One point of contact, the right specialists

AI Development Team

An AI development team from Context Studios means one central point of contact who owns accountability and architecture, plus experienced freelancers and specialists who join depending on the project. Founder Michael Kerkhoff leads every engagement himself and assembles the right expertise: flexible, without overhead costs and with knowledge transfer to your team.

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AI-native development studio in BerlinClaude · GPT · Convex · Next.jsWorking in your tools and sprintsAgile development with weekly demos

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Sprint
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Fixed price after scoping · proposal within 48 h

Last updated: October 1, 2026

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How is the team at Context Studios structured?

AI service

An AI development team at Context Studios is a project-based team with a fixed core: founder Michael Kerkhoff is the central point of contact and is responsible for architecture and quality. Depending on the task, he brings in experienced freelancers and specialists from a curated network, for example for data engineering, design or quality assurance.

Point of contact: Founder Michael Kerkhoff, responsible for architecture and quality
Network: Experienced freelancers and specialists, brought in as needed
Roles: AI engineering, full-stack development, plus data engineering, design and quality assurance where needed
Target group: Companies that need AI development
Duration: Depends on the project, freely agreed
Billing: Scope agreed in advance, predictable costs

AI developer AI development partner AI development company AI agency or freelancer?

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What are the advantages of this team model?

One person who is accountable, plus exactly the expertise your project needs

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One central point of contact

Michael Kerkhoff leads your project himself: he is responsible for architecture, technical decisions and quality, and remains your direct contact throughout, with no hand-offs between sales, project management and development.

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A curated network

Depending on the task, experienced freelancers and specialists join, for example for data engineering, frontend, design or quality assurance. They work to shared code standards and under the founder's architectural responsibility.

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The right expertise for each project

Instead of a fixed team that has to fit every task, the line-up follows your project: AI engineering with Claude, GPT and open-source models, plus exactly the specialism the project needs at that stage.

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Flexible scope

The scope grows and shrinks with your needs. Specialists are brought in for individual phases and released afterwards, so you pay for work on your project, not for the utilisation of a fixed team.

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No overhead costs

No recruiting, no payroll, no administrative apparatus of a large agency. Scope and costs are agreed in advance and therefore stay predictable.

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Quality and process integration

We work in your tools such as Jira, GitHub and Slack, with shared sprints, code reviews, automated tests and CI/CD, coordinated with your internal team.

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How does the collaboration start?

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Initial call
A free 30-minute video call with Michael Kerkhoff. We get to know your project, assess where AI adds value and give you a first estimate of feasibility, effort and timeframe.
Step 1
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Proposal & planning
A detailed feature breakdown, a technical architecture plan and a written proposal covering scope, schedule and a fixed price.
Step 2
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AI-accelerated development
Agile development with weekly demos and production-ready code backed by automated tests. Goal: a working MVP in about 4 weeks.
Step 3
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Launch & operation
Production deployment with complete documentation and handover. 30 days of free bug fixing from final delivery; maintenance and further development by agreement.
Step 4

Questions about the team model

(01)Who is our point of contact?

Your central point of contact is founder Michael Kerkhoff. He leads the project from the first call to handover, is responsible for architecture and technical quality, and makes the key decisions together with you. Freelancers and specialists who join work under his lead. Throughout the collaboration you have one person who knows your project completely and stands behind it.

(02)How big is the team?

There is deliberately no fixed team size. The core is always the founder; depending on the project, he brings in experienced freelancers and specialists from his network, for example for data engineering, frontend, design or quality assurance. The line-up depends on the task and phase and is defined together during scoping. Only people whose expertise is genuinely needed work on your project.

(03)How much does an AI development team cost?

Costs depend on scope, the expertise required and the duration of the project. Because you are not funding agency overhead or a permanently employed team, you pay for the work on your project. Fixed price after scoping, quote within 48 hours. A fixed-price workshop from €1,500 is also a good way to get to know each other.

(04)How long does the collaboration run?

The duration depends entirely on your project: from a clearly scoped project through several phases to ongoing development. Scope and line-up can be adjusted between phases, with specialists joining or being released. We agree a clear goal for each phase so that the value stays visible and you can make a well-founded decision about the next step.

(05)Do we get to know the people involved beforehand?

Yes. Michael Kerkhoff leads the first call himself and remains your point of contact. If freelancers or specialists join for a project phase, we introduce them to you before they start and explain their role. You always know who is working on your project and can judge whether their way of working and communicating fits your team.

(06)How does quality stay consistent when specialists join?

Architecture, code standards and reviews are in one pair of hands. The founder defines the technical foundation, checks contributions in code review and is responsible for automated tests, CI/CD and documentation. Weekly demos and transparent reporting show you the progress. Quality therefore stays consistent even when the line-up changes between project phases.

(07)What happens with knowledge transfer at the end?

At the end of the collaboration we carry out a structured knowledge transfer: code walkthroughs, architecture documentation, video recordings and Q&A sessions with your team. If you wish, we support building your internal AI team with coaching and mentoring so that you can develop the solution further on your own.

(08)Can the team work on site?

We mostly work remotely, which has proven effective for focused development work. For important dates such as kickoffs, sprint reviews or strategy workshops we are happy to come to you, and meetings at our studio in Berlin-Charlottenburg are always possible. We agree hybrid models individually.

(09)How does our own team build AI skills along the way?

By working on the same code: pair programming, code reviews and documented decisions make knowledge accessible to your developers. We also offer training and workshops. Step by step, your organisation builds the skills to run and extend AI systems itself.

Industries

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Which technology stack does the team work with?

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AI & ML

Anthropic ClaudeOpenAI GPTGoogle GeminiOpen-source LLMs (Llama, Qwen, DeepSeek, Mistral)RAG & vector databases (pgvector, Pinecone, Weaviate)MCP (Model Context Protocol)Hugging Face Transformers

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Web & Mobile

Next.js & ReactTypeScriptReact Native & ExpoTailwind CSSshadcn/uiVercel Edge Runtime

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Backend & Data

Node.js & HonoPythonPostgreSQL & SupabaseConvex (Real-Time DB)RedistRPC & GraphQLOpenAPI

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DevOps & Infrastructure

Vercel & AWSDocker & KubernetesCI/CD (GitHub Actions)OpenTelemetry & GrafanaLangfuse (LLM Monitoring)

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Which situations does this model suit?

Growth-stage startups

Ship features fast after funding while the founding team focuses on sales and customers, without the distraction of recruiting.

SMEs with AI ambitions

The team works like a temporary AI department, developing solutions while building internal know-how in parallel.

Corporates with capacity bottlenecks

IT departments that cannot handle AI projects alongside day-to-day work get an independent unit integrated into corporate processes.

Agencies & service providers

Agencies that want to offer AI services to their clients get development capacity in the background while they manage the client relationship.

Product companies

Introducing AI as a core feature: AI modules that fit into the existing product, with a growing understanding of the codebase and users.

Research institutions

Turning research approaches into working software and supporting the technical side of projects and grant applications.

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Project examples: what the collaboration can look like

Examples we can build for you

SME

Temporary AI department for an SME

The team implements several AI applications one after another, from a document workflow to an internal knowledge assistant, while training the internal IT team.

Several applications in sequence · Knowledge transfer · Predictable costs

Software

Extending a product team

AI engineering complements an existing product team, works in its sprints and repositories and brings AI features into the product.

Work in the client's systems · Shared code standards · Goal: faster AI features

Agency

White-label development for an agency

The team develops AI solutions for a digital agency's clients, while the agency owns consulting and the client relationship.

Work in the background · Clear interfaces · Documented handover

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