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AI infrastructure from Berlin

AI Platform Development

AI platform development creates a shared technical foundation on which your teams build many AI applications quickly, securely and under control, instead of reinventing everything for each project. Context Studios, an AI-native development studio in Berlin, builds such platforms with a model gateway, data access, governance and monitoring.

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AI-native development studio in BerlinClaude · GPT · Gemini · open sourceMulti-tenant architectureGovernance and cost control
  1. Workshop
  2. Setup
  3. Sprint
  4. Build & Support

Fixed price after scoping · proposal within 48 h

Last updated:

(01)

What is an AI platform?

AI service

An AI platform is a central technical infrastructure on which several AI applications of a company run. It provides shared building blocks, namely model access, data connections, security, monitoring and governance, so that new applications can be built faster, more cheaply and in compliance with the rules.

Specialisation
Platform architecture, model orchestration, API gateways, MLOps
Technologies
Convex, Kubernetes, Model Context Protocol, Terraform
Target group
CTOs and CIOs, enterprise IT, platform teams, multi-business-unit organisations
Project duration
Typically 10–20 weeks for a first platform version
Compliance
GDPR, security aligned with ISO 27001, EU AI Act requirements

Enterprise AI developmentAI integrationAI API developmentAI SaaS development

(02)

Which building blocks make up an AI platform?

Shared components instead of isolated solutions

(01)

Multi-model orchestration

Claude, GPT, Gemini, Llama and your own models through one uniform layer: the platform picks the right model for each request based on quality, cost and data protection and allows switching without rebuilding applications.

(02)

Central LLM gateway

A gateway bundles authentication, rate limiting, logging and cost tracking for all model calls. That way you keep an eye on usage and budgets per team or application.

(03)

Data infrastructure

Vector databases such as pgvector, Pinecone or Weaviate and real-time data with Convex make company knowledge usable for all applications, with a uniform access concept.

(04)

Governance & compliance

Central rules for data protection, approvals and documentation make it easier to comply with the GDPR and the EU AI Act. Every application inherits the platform's guardrails.

(05)

Monitoring & observability

Metrics, logging and tracing for all AI operations with Langfuse, LangSmith and Grafana show quality, latency and cost transparently.

(06)

Developer self-service

Templates, documentation and a portal let your teams build new AI applications on the platform on their own, faster and to shared standards.

(03)

How is an AI platform built?

  1. (01)

    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
  2. (02)

    Proposal & planning

    A detailed feature breakdown, a technical architecture plan and a written proposal covering scope, schedule and a fixed price.

    Step 2
  3. (03)

    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
  4. (04)

    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 AI platform development

(01)When does a company need a central AI platform?
As soon as several teams build their own AI applications, duplicate integrations, inconsistent security rules and uncontrolled costs appear. A platform handles these tasks once, centrally. It typically pays off from the second or third AI use case or when governance and cost control are required across the organisation.
(02)Can we build the platform step by step?
Yes, and we recommend it. We start with the building blocks your first use case needs, such as the model gateway, data access and monitoring, and extend the platform with each further application. That creates value early, and architecture decisions are tested against real requirements rather than assumptions.
(03)How long does it take to build an AI platform?
The goal is a first usable platform version in about 10–16 weeks. A comprehensive platform with all enterprise features such as a self-service portal, multi-tenancy and extensive governance typically emerges over several stages. Each stage delivers usable functionality instead of one big reveal at the end.
(04)How does the platform integrate with our existing IT?
Via APIs, connectors and your existing identity management. The platform connects data sources such as ERP, CRM, document management and data warehouse and takes over roles and permissions from systems such as Microsoft Entra ID. Existing applications can adopt the platform services step by step.
(05)How are costs allocated to departments?
The gateway records every model request with team, application and cost. From this come reports for internal charging, budgets and limits per area, and alerts on outliers. That keeps it transparent which application creates which value and which costs, and budgets can be managed in a targeted way.
(06)Which AI models can the platform manage?
Commercial models such as Claude, GPT and Gemini, open-source models such as Llama, Mistral or Qwen, and your own fine-tuned models, in the cloud or in your own data centre. New models can be added without adapting applications, because they are connected via a uniform interface.
(07)How do you ensure the platform's availability?
Through a redundant architecture, automatic scaling and fallback models: if a model provider fails or slows down, the gateway routes requests to an alternative. Monitoring and alerts report disruptions early. We agree operation and response paths to match the importance of the platform for your business.
(08)Can our developers build AI applications on the platform themselves?
Yes, that is a central goal. Templates, SDKs, documentation and a self-service portal let your teams develop new applications on their own, while security, data access and cost control remain centrally governed. If you wish, we support the first projects with training and reviews.
(04)

Technology stack for AI platforms

(01)

AI & ML

Anthropic Claude, OpenAI GPT, Google GeminiOpen-source LLMs (Llama, Mistral, Qwen)LLM gateway & model routingMCP (Model Context Protocol)RAG & vector databases (pgvector, Pinecone, Weaviate)Langfuse & LangSmith (observability)Terraform & Kubernetes
(02)

Web & Mobile

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

Backend & Data

Node.js & HonoPythonPostgreSQL & SupabaseConvex (Real-Time DB)RedistRPC & GraphQLOpenAPI
(04)

DevOps & Infrastructure

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

Who benefits from an AI platform?

Large enterprises & corporations

Organisation-wide AI infrastructure for many teams and business units that standardises and controls access to AI.

Technology companies

Central AI infrastructure for products and internal tools that delivers AI features consistently and cost-efficiently.

Financial services

Compliant platforms for analytics and automation, aligned with the requirements of banks and insurers.

Healthcare

Secure platforms for medical AI applications in which patient data protection is considered from the start.

SaaS providers

A multi-tenant AI backend that provides AI features to all customers and scales with them.

Research institutions

Flexible environments for experiments and model management that speed up research projects.

(06)

AI platforms: example projects

Examples we can build for you

Corporate

Internal AI platform for several departments

A central gateway, a knowledge base and templates let departments build their own assistants, with shared security rules and cost control.

Cost allocation per department · Shared access concept · Self-service portal
Software

AI backend for a SaaS product

A multi-tenant platform provides AI features for all of a product's customers, separates data cleanly and bills usage per customer.

Tenant separation · Usage-based billing · Model routing
Financial services

Governance layer for existing AI applications

Existing AI applications are connected to a shared gateway that standardises logging, approvals and EU AI Act documentation.

Central logging · EU AI Act documentation · Fallback models
(07)

AI platform development: consulting in Berlin

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

Plan your AI platform

Talk to us for 30 minutes about your AI plans, and we will show you which platform building blocks you need first.