Banking & insurance
AI for preparing credit decisions, claims handling and regulatory reporting – with human approval and auditable documentation.
AI for large enterprises
Enterprise AI Development
Enterprise AI development brings AI safely into the processes of large organisations: with governance, access control, audit logging and integration into SAP, Salesforce or Azure. Context Studios builds scalable AI systems for multiple business units, takes the EU AI Act and data protection into account from the start and works within your existing IT governance.
Enterprise-grade architectureSecurity by designRollout-ready architectureC-level consulting
- Workshop
- Setup
- Sprint
- Build & Support
Fixed price after scoping · proposal within 48 h
Last updated:
(01)
What does enterprise AI development mean?
AI service
Enterprise AI development is the design and implementation of AI systems for large organisations. Unlike startup projects, the focus is on security, governance, approval processes and integration into established IT landscapes. Context Studios takes multi-BU architectures, SAP integration and EU AI Act compliance into account from the outset.
- Specialisation
- AI strategy for corporates, multi-BU rollout, AI governance
- Technologies
- Claude Enterprise, Azure OpenAI, Convex, SAP AI, Kubernetes
- Target group
- CTO/CIO, CDO, transformation office
- Typical project duration
- Typically 12–24 weeks for corporate implementations
- Relevant standards
- ISO 27001, BSI IT-Grundschutz, EU AI Act
(02)
What makes AI development for large enterprises different?
The requirements we address in every enterprise project
(01)
Multi-BU architecture
AI architectures for multiple business units with central governance and decentralised configuration. Each business unit gets its own data spaces, roles and prompts, while model choice, cost control and security policies are managed centrally.
(02)
Security & compliance
Encryption in transit and at rest, role-based access control with LDAP or SSO integration, and complete audit logging. This keeps it traceable who processed which data with which model.
(03)
Legacy integration
Integration into SAP, Oracle and Salesforce via stable interfaces, without destabilising existing systems. We attach AI functions to your core systems instead of replacing them, and respect your release and change processes.
(04)
Change management
Structured change management with stakeholder communication, training programmes and success measurement. AI only gets used when teams understand what it can do and where its limits are.
(05)
Governance & ethics
AI governance with clear responsibilities, defined decision processes and ethical guidelines in line with the EU AI Act. We document risk classes, data flows and controls so that internal audit and data protection can review them.
(06)
Scalable infrastructure
Kubernetes-based deployments that scale with a growing number of users and are designed for high availability. Monitoring, cost tracking per business unit and clear operating procedures are part of it from the start.
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How does an enterprise AI project work?
- (01)
Consultation call
Free initial call (30 minutes). We get to know your starting point and IT landscape and identify the AI opportunities with the greatest leverage.
Day 1 - (02)
Planning & proposal
Fixed price after scoping, proposal within 48 hours.
Days 2–3 - (03)
Development
Agile development with weekly demos and interim results, coordinated with IT security, data protection and business units.
Weeks 1–8 - (04)
Launch & operations
Deployment, monitoring and continuous optimisation.
Ongoing
Frequently asked questions about enterprise AI
(01)What makes AI development for large enterprises more expensive?
Corporate projects require additional effort for security architecture, legacy integration, change management and compliance documentation. On top of that come alignments with IT security, the works council and data protection. This work is not overhead but a prerequisite for an AI system being allowed to run in production in a corporate. That is why we plan it transparently from the start.
(02)How do you handle approval processes in corporates?
We know the reality in large organisations: decision papers, security assessments, ROI analyses and data protection impact assessments. We provide the necessary documents and build a proof of value in parallel, so decision-makers see a working solution and not just a concept paper. This shortens the path to approval.
(03)Can you work within IT governance structures?
Yes. We adapt to your IT governance, for example ITIL processes, change advisory boards and documented operating procedures. For us, that means working within your structures: with cleanly documented releases, traceable tests and clear handovers to your operations team, so your IT stays in control at all times.
(04)How do you ensure EU AI Act compliance?
We classify every use case according to the risk levels of the EU AI Act. For high-risk applications we implement risk management, logging, human oversight and transparency obligations and document them in an auditable way. For all other applications we provide the required labelling and clean technical documentation.
(05)Which AI models do you use in large enterprises?
We work model-agnostically with Claude, GPT, Gemini and open-source models such as Llama or Mistral. Corporates often use existing contracts, for example via Azure. For particularly sensitive data we run open-source models in your own infrastructure. An abstraction layer ensures that you can switch models later without rebuilding the application.
(06)How long does an enterprise AI project take?
A proof of value is typically usable after four to eight weeks. A company-wide implementation with integration, security approval and rollout usually takes 12 to 24 weeks, depending on approval processes and the number of business units. We plan in stages so that visible value emerges early and budgets can be released step by step.
Related pages
(04)
Technologies for enterprise AI
(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)
In which industries do corporates use AI?
Automotive
AI along the value chain: quality control, predictive maintenance and supply chain optimisation.
Pharma & life sciences
GxP-compliant AI systems for clinical research, document management and the analysis of study data.
Energy & utilities
AI for grid control, load forecasting and asset management in critical infrastructure.
Telecommunications
AI for network operations, fraud detection and customer service automation.
Retail & e-commerce
AI for demand forecasting, personalisation and assortment planning.
(06)
Example projects
Examples we can build for you
Customer service
Support agent for corporates
An AI agent that automates first responses to customer enquiries – with a company-wide knowledge base and multi-BU access control.
Automated first response · Multilingual · 24/7
Knowledge management
Knowledge management system
A RAG system for large document collections with source verification and access control.
Source-based · Scalable
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Consultation in Berlin
- Founder AI-native since
- 2024
- Phone
- +49 30 96610664
- info [at] contextstudios [dot] ai
Start your enterprise AI transformation
Talk to us about your corporate AI strategy – in a free 30-minute initial 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