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AI system integration

LLM Integration

LLM integration means embedding language models such as Claude, GPT or Llama into your existing systems so that they work securely with your data and processes. Context Studios, an AI-native development studio in Berlin, develops the API gateways, middleware and data connections for this – for SAP, Salesforce, your own microservices and legacy systems.

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Canal lock with a teal-painted steel gate between stone walls under an overcast sky – a visual metaphor for the controlled connection of language modelsAI-generated image
SAP, Salesforce & custom APIsMulti-provider with fallbackGoal: first integration in about 3 weeks
  1. Workshop
  2. Setup
  3. Sprint
  4. Build & Support

Fixed price after scoping · proposal within 48 h

Last updated:

(01)

What is LLM integration?

AI technology

LLM integration is the embedding of large language models such as GPT, Claude or Llama into existing enterprise systems, workflows and data architectures. Beyond the API call, it covers authentication, data preparation, context enrichment, error handling, caching, rate limiting and monitoring – so the model works reliably as part of your system landscape.

Specialisation
API integration, middleware, event-driven LLM connection
Technologies
REST/GraphQL APIs, webhooks, message queues, gRPC
Target group
Companies with existing IT infrastructure and integration needs
Project duration
Typically 2–8 weeks for standard integrations
Compliance
GDPR, ISO 27001, VPN/private endpoints

AI integrationLLM developmentAI API developmentRAG developmentAI workflow automation

(02)

What does connecting language models involve?

Robust interfaces between language models and your enterprise IT

(01)

API gateway & routing

Development of a central API gateway for your language models with intelligent routing, load balancing between providers and automatic fallback in case of outages – for high availability.

(02)

Data connection & context enrichment

Integration of your CRM, ERP and document management systems as data sources — with real-time context enrichment and structured handover to the model.

(03)

Event-driven LLM pipelines

Building asynchronous processing pipelines with message queues that integrate LLM calls into your existing event-driven architectures – scaling with your request volume.

(04)

Multi-provider orchestration

Seamless switching between OpenAI, Anthropic, Google and open-source models based on cost, latency and task complexity — with a unified API for your application.

(05)

Monitoring & observability

End-to-end monitoring of all LLM interactions with latency tracking, token consumption analysis, error rate alerts and detailed tracing — for full transparency in production.

(06)

Security & access control

Implementation of OAuth 2.0, API key management, per-user rate limiting and encrypted data transfer — so that your integration meets enterprise security standards.

(03)

How does connecting a language model work?

  1. (01)

    Consultation

    Free 30-minute initial call via video. We get to know your system landscape, identify suitable use cases for language models and give you a first assessment of feasibility and timeline.

    Day 1
  2. (02)

    Proposal & planning

    You receive a written proposal with scope, timeline and fixed price – including an interface overview and a security concept.

    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 and 30 days of free bug fixing from final delivery. Maintenance and further development by agreement.

    Week 4+

Frequently asked questions about connecting language models

(01)Which systems can be connected to an LLM?
Basically any system with an interface: ERP systems such as SAP, CRM platforms such as Salesforce or HubSpot, document management, databases, email servers, ticketing systems and custom web applications. Legacy systems without a modern API can also be connected via adapters, database access or middleware. What matters is which data the model may read and which actions it may trigger.
(02)How long does a typical integration project take?
A simple connection with one endpoint can typically be implemented in 1–2 weeks. A standard integration with data connection and monitoring usually takes 3–6 weeks. Complex enterprise integrations with several source systems, custom middleware and security audits typically take 6–12 weeks. We set the exact timeline after scoping.
(03)How do you safeguard the availability of the integration?
We implement multi-provider failover, such as switching automatically from OpenAI to Anthropic during outages, caching for recurring requests, circuit breakers for overload situations and health checks with automatic alerts. We also define how your application behaves when no model responds – for example with queues or clear feedback to users.
(04)Can different LLM providers be used at the same time?
Yes, that is even recommended. We develop a unified abstraction layer that chooses the right model for each requirement – for example GPT for fast standard tasks, Claude for long documents and Llama for data-sensitive processing in your own data centre. This lets you optimise quality, cost and data protection per use case and stay independent of any single provider.
(05)How is sensitive company data protected during integration?
Through several layers of security: detection and masking of personal data before the API call, encrypted transfer via TLS 1.3, private endpoints where available, audit logging of all data flows and optional on-premise processing with local models for highly sensitive information. Together with your data protection officer, we define which data the model may see at all.
(06)What happens if an LLM provider changes its API?
Our abstraction layer protects your application against breaking changes. We track API changes from the providers, test new model versions in staging environments with your test cases and adapt the middleware before old interfaces are switched off. Under an agreed maintenance arrangement we handle this on an ongoing basis; without one, we document the steps so your team can carry them out itself.
(07)What are the running costs?
Running costs consist of infrastructure such as the API gateway, monitoring and caching plus the models' API costs; both depend on usage volume, and we quantify them concretely during scoping. Caching and intelligent routing can reduce API costs noticeably. For the integration itself: fixed price after scoping, proposal within 48 hours.
(08)Can existing chatbot solutions be extended with LLM capabilities?
Yes. We can extend existing rule-based chatbots with LLM capabilities without replacing the whole system. The language model then handles open, complex requests, while the existing bot continues to process structured workflows such as status queries. This keeps proven processes stable while you extend the range of functions step by step.
(09)What monitoring options are available after integration?
We provide dashboards for latency per request, token consumption and costs, error rates and retry statistics, model performance and usage patterns. Automatic alerts warn of anomalies, and detailed tracing allows individual requests to be analysed down to prompt level. We set up tools such as Langfuse or Grafana so that your team can use them independently.
(10)Do you also support integrating open-source models?
Yes. We integrate open-source models such as Llama or Mistral on your own infrastructure, in Kubernetes clusters or via managed services such as AWS Bedrock. The abstraction layer treats open-source and commercial models the same way, so switching later is possible at any time. This is particularly relevant when data must not leave your network.
(04)

Technologies for integration

(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 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)

Fields of application by industry

Enterprise software

Connecting LLMs to SAP, Salesforce, ServiceNow and other enterprise platforms — via standardised connectors and custom APIs for end-to-end business process automation.

Fintech & banking

GDPR-compliant LLM connection to core banking systems, trading platforms and compliance tools — with private endpoints and full audit trail integration.

E-commerce

Connecting LLMs to shop systems such as Shopify, Magento or custom platforms for intelligent product search, recommendations and automated customer interaction in real time.

Logistics & supply chain

Embedding language models in warehouse management and TMS systems for automated document processing, supplier communication and route optimisation.

Healthcare IT

Integrating LLMs into hospital information systems and practice software — in compliance with GDPR data protection requirements and medical documentation standards.

Insurance

Connecting language models to policy administration systems and claims platforms for automated claims intake, policy analysis and intelligent customer correspondence.

(06)

Example projects

Examples we can build for you

Sales

LLM connection to the CRM

A language model that summarises emails and call notes, creates tasks in the CRM and suggests draft replies – connected via the Salesforce or HubSpot API.

CRM integration · Draft replies · Approval by sales
Knowledge management

RAG-based document system

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

Source-based answers · Fast search · Scalable
IT platform

Multi-provider gateway

A central gateway through which all internal applications use language models – with routing, fallback, cost control and an audit log in one place.

Unified API · Automatic fallback · Cost control
(07)

Connecting language models — consultation in Berlin

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

Integrate LLMs into your systems

Connect modern language models with your existing IT infrastructure. Discuss your system landscape in a 30-minute call directly with the founder.