--- type: "LandingPage" title: "AI Platform Development: Gateway & Governance" description: "AI platform development: shared infrastructure for many AI applications with model gateway, data access, governance and monitoring, GDPR-compliant." resource: "https://www.contextstudios.ai/ai-platform-development" language: "en" tags: ["AI platform development", "AI platform", "enterprise AI platform", "AI infrastructure", "LLM gateway", "model orchestration", "AI governance", "MLOps", "multi-tenant AI", "AI platform engineering"] generated: by: "process:contextstudios-md/1" at: "2026-10-08T19:33:38.828Z" status: "stable" --- # AI Platform Development: Gateway & Governance 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. 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. Entity: AI Platform Development 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 ## Which building blocks make up an AI platform? Shared components instead of isolated solutions ### 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. ### 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. ### 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. ### 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. ### Monitoring & observability Metrics, logging and tracing for all AI operations with Langfuse, LangSmith and Grafana show quality, latency and cost transparently. ### 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. ## How is an AI platform built? ### 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. ### Proposal & planning A detailed feature breakdown, a technical architecture plan and a written proposal covering scope, schedule and a fixed price. ### AI-accelerated development Agile development with weekly demos and production-ready code backed by automated tests. Goal: a working MVP in about 4 weeks. ### Launch & operation Production deployment with complete documentation and handover. 30 days of free bug fixing from final delivery; maintenance and further development by agreement. ## Questions about AI platform development Q: When does a company need a central AI platform? A: 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. Q: Can we build the platform step by step? A: 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. Q: How long does it take to build an AI platform? A: 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. Q: How does the platform integrate with our existing IT? A: 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. Q: How are costs allocated to departments? A: 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. Q: Which AI models can the platform manage? A: 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. Q: How do you ensure the platform's availability? A: 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. Q: Can our developers build AI applications on the platform themselves? A: 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. ## 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. ## Technology stack for AI platforms ## Who benefits from an AI platform? ## AI platforms: example projects Examples we can build for you ## AI platform development: consulting in Berlin