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AI-native development studio

AI Development for Businesses

AI development covers the design, training, integration and operation of AI systems that solve concrete business problems. Context Studios builds tailor-made solutions with machine learning, language models and computer vision – from proof of concept and MVP to production, GDPR-compliant and directly with the founder.

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Octagonal tower with a honeycomb facade of glass blocks photographed from below, glass in muted teal blue against a pale skyAI-generated image
From PoC to enterprise operationGDPR-compliant, EU AI Act in view
  1. Workshop
  2. Setup
  3. Sprint
  4. Build & Support

Fixed price after scoping · proposal within 48 h

Last updated:

(01)

What is meant by AI development?

Engineering discipline

AI development is the design, programming, training and operation of software that uses artificial intelligence methods to handle tasks such as forecasting, text understanding or image analysis. It combines data preparation, model selection, integration into existing systems and MLOps. Context Studios delivers such projects as an AI-native development studio.

Specialisation
End to end: ML, NLP, computer vision, generative AI, predictive analytics
Technologies
Python, PyTorch, TensorFlow, Convex, OpenAI, Anthropic Claude, Hugging Face, FastAPI
Target group
Companies of all sizes with data-driven challenges
Methodology
Agile development, MLOps, CI/CD for ML, data-driven iteration
Compliance
GDPR, EU AI Act

AI software developmentMachine learning developmentAI app developmentAI platform developmentAI developersAI agency

(02)

Which areas does developing AI cover?

Six areas of expertise for successfully delivering your AI project.

(01)

Machine learning & deep learning

Development of ML models for classification, regression, clustering and anomaly detection. From classical algorithms and ensemble methods to neural networks – we choose the best approach for your use case and your data.

(02)

Natural language processing

NLP solutions for text analysis, sentiment detection, named entity recognition, summaries and chatbots. We use modern transformer architectures and LLMs to process language in all its facets.

(03)

Computer vision

Image recognition and analysis systems for object detection, image segmentation, OCR and visual quality control. We use established architectures such as YOLO, vision transformers and Segment Anything for precise image analysis.

(04)

Generative AI & LLM applications

Development of applications based on large language models – from RAG systems and AI assistants to content generation. Integration of GPT, Claude, Gemini and open-source models into your business processes.

(05)

Predictive analytics

Forecasting models for sales, customer behaviour, machine failures and market developments. Data-driven forecasts enable proactive decisions and give you a strategic edge over the competition.

(06)

MLOps & production operation

Robust deployment pipelines, automated model monitoring and lifecycle management. With MLOps we make sure your AI models run reliably, scalably and maintainably in production.

(03)

How does an AI project with us work?

  1. (01)

    Consultation call

    Free initial call via video. We get to know your business, identify AI potential and give you a first assessment of feasibility and schedule.

    Day 1
  2. (02)

    Proposal & planning

    You receive a written proposal with scope, schedule and fixed price.

    Days 2–3
  3. (03)

    AI-accelerated development

    Agile development with weekly demos. Goal: a working MVP in about 4 weeks, with production-ready code and automated tests.

    Weeks 1–4
  4. (04)

    Launch & support

    Production deployment with complete documentation.

    Week 4+

Frequently asked questions about AI projects

(01)What do I need to start an AI project?
Ideally, you bring a clearly defined business problem, relevant data and internal stakeholders. But we can start even without existing data – in a discovery workshop we identify opportunities and clarify which data needs to be collected or sourced. What matters most is a committed project owner on your side.
(02)How long does a typical AI project take?
The duration varies with complexity. As a guide: a proof of concept typically takes 2–4 weeks, an MVP 6–10 weeks and a complete enterprise system 12–24 weeks. We always recommend starting with a focused PoC to validate feasibility and value quickly.
(03)What does an AI project cost?
Costs depend on scope, data situation and integration effort. Fixed price after scoping, proposal within 48 hours. To get started there are fixed-price workshops: Light Discovery (half a day) for €1,500, Strategy Day for €2,500 and Prototyping Sprint (2 days) for €4,500. This lets you clarify value and feasibility before committing larger budgets.
(04)What data do you need for development?
That depends on the use case. Classical machine learning needs historical, structured data – the more, the better. For LLM-based applications, a few examples and domain knowledge are often enough. We analyse the available data and, if necessary, recommend strategies for enriching it.
(05)Can you also improve existing AI models?
Yes, optimising existing models is one of our core services. We analyse performance, identify weaknesses and improve accuracy, speed or robustness. Typical measures include feature engineering, hyperparameter tuning, architecture optimisation and retraining with new data. We agree on measurable goals beforehand so that the improvement is traceable.
(06)How do you make sure the AI solution works in the long term?
Through consistent MLOps practices: automated monitoring, drift detection, regular retraining and performance alerts. From the start we build systems that adapt to changing data and requirements, and we document them so that your team understands them. On request, we take over ongoing operation by agreement.
(07)Which programming languages and frameworks do you use?
Python is our main language for ML development, complemented by TypeScript for web interfaces and APIs. We use PyTorch, TensorFlow, scikit-learn, Convex, Hugging Face and all major LLM APIs. The choice of framework depends on the best fit for your use case.
(08)Can the AI solution be integrated into our existing systems?
Yes, integration is a central part of our work. We develop REST and GraphQL APIs, webhook-based integrations and direct database connections. Whether ERP, CRM, data warehouse or custom software, we integrate the AI solution so that it fits your existing system landscape.
(09)How do you handle sensitive data and data protection?
Data protection is firmly anchored in our development process. We work with privacy by design, encryption, access control and pseudonymisation, develop in line with the GDPR and prepare you for the requirements of the EU AI Act. On request, we run the solution exclusively in your own infrastructure.
(10)Do you also offer support after go-live?
Yes. We fix defects free of charge for 30 days from final delivery. Maintenance and further development after go-live are agreed individually – from monitoring and bug fixing to regular model reviews and concrete improvement suggestions – so your solution grows with your requirements and stays reliable.
(11)Where is the data stored in AI projects?
By default in European data centres, for example with AWS in Frankfurt or Hetzner in Germany. On request, we run the solution entirely in your own infrastructure. We document transparently which data flows to which services, so that data protection and IT security can trace every decision.
(12)What does the EU AI Act mean for my project?
We place your use case early in the risk classes of the EU AI Act and implement the transparency obligations, documentation and technical measures that apply. This way you know before the start which obligations apply to your project and avoid costly rework shortly before go-live.
(13)Do you work across Germany and Europe?
Yes. We work from Berlin for companies across Germany and Europe, remotely via video call and in four languages: German, English, French and Italian. We take country-specific requirements such as language, data protection or industry regulations into account from the start.
(14)How do you handle multiple languages?
We use multilingual language models and embeddings that cover all common EU languages, and we test the systems in every target language with real examples from your day-to-day business. Ideally you maintain content only once; answers are produced in the user's language.
(15)Is GDPR-compliant AI development possible?
Yes. As a German company we build GDPR-compliant by default. On request we deliver private AI systems or on-premise deployments where no data leaves your infrastructure.
(16)Can my data stay in Europe?
Yes. We host in European data centres or on your own servers. With open-source models such as Llama or Mistral, fully self-hosted setups are possible too.
(04)

Our technology stack

(01)

AI & ML

Anthropic ClaudeOpenAI GPTGoogle GeminiOpen-Source LLMs (Llama, Qwen, DeepSeek, Mistral)ConvexRAG & Vector DBs (Pinecone, Weaviate)MCP (Model Context Protocol)Hugging Face TransformersComputer Vision (YOLO, SAM)ElevenLabs (Voice AI)Google Veo (Video AI)
(02)

Web & Mobile

Next.js & ReactTypeScriptReact Native & ExpoTailwind CSSShadcn/uiVercel Edge Runtime
(03)

Backend & Data

Node.js & Hono (Edge)PythonPostgreSQL & SupabaseConvex (Real-Time DB)RedistRPC & GraphQLOpenAPI
(04)

DevOps & Infrastructure

Vercel & AWSDocker & KubernetesCI/CD-Pipelines (GitHub Actions)OpenTelemetry & GrafanaLangfuse (LLM Monitoring)
(05)

Which industries do we develop AI for?

Financial services

Development of AI models for credit scoring, fraud detection, algorithmic trading and regulatory reporting. Systems that process large transaction volumes in real time and take regulatory requirements into account.

Healthcare & life sciences

Medical image analysis, clinical decision support, drug discovery and patient data analysis. MDR-compliant development processes with full auditability.

Manufacturing & automotive

Predictive maintenance, visual quality assurance, production planning and autonomous control systems. AI models connected directly to machine controls and IoT sensors.

Telecommunications

Network optimisation, churn prediction, intelligent customer interaction and capacity planning. AI systems that improve the user experience and reduce operating costs.

Real estate & PropTech

AI-supported property valuation, market forecasts, automated document processing and intelligent building control. Data models for analysing large volumes of market transactions.

Media & publishing

Content personalisation, automated content moderation, recommendation systems and semantic search. AI solutions that increase user engagement and speed up editorial processes.

(06)

Example projects

Examples we can build for you

Customer service

AI-powered support agent

An AI agent that understands customer requests in natural language, accesses internal knowledge bases and delivers answers automatically – around the clock.

Automated first response · Multilingual · Available 24/7
Knowledge management

RAG-based document system

An intelligent knowledge system with RAG architecture: it searches large document collections and delivers source-based answers in seconds.

Source-based answers · Fast search · Scalable
Process automation

Workflow automation with AI agents

Autonomous AI agents that automate recurring business processes – from data extraction to reporting.

Automated end to end · Fewer errors · Time savings
(07)

Consultation in Berlin

Founder AI-native since
2024
Email
info [at] contextstudios [dot] ai

Start your AI project

Start your project with Context Studios, your AI-native development studio in Berlin – with a free 30-minute initial call.