPU)
- HuggingFace Endpoints,managed cloud hosting
- Azure ML / AWS Bedrock,Enterprise cloud integration
Open source models are not an emergency solution; for many business applications, Llama 3.3 or Mistral Large are comparable in quality to GPT-4. The crucial difference: your data never leaves your infrastructure.
This is how we integrate ChatGPT into your software
Structured, documented and without a long lead time.
Requirements analysis
What function should AI take on? What data does she need? Which systems need to be connected? What data protection requirements are there?
Technical integration concept
Model selection, system prompting, context architecture, token management, data pipeline and security concept.
Development and integration
Backend integration, API connection, frontend embedding and testing in the staging system.
Productive operation and monitoring
Launch with monitoring, logging and token control. Continuous optimization of AI quality.
Why CodeGuides for your ChatGPT integration
ChatGPT integration is software development, not just prompting.
We develop the backend layer that orchestrates API calls, enriches context and processes responses, not just the API call.
ChatGPT needs context from your data. We develop the data pipelines that integrate user context, company data and system health.
We advise on the choice between OpenAI Direct, Azure OpenAI and open source models and develop the architecture to suit your data protection requirements.
Logging, token budgets and cost monitoring are planned from the start. No unexpected cost explosions in productive operations.
We integrate AI functions into your existing web app, your backend or your SaaS product, without completely new development.
We deploy and integrate Llama, Mistral, Phi and DeepSeek on our own infrastructure, with Ollama, vLLM or cloud services such as Azure ML and AWS Bedrock.
Logging, feedback loops and response quality monitoring for productive and controllable AI operations.
More AI services from CodeGuides
ChatGPT integration is a building block, here are more AI offerings.
All CodeGuides AI services at a glance.
AI assistants with their own company data, RAG architecture and source information.
Automate manual processes with AI: documents, emails, tickets.
Use case analysis and technical feasibility check before development.
Concrete use cases and experiences for medium-sized companies.
Ready to integrate an AI language model for production?
Whether OpenAI, Azure OpenAI or open source, we examine integration options, choose the right model and deliver a productive AI function in your existing software.
Frequently asked questions about ChatGPT and OpenAI integration
What is a ChatGPT integration?
A ChatGPT integration connects your existing software with the OpenAI API or Azure OpenAI. AI functions such as text generation, classification, summary or data extraction are embedded into your web app, your backend or your internal tools, production-ready and with a clean architecture.
How much does a ChatGPT integration cost?
A simple integration with standard functions typically costs 8,000 to 25,000 euros. More complex integrations with their own data pipeline, access rights and several modules are calculated individually. In the initial consultation you will receive a concrete assessment.
What is the difference between OpenAI and Azure OpenAI?
Both use the same language models. Azure OpenAI runs on Microsoft infrastructure in the EU with clear data protection and compliance guarantees. OpenAI Direct is available faster but has different data retention rules. The choice depends on your privacy needs.
Can ChatGPT access our own data?
Yes, ChatGPT can access your data from databases, APIs or documents via function calling, RAG architecture or direct context inclusion. We are developing the data pipeline and access architecture for this.
Is a ChatGPT integration GDPR compliant?
Yes, with the right architecture. Azure OpenAI offers EU data storage and enterprise data protection agreements. A local model infrastructure can also be useful for sensitive data. We advise on data protection-compliant architectural decisions.
Can you integrate ChatGPT into our existing web app?
Yes, that is our focus. We integrate ChatGPT and OpenAI API into existing web apps, backends, SaaS products and internal tools, without completely new development.
How are token costs controlled?
We develop token management, budget limits, logging and cost monitoring as part of the integration. This means that AI costs remain transparent and controllable.
Which models are supported?
Proprietary models: GPT-4o and GPT-4 Turbo via OpenAI API and Azure OpenAI. Open source models: Llama 3.3, Mistral, Phi-4, DeepSeek R1 and Qwen 2.5 for self-hosted deployments. We advise on the choice of model depending on data protection, quality requirements, latency and costs.
When are open source models worthwhile instead of GPT-4?
Open source models like Llama 3.3 or Mistral Large make sense when (1) complete data sovereignty is required and no data transfer to external APIs is allowed, (2) high request volumes make token costs unattractive, (3) fine-tuning to your own company data is planned or (4) regulatory requirements (health, finance, legal) exclude external API calls. For many business needs, they are equivalent in quality to GPT-4.
Can we start with a function?
Yes, we recommend that. An integration for a clearly defined AI function as an MVP, then gradual expansion to further functions and system connections.
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 ChatGPT integration
Describe which AI function you would like to integrate into your software. In the initial consultation we examine technical requirements and integration options.
Arrange a meeting
45 minutes, technical assessment of your ChatGPT integration.
- 45 minutes Google Meet
- Integration options and architecture
- OpenAI, Azure OpenAI or Open Source, whichever suits you
- Assess data protection, costs and deployment options
- Clear recommendation and next steps