AI language models integrate into your software
CodeGuides integrates ChatGPT, the OpenAI API, Azure OpenAI, and open-source models such as Llama and Mistral into existing web apps, backends, and business processes. Our integrations are production-ready, with clean architecture and a clear security plan.
- OpenAI API, Azure OpenAI, and open-source models (Llama, Mistral, Phi)
- Own system prompts, context control and token management
- Self-hosted deployment for full data sovereignty
Bring your existing software. We’ll review the integration options and technical requirements.
- OpenAI API & Azure OpenAI
- Open source models (Llama, Mistral)
- Self-hosted for data sovereignty
- Productive operation
AI Integrate into existing software: ChatGPT, Claude, RAG and your own data
AI can be integrated into existing software by connecting a language model to your application through a secure interface and configuring the appropriate permissions. Options include ChatGPT and OpenAI, Claude, and open-source models. For answers based on your own data, we use a RAG approach. CodeGuides integrates these models in compliance with GDPR, including in ERP and CRM systems.
How can ChatGPT be integrated into existing software?
We connect the OpenAI or Azure OpenAI API to your application, with authentication, rate limiting, and middleware to control 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 relevant excerpts are provided to the model as context with each request. This lets the chatbot answer based on your own data rather than general knowledge.
How is an AI assistant created with company data?
We combine RAG with role-based access controls and connections to internal data sources. The assistant only sees information the user’s role allows them to access.
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
Adding an API key is easy. That alone does not make a production-ready ChatGPT integration.
Without carefully designed system prompts, AI responses are generic and lack knowledge of your products and processes. The results are often unusable.
Without token management and budget limits, AI costs can grow uncontrollably. Production-ready integrations require monitoring and controls.
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 cause errors in production.
What does the AI answer? How are requests phrased? Without logging and monitoring, quality assurance isn’t 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.
How a production-ready ChatGPT integration
A well-designed integration has several layers, with the frontend, backend, and AI layer clearly separated.
1. Frontend / user interface
The AI function appears where users need 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 AI calls, using system prompts with business context, database queries for user and system data, token budget controls, logging, and error handling.
3. Model Endpoint (OpenAI / Azure / Self-hosted)
Depending on your data protection requirements, calls go to the OpenAI API, Azure OpenAI, or a self-hosted open-source endpoint (Llama, Mistral, or Phi via Ollama or vLLM). Responses are returned in a structured format, validated, and passed to the frontend.
4. Monitoring and quality assurance
Logging, token usage tracking, feedback mechanisms, and quality monitoring run continuously to keep the integration under control in production.
ChatGPT integrationsthat we develop
Examples of use cases we integrate into existing software for production.
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 prompts, user context from the database, and a 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-specific context and access rights, using 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 summary format, language, and length, available 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 need to be transferred to structured database fields.
ChatGPT extracts specified fields from free text and returns structured JSON, integrated directly into the 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 from international markets arrive 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 type of 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 support through the 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 requirements | ✓ Ready to go straight away | Azure subscription required | Infrastructure setup required (Ollama, vLLM, HuggingFace) |
| Regulated industries | Restricted | Well suited | ✓ Ideal (no external API calls) |
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 customization 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 with a strong GDPR focus
- Mistral 7B: highly efficient on CPUs and small GPUs
- Mixtral (MoE): high quality with low resource consumption
- Multilingual, especially strong in German
Phi-4 (Microsoft)
Small Language Model · MIT license
- 14B parameters, with state-of-the-art quality
- Ideal for on-premises deployment with limited GPU capacity
- Strong 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
- MIT license, full commercial use permitted
Qwen 2.5 (Alibaba)
Coding · Multilinguality
- Particularly strong at code generation and analysis
- Very good multilingualism including German
- Model sizes range 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 aren’t a fallback option. For many business applications, Llama 3.3 and Mistral Large deliver quality comparable to GPT-4. The key 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 role should the AI play? What data does it need? Which systems need to be connected? What data protection requirements apply?
Technical integration concept
Model selection, system prompts, context architecture, token management, data pipelines, and security planning.
Development and integration
Backend integration, API connection, frontend embedding, and testing in the staging environment.
Productive operation and monitoring
Launch with monitoring, logging, and token controls, followed by continuous improvements to AI quality.
Why CodeGuides for your ChatGPT integration
ChatGPT integration is software development, not just prompt engineering.
We develop the backend layer that orchestrates API calls, adds context, and processes responses—not just the API connection.
ChatGPT needs context from your data. We develop the data pipelines that bring together user context, company data, and system status.
We help you choose between OpenAI Direct, Azure OpenAI, and open-source models, then develop an architecture suited to your data protection requirements.
Logging, token budgets, and cost monitoring are built in from the start to help prevent unexpected cost spikes in production.
We integrate AI features into your existing web app, backend, or SaaS product without rebuilding the entire system.
We deploy and integrate Llama, Mistral, Phi, and DeepSeek on your own infrastructure using Ollama, vLLM, or cloud services such as Azure ML and AWS Bedrock.
Logging, feedback loops, and response quality monitoring help keep AI operations productive and under control.
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 powered by your company’s data, with RAG architecture and source citations.
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 to the OpenAI API or Azure OpenAI. It embeds AI features such as text generation, classification, summarization, and data extraction in your web app, backend, or internal tools—with production-ready code and 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 the 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 you on model selection based on data protection, quality requirements, latency, and cost.
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 data transfers to external APIs are not allowed, (2) high request volumes make token costs prohibitive, (3) fine-tuning on your company’s data is planned, or (4) regulatory requirements in healthcare, finance, or law rule out external API calls. For many business use cases, their quality is comparable to GPT-4.
Can we start with a function?
Yes, we recommend starting with an integration for one clearly defined AI feature as an MVP, then gradually expanding it with additional features and system connections.
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 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.
Schedule 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