Enterprise IT
Complex AI software for large organisations: integration into existing SAP landscapes, Active Directory connection, multi-tenant architectures and enterprise SSO – designed to meet the requirements of IT security and compliance teams.
AI software engineering
AI Software Development
AI software development means building production software in which AI models are part of the architecture, not a bolted-on plugin. Context Studios, an AI-native development studio in Berlin, delivers backends with LLM integration, APIs and multi-service systems – tested, monitored, documented and maintainable by your own team.
Production-grade codeMonitoring & alertingCI/CD pipelines includedClean architecture principles
- Workshop
- Setup
- Sprint
- Build & Support
Fixed price after scoping · proposal within 48 h
Last updated:
(01)
What is AI software development?
AI service
AI software development is the engineering of software systems in which AI models are an integral part of the architecture. Beyond classic development it requires model selection and integration, prompt engineering, data pipelines, systematic evaluation and MLOps, so that AI features stay reliable, testable and maintainable in production.
- Specialisation
- AI backend systems, API development, system architecture, MLOps
- Technologies
- Python, TypeScript, Next.js, Convex, PostgreSQL, Docker, Kubernetes
- Target group
- CTOs, engineering teams, companies with existing IT infrastructure
- Project duration
- Typically 6–20 weeks, depending on system complexity
- Compliance
- GDPR-compliant, oriented towards ISO 27001
AI app developmentAI platform developmentAI API developmentAI integrationEnterprise AI development
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What makes our AI software production-ready?
Six engineering principles behind every AI system we deliver
(01)
Clean architecture for AI
Our AI software follows the SOLID principles with clearly separated layers: domain logic, application layer, AI integration and infrastructure. This lets you swap AI models without touching business logic and keeps the software maintainable and testable in the long run.
(02)
CI/CD and automated tests
Every project ships with a complete CI/CD pipeline: automated unit tests, integration tests, AI model evaluations and deployments. Pull requests are tested and reviewed automatically, so every code change is checked before it reaches production.
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Scalable backend architecture
Microservices, serverless functions or a well-structured monolith – we choose the architecture that fits your requirements and your team. Our backends are designed for horizontal scaling and handle many concurrent AI requests with consistent response times.
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Security by design
Security is an architectural principle, not a feature. We implement defence in depth with input validation, parameterised queries, encrypted data transfer, role-based access control and audit logging, and harden every API against the OWASP Top 10.
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Observability and monitoring
Each application ships with monitoring: dashboards for system health, AI performance metrics, error tracking and structured logging. When anomalies occur, you and your team are alerted before users are affected.
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Technical documentation
We deliver more than code: architecture diagrams, API specifications (OpenAPI), deployment guides and runbooks for operations. Your team can understand, run and extend the software without depending on us.
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How does an AI software project run?
- (01)
Consultation
Free 30-minute initial call via video. We get to know your business and your existing systems, identify where AI adds value and give you a first assessment of feasibility and timeline.
Day 1 - (02)
Proposal & planning
You receive a written proposal with scope, timeline, fixed price and a technical architecture outline.
Days 2–3 - (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 - (04)
Launch & support
Production deployment with complete documentation and 30 days of free bug fixing from final delivery. Maintenance and further development by agreement.
Week 4+
Frequently asked questions about AI software projects
(01)Which architecture patterns do you use for AI software?
We rely on clean architecture with clear layer separation: a domain layer for business logic, an application layer for use cases, an infrastructure layer for databases and external APIs, and a dedicated AI layer for model integration. This separation lets you replace models or databases without changing business logic and keeps each part independently testable.
(02)How do you ensure code quality?
Through several layers of quality assurance: automated unit and integration tests, static code analysis, peer review of every pull request, automated CI/CD pipelines and regular security checks. For AI components we additionally run systematic evaluations against defined test datasets, so changes to prompts or models are measured before they go live.
(03)Can you integrate AI into our existing software landscape?
Yes, integration into existing systems is one of our core competences. We analyse your current architecture, identify suitable integration points and build adapter layers that bring AI functionality into your software via APIs, message queues or events – without destabilising the systems you run today.
(04)How do you deal with technical debt?
We avoid technical debt through consistent code reviews, refactoring as a fixed part of every sprint and clear coding standards. If an existing system already carries technical debt, we draw up a refactoring plan with prioritised measures that are implemented step by step without putting ongoing operations at risk.
(05)Which database technologies do you use?
The choice depends on your use case: Convex for real-time applications with reactive data sync, PostgreSQL for transactional workloads, pgvector, Pinecone or Weaviate as vector stores in RAG systems, and Redis for caching and session management. We often combine several databases in a polyglot persistence architecture.
(06)How is the AI software deployed and operated?
We deliver infrastructure as code with Terraform or Docker Compose, so your IT can run deployments reproducibly. For serverless architectures we use Vercel and Convex with automatic scaling. For on-premise requirements we provide Docker containers that run in your own infrastructure, including monitoring and runbooks.
(07)How do you handle the performance of AI models?
Model inference can become a bottleneck. We implement streaming responses for a better user experience, request batching for efficiency, caching layers for recurring requests and asynchronous processing for long-running tasks. Parallel model calls and careful prompt design keep latency as low as the use case allows.
(08)Do you also modernise legacy systems with AI?
Yes. We modernise legacy systems step by step by integrating AI components. The strangler fig pattern lets us replace individual modules with AI-supported alternatives while the overall system keeps working. This way your software evolves gradually instead of being rewritten in one risky big bang.
(09)What are the running costs of AI software?
Running costs consist mainly of hosting and model API usage; API costs depend on the model and volume. We keep them down with caching, model routing that uses expensive models only when needed, and efficient prompt strategies. For development: fixed price after scoping, proposal within 48 hours.
(10)How do you secure quality and acceptance of the delivered software?
Our contracts contain clear acceptance criteria that we define together before the project starts, so there are no surprises at acceptance. From final delivery you receive 30 days of free bug fixing; maintenance and further development after go-live are available by agreement, and your team receives the documentation needed to take over.
Related pages
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Our software engineering stack
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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 16 & React 19TypeScriptReact Native & ExpoTailwind CSS v4Shadcn/uiVercel Edge Runtime
(03)
Backend & Data
Node.js & Hono (Edge)PythonPostgreSQL & SupabaseConvex (Real-Time DB)RedistRPC & GraphQLOpenAPI 3.1
(04)
DevOps & Infrastructure
Vercel & AWSDocker & KubernetesCI/CD-Pipelines (GitHub Actions)OpenTelemetry & GrafanaLangfuse (LLM Monitoring)
(05)
Industries we build AI software for
Insurance
Automated claims handling, contract analysis and AI-supported risk assessment. The software can extract relevant information from documents and photos and prepare decision proposals for your case handlers.
Telecommunications
AI software for network management, customer lifecycle analysis and automated customer service – analysing network data in real time and helping to anticipate outages.
Pharma & life sciences
Document management for clinical studies, detection of adverse events and AI-supported literature research, built with the validation requirements of regulated industries in mind.
Automotive
AI software for quality assurance in production, predictive maintenance of equipment and supply chain control, integrated with existing MES and ERP systems.
Trade & retail
Inventory management with AI forecasts, dynamic pricing and personalised customer communication, based on sales data, seasonality and market trends.
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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 answers automatically – around the clock.
Automated first response · Multilingual · Available 24/7
Knowledge management
RAG-based document system
A knowledge system with RAG architecture that searches large document collections and returns 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 report generation.
End-to-end automated · Fewer errors · Time savings
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AI software engineering – consultation in Berlin
- Founder AI-native since
- 2024
- Phone
- +49 30 96610664
- info [at] contextstudios [dot] ai
Let us build your AI software
Professional software development with AI at its core. Discuss your requirements in a 30-minute call directly with the founder.
- First call: 30 minutes by video
- Directly with the founder, Michael Kerkhoff
- Free of charge, no obligation
Context Studios · Kaiser-Friedrich-Str. 6 · 10585 Berlin