AI Integration

🤖 LLMs create real value when they are embedded into real processes

A single language model can answer questions.

A meaningful AI integration does much more: it accesses company data, makes decisions based on clear rules, calls APIs and returns results into existing systems: the shop, the ERP, the support platform or an internal tool.

That is where measurable value is created.

We integrate large language models, RAG systems and AI agents into existing software landscapes. Cloud-based via providers like Anthropic (Claude), OpenAI or OpenRouter, or fully self-hosted on dedicated hardware. With a clear focus on data privacy, cost control and long-term maintainability.

⚙️ When AI integration is economically reasonable

Not every task needs an LLM. Classical software is often faster, cheaper and more predictable.

AI integration plays out its strengths especially when:

• large amounts of unstructured data must be evaluated
• natural language is required as input or output
• knowledge from many scattered sources must be combined
• decisions must follow flexible rules
• recurring routine tasks can be automated

In these scenarios, the investment pays off. In other cases, classical interfaces, workflows or scripts are often the better choice, and that is exactly what we will tell you.

🧩 RAG systems: making your own data usable

Retrieval Augmented Generation (RAG) combines language models with controlled, internal data sources.

Instead of letting the model answer alone, relevant knowledge is retrieved from a vector database and passed as targeted context.

This solves two key problems:

• hallucinations are significantly reduced
• current and company-specific data becomes usable

Typical use cases include:

• internal knowledge bases and employee assistants (e.g. based on Notion)
• technical support systems on top of product documentation
• research tools across contracts, tickets and emails
• product advisors in eCommerce based on internal data

On the technical side, we typically work with Qdrant as a vector database, LangChain, LlamaIndex or Paperclip for pipeline logic and an LLM chosen per requirement, cloud or self-hosted.

🛠️ AI agents: tasks, not just answers

An agent is more than a chatbot.

It receives a goal and decides on its own which tools to use: calling APIs, querying data, chaining steps and returning results.

Typical examples from real projects:

Support automation

Read tickets, classify them, search the knowledge base, draft a response, escalate if needed.

Shop and ERP workflows

Validate orders, enrich master data, generate product texts, answer supplier inquiries automatically.

Back office automation

Extract data from PDFs, emails or Excel files and feed it back into existing systems in a structured way.

Research and analysis agents

Multi-step research across internal and external sources with a clear audit trail.

We deploy agents where they work faster or cheaper than manual processes, not as an end in itself.

🏗️ Typical architecture of an AI integration

Business Platform
│
├─ Data sources (ERP, shop, PIM, Notion / wiki, tickets, emails, files)
├─ Indexing & embeddings
├─ Vector database (Qdrant)
├─ RAG / agent layer (LangChain, LlamaIndex, Paperclip)
├─ LLM (Claude / GPT in the cloud, self-hosted e.g. Llama, Qwen, Mistral via Ollama / vLLM)
├─ Orchestration & workflows (n8n, custom services)
└─ Integration with existing systems (APIs, webhooks, UIs)

This architecture is intentionally modular.

Individual components can be replaced: switching models, scaling the vector database or replacing a provider with a self-hosted solution without rebuilding the entire application.

☁️ Cloud LLMs or self-hosted?

This is the most important decision in any AI integration.

We work with both and provide honest advice on what fits each case.

Cloud models (Claude by Anthropic, GPT by OpenAI, additional models via OpenRouter as a gateway)

• currently leading for complex reasoning tasks
• no infrastructure to operate, fast to start
• cost scales per token with usage
• data leaves your environment

Self-hosted models (Llama, Qwen, Mistral and other open-source models, run e.g. via Ollama or vLLM)

• full data sovereignty
• predictable cost based on hardware instead of tokens
• lower latency within your network
• higher requirements for hardware and operations

In many projects, a hybrid setup is the best choice: sensitive workloads run locally, demanding reasoning tasks run in the cloud.

🖥️ Hardware planning for self-hosted LLMs

Self-hosted models depend heavily on the right hardware.

We plan setups ranging from a small single-GPU server for internal tools up to multi-GPU machines for production inference under load.

Typical aspects of the planning:

• model choice (e.g. 7B, 13B, 70B, MoE architectures like Mixtral)
• quantization (e.g. 4-bit, 8-bit) to reduce memory requirements
• GPU selection (VRAM, bandwidth, power)
• inference stack (Ollama, vLLM, llama.cpp)
• scaling across multiple nodes
• monitoring and load balancing
• backup, update and model rollout strategy

We do not sugarcoat. If a use case does not fit the available hardware, we say so and propose alternatives via cloud or hybrid setups.

🧰 Technology stack

We deliberately work with a clear, controllable stack:

• LLMs (cloud): Claude (Anthropic), GPT (OpenAI), additional models via OpenRouter
• LLMs (self-hosted): Llama, Qwen, Mistral and other open-source models
• Inference runtimes: Ollama, vLLM, llama.cpp
• Vector database: Qdrant
• Agent & RAG frameworks: LangChain, LlamaIndex, Paperclip
• Typical data sources: Notion, internal wikis, ERP and shop systems, ticket systems, mailboxes, file storage
• Workflow orchestration: n8n
• Backend: Symfony / PHP, Spring Boot / Java, Node.js, depending on the existing system landscape
• Infrastructure: Docker, Kubernetes, Hetzner, Kubernetes ONE (Profihost), AWS

This keeps projects maintainable and evolvable, even without Kickbyte.

🔐 Data privacy, security and control

AI integration almost always touches sensitive data.

That is why privacy and security are not an afterthought for us, but a starting point.

Concrete building blocks:

• data classification before integration
• clear separation between index and request data
• GDPR-compliant hosting options, including Germany
• logging and audit trails for all agent actions
• configurable filters and guardrails
• fully self-hosted setups without external APIs when needed

⚠️ Challenges in AI projects

AI projects rarely fail because of the technology. They fail because of unclear goals and poor data quality.

Typical challenges:

• vague or overly broad use cases
• fragmented or poor data
• missing evaluation of quality and accuracy
• runaway costs from inefficient prompts or models
• weak integration into existing processes

We address these projects pragmatically: clear use case, fast prototype, measurable results, then production rollout.

🧑‍💻 Our role in AI projects

We support companies along the full lifecycle of an AI integration.

Typical responsibilities include:

• use case evaluation and business case analysis
• prototyping and proof of concept
• architecture and model selection
• building RAG systems and agents
• integration into existing systems via APIs and workflows
• hardware planning for self-hosted LLMs
• operation, monitoring and continuous improvement

We combine AI expertise with years of experience in custom development and system integration. That combination is what makes the difference. AI without clean integration remains a toy.

🎯 When AI integration makes the most sense

AI integration is particularly valuable for companies that:

• hold large amounts of data in documents, emails, tickets or PIM/ERP
• want to automate repetitive tasks
• need to make internal knowledge more accessible
• want to extend their shops, products or services with AI features
• deliberately focus on data sovereignty and long-term independence

In all these cases, a clean AI integration delivers real and lasting value.

🧠 AI that fits your business

AI is no longer an end in itself.

It is becoming a regular part of modern business processes: in eCommerce, in the ERP, in support, in internal knowledge management.

The decisive factor is not the largest model, but the right combination of use case, model, data and integration.

We build AI solutions that fit into existing systems, deliver measurable value and stay maintainable over time.