---
type: "LandingPage"
title: "LLM Development: GPT, Claude & RAG for Business"
description: "LLM and GPT development from Berlin: RAG systems, fine-tuning and production applications with GPT, Claude and open-weight models – integrated, GDPR-compliant."
resource: "https://www.contextstudios.ai/llm-development"
language: "en"
tags: ["LLM development", "GPT development", "large language model", "LLM application", "Claude development", "LLM fine-tuning", "RAG system", "enterprise LLM", "open-source LLM", "GPT API"]
generated:
  by: "process:contextstudios-md/1"
  at: "2026-10-09T01:19:30.246Z"
status: "stable"
---

# LLM Development: GPT, Claude & RAG for Business

LLM development makes large language models such as GPT, Claude or Llama usable for your specific tasks: connected to your company knowledge via RAG, adapted through fine-tuning where needed and integrated into your systems. Context Studios, an AI-native development studio in Berlin, turns this into monitored, GDPR-compliant applications instead of chat-window experiments.

Context Studios integrates large language models into your systems and brings them into production: from model selection through prompt engineering and RAG to fine-tuning and agent systems, tailored to your domain. The goal is precise, traceable and compliant applications that fit into existing IT landscapes and can be operated in a controlled way.

LLM development covers the design, adaptation and integration of large language models into business applications. The focus is rarely on training your own models, but on orchestrating existing foundation models such as GPT, Claude, Llama or Mistral with prompt engineering, RAG, fine-tuning and agent architectures.

Entity: LLM development

Specialisation: LLM applications, RAG systems, agent architectures, GPT integrations

Technologies: GPT, Claude, Llama, Mistral, Qwen, LangGraph, Convex

Target group: Mid-sized companies and enterprises with text-heavy processes

Project duration: Typically 4–16 weeks, depending on complexity

Compliance: GDPR, EU AI Act in view, on-premise options

## What does our work with language models include?

Six building blocks for production language model applications

### Model selection (GPT, Claude, Llama, Mistral)

Multi-model strategy: we compare models on your own sample tasks and use GPT, Claude, Llama or other models specifically where quality, cost and data protection fit together best.

### Prompt engineering and evaluation

Systematically developed prompts with test sets and automated evaluations ensure consistent outputs. Every change is measured before it goes into production.

### RAG pipelines (Pinecone, Weaviate)

Language models connected to your company knowledge: RAG delivers source-based answers from documents and databases and keeps knowledge current without retraining the model.

### Fine-tuning (LoRA, QLoRA)

Domain-specific adaptation of models to your terminology, formats and tone of voice when prompting and RAG alone are not enough. We check beforehand whether the effort is worthwhile.

### Agent architectures (LangGraph)

AI systems that plan, use tools and complete multi-step tasks: agent architectures connect language models with your systems and keep people in the loop for critical steps.

### Guardrails and compliance (EU AI Act, GDPR)

Guardrails, logging, model routing and caching: outputs stay within the defined scope, decisions remain traceable and ongoing API costs stay predictable.

## How does an LLM project work?

From the first idea to a monitored application in production.

### Initial call

Free 30-minute initial call by video. We clarify the use case, data sources and data protection requirements and give you a first assessment of feasibility, model choice and timeline.

### Evaluation and proposal

We test suitable models on sample tasks from your daily business and define quality criteria. You receive a written proposal with scope, timeline and fixed price.

### Development

Agile development with weekly demos. Goal: a first production version in approx. 4–8 weeks, with test sets, guardrails, monitoring and integration into your systems.

### Launch and operation

Production deployment with complete documentation and 30 days of free bug fixing from final delivery. Quality and cost monitoring and further development by agreement.

## Frequently asked questions about LLM development

Q: Which large language model is best suited to my business?

A: That depends on the use case: we weight text quality, language, cost per request, response speed and data protection requirements differently for each project. We work with GPT, Claude, Gemini, Llama, Mistral and other models, test them on your own sample tasks and recommend the right model. A combination often makes sense, with simple tasks routed to cheaper models.

Q: What does it cost to develop an LLM-based application?

A: Costs depend on the use case, the number of data sources and integrations, the need for fine-tuning and the requirements for hosting and compliance. On top come running costs for model calls, which we keep predictable with routing and caching. After a short scoping you receive a clear proposal: fixed price after scoping, proposal within 48 hours.

Q: How long does it take to develop an LLM solution?

A: The goal for a first production version is typically a period of approx. 4–8 weeks. A complete solution with fine-tuning, several integrations and enterprise features typically takes 8–16 weeks. The decisive factors are the data situation, the number of connected systems and the approval paths in your company. We agree the schedule together after scoping.

Q: Can LLMs work with our internal company data?

A: Yes. Through RAG architectures, language models access your documents, wikis and databases at runtime without this data being trained into the model. Access rights are preserved, so each person only receives answers from sources they are allowed to see. Fine-tuning can additionally adapt models to your domain and terminology.

Q: How do we avoid hallucinations in LLM answers?

A: Hallucinations cannot be ruled out completely, but they can be reduced significantly. We anchor answers in your sources via RAG and have the model cite them. We add structured outputs, plausibility checks and confidence thresholds that route uncertain cases to people. Test sets with real questions show before every release how reliably the application answers.

Q: Can GPT or Claude be used in a GDPR-compliant way?

A: Yes, with the right architecture. We use European hosting options and the providers' data processing agreements, encrypt data and pseudonymise personal content before it reaches a model. For particularly sensitive data we use open-weight models on your own infrastructure. We clarify which option fits together with your data protection officer.

Q: What is the difference between fine-tuning and RAG?

A: Fine-tuning adapts the model weights to your terminology, formats and tone; the knowledge then sits inside the model. RAG lets the model access your company knowledge at runtime and answer based on sources; changes to documents take effect immediately. For current factual knowledge RAG is usually the better choice, for style and special formats fine-tuning. Both approaches can be combined.

Q: Can we connect existing systems such as SAP or Salesforce to an LLM?

A: Yes. Via APIs, function calling and, where useful, the Model Context Protocol, we connect language models to CRM, ERP, helpdesk, DMS and other platforms. The model can then query data, create records or trigger workflows, always with the permissions you define. Critical actions can be tied to human approval.

Q: How do we measure the ROI of an LLM implementation?

A: Before the project starts we define measurable criteria, such as processing time per case, the share of requests resolved automatically or the error rate, and record a baseline. After launch we compare these values with project and operating costs. We check quality through automated benchmarks, human evaluation and A/B tests; model routing additionally lowers ongoing API costs.

Q: Which open-source models do you recommend as an alternative to GPT?

A: Llama, Mistral, Qwen and DeepSeek are capable open-weight alternatives that can also be self-hosted. Which model fits depends on the task, language, required quality and existing infrastructure. We evaluate the candidates on your sample tasks and also take licence terms and the effort for operation and updates into account.

Q: How does a professional GPT or LLM solution differ from ChatGPT or custom GPTs?

A: ChatGPT and custom GPTs from the GPT Store are useful for getting started. A professional solution goes much further: function calling, database connections, multi-step workflows, guardrails, logging, error handling and scaling. With your own API integration you keep control over data flows, keys and access and get a monitored application that is integrated into your systems.

Q: Can GPT applications also run offline or on-premise?

A: OpenAI's GPT models require an API connection; Azure OpenAI offers private endpoints in your virtual network. For fully offline-capable solutions we use open-weight models such as Llama or Mistral on your own infrastructure. We build the application so that the model can be swapped later without changing the rest of the architecture.

## Which task should a language model take on in your business?

Discuss use case, data sources and data protection in a free 30-minute call directly with the founder. You get an honest assessment of model choice and feasibility.

## What do we build LLM applications with?

## In which industries do we use LLMs?

## Examples of LLM projects

Examples we can deliver with you. Stated results are targets.

## Language model projects from Berlin-Charlottenburg