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AI agents · RAG · MCP · Automation

AI Agency Berlin for Agents, RAG and Automation

We build AI agents that complete tasks on their own, RAG systems that answer with sources from your company knowledge, and MCP servers that give AI assistants secure access to your tools. Context Studios is an AI-native development studio in Berlin-Charlottenburg, and you work directly with founder Michael Kerkhoff.

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Glass high-rise facades in Berlin-Charlottenburg seen from below, with a verdigris copper roof edge against a pale grey skyAI-generated image
AI-native development studioModel-agnosticGDPR & EU AI ActBerlin-Charlottenburg
  1. Workshop
  2. Setup
  3. Sprint
  4. Build & Support

Fixed price after scoping · proposal within 48 h

Last updated:

(01)

What does an AI agency do?

Organization

An AI agency builds software in which language models do actual work: they read documents, call tools and hand over to people when a decision is needed. Context Studios is an AI-native development studio in Berlin-Charlottenburg for AI agents, RAG systems, MCP servers and workflow automation, model-agnostic with Claude, GPT, Gemini or open models.

Location
Berlin-Charlottenburg
Focus
AI agents, RAG systems, MCP servers, workflow automation
Model families
Claude, GPT, Gemini, Llama, Mistral, Qwen
Audience
Companies with recurring knowledge and process work
Project scope
Target: first MVP in about 4 weeks, then staged expansion
Compliance
GDPR, EU AI Act

AI Agent DevelopmentRAG DevelopmentMulti-Agent SystemsMCP Server Development

(02)

What we build as an AI agency

AI that works inside your processes instead of only answering questions.

(01)

AI agents

Agents triage requests, look up data, draft responses and trigger actions in your systems, with fixed approval points for your team.

(02)

RAG systems

Knowledge from documents, wikis and tickets becomes searchable. Answers come with sources, and existing permissions stay in place.

(03)

MCP servers

With the Model Context Protocol we make your applications usable for AI assistants, including authentication, permissions and logging.

(04)

Workflow automation

Processes between CRM, email, ERP and spreadsheets run automatically. AI takes over the steps that used to require reading and sorting.

(05)

Multi-agent systems

Specialised agents split complex tasks such as research, review and summarising, and check each other’s results.

(06)

Evaluation and operations

Tests on real cases, monitoring of quality and cost, and guardrails against misbehaviour keep an agent reliable after launch.

(03)

Technologies for agents and RAG

(01)

Model families

ClaudeGPTGeminiLlamaMistralQwenDeepSeek
(02)

Agents and protocols

Model Context Protocol (MCP)LangGraphTool callingPythonTypeScript
(03)

Knowledge and search

Vector databases (pgvector, Pinecone)Hybrid searchEmbeddingsReranking
(04)

Operations and observability

LangfuseOpenTelemetryDockerEU cloud regions
(04)

How an AI agent comes together

Start small, measure, then expand.

  1. (01)

    Intro call

    We discuss the workflow an agent should take over and which systems and data are involved.

    30 minutes
  2. (02)

    Scoping and test cases

    We define tasks and limits and collect real example cases against which quality can be measured later.

    about 1 week
  3. (03)

    Building the MVP

    Agent, integrations and interface come together in short iterations with a weekly demo until a first usable MVP is in place.

    Target: about 4 weeks
  4. (04)

    Pilot and expansion

    The agent runs with selected users, we evaluate the results and expand step by step. Maintenance is agreed separately if needed.

    afterwards
(05)

Industries with typical agent tasks

FinTech and banking

Pre-check KYC and credit documents, classify requests, make internal policies searchable via RAG.

Healthcare

Structure doctors’ letters and findings and make internal guidelines searchable, in line with data protection rules.

E-commerce

Maintain product copy and attributes, pre-answer customer requests, analyse return reasons.

SaaS providers

AI features and an MCP server for your product, so customers can use it from their AI assistants.

Legal and compliance

Search contracts by clause, detect deadlines, research with citations.

Logistics

Answer shipment enquiries, spot exceptions, consolidate dispatch data.

(06)

Agent projects we can deliver

Typical projects from our focus area. Not references; any figures are targets.

E-commerce

Support agent with a knowledge base

An agent answers recurring customer requests in several languages from your knowledge base and hands complex cases to your team with a summary.

Target: most standard requests handled automatically · Hand-over to people built in
Legal

RAG research for legal departments

A RAG system searches contracts, briefs and templates and answers with citations, so every statement can be verified.

Answers with sources · Permissions per matter
SaaS

MCP server for an existing product

An MCP server makes your application usable from Claude, ChatGPT and other assistants. Customers retrieve data and trigger actions without switching interfaces.

Target: usable in common AI assistants · Permissions and logging

Frequently asked questions about our AI agency

(01)What does a project with an AI agency cost?
It depends on scope, integrations and data. That is why we define the frame in scoping first. After that: fixed price after scoping, proposal within 48 hours.
(02)How do I find the right AI agency in Berlin?
Check whether the agency has built and operated agents and RAG systems itself, whether it measures quality with real test cases and whether it is model-agnostic. A good agency will also tell you when a simple automation without AI is enough.
(03)What is the difference between a chatbot and an AI agent?
A chatbot answers questions. An AI agent completes tasks: it calls tools, reads from and writes to your systems and works through several steps in sequence, within clearly defined permissions.
(04)What is an MCP server and why would I need one?
The Model Context Protocol is an open standard that lets AI assistants access tools and data. An MCP server for your application makes its functions usable from assistants such as Claude or ChatGPT, with its own sign-in and permissions.
(05)How long does an AI agent project take?
Our target for a first agent MVP is about 4 weeks. The actual duration depends on integrations, data volume and regulatory requirements. After scoping you receive a milestone plan.
(06)Which models do you use?
We work with Claude, GPT and Gemini as well as open models such as Llama, Mistral or Qwen. The choice depends on task, quality, cost and data protection, and switching models later remains possible.
(07)How do you ensure data protection?
We use providers’ EU regions or self-hosting, give agents only the permissions they need and log access. For personal data we settle data processing agreements and the EU AI Act risk class up front.
(08)What sets Context Studios apart from other AI agencies?
We are an AI-native development studio, not a web agency that adds a chat interface on the side. Agents, RAG and MCP are our core business, and you work directly with the founder, Michael Kerkhoff.
(09)Can I visit you in Berlin?
Yes. Our office is in Berlin-Charlottenburg, Kaiser-Friedrich-Str. 6, 10585 Berlin. The free 30-minute initial call takes place on site or by video, whichever suits you.
(07)

Discuss your AI agent project

Book a free 30-minute call or tell us which workflow an agent should take over for you.

(08)

AI agency in Berlin-Charlottenburg

Founder AI-native since
2024
Address
Kaiser-Friedrich-Str. 6, 10585 Berlin
Email
info [at] contextstudios [dot] ai
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In the first call we work out which format fits your project and what the next step is.