LLM Integration Consulting
Expert consulting for integrating Large Language Models into your products and workflows
Large Language Models are not features. They are infrastructure components that must be integrated carefully — or they become expensive, unreliable, and risky.
H-Studio provides LLM Integration Consulting for companies that want to embed Large Language Models into real products, internal systems, and business workflows — securely, predictably, and at scale.
We focus on architecture, governance, and production readiness — not demos.
What LLM Integration Really Means
Integrating an LLM is not just "calling an API".
Real integration requires:
Without this, LLMs:
What We Help You Integrate
Product & Platform Use Cases
Internal & Operational Use Cases
Our LLM Integration Approach
Architecture & Use-Case Validation
We define:
LLMs must fit your system — not the other way around.
Prompt & Context Engineering
We design:
This ensures:
Data & Knowledge Integration
We connect LLMs to:
Often via:
Governance, Safety & Compliance
Enterprise-ready LLM integration includes:
Production Readiness
We help you with:
Typical Problems We Solve
Who This Service Is For
Related AI Services
Start with an LLM Architecture Review
We assess: feasibility, risks, architecture, and integration strategy.
FAQ
What's the difference between LLM integration and just using an API?
Using an API is making a call. LLM integration means embedding LLMs as system components with proper architecture, control, governance, and production readiness. Integration includes prompt engineering, context management, data boundaries, fallback logic, monitoring, and compliance — not just API calls.
How do you prevent LLMs from hallucinating?
We use prompt engineering, guardrails, context constraints, RAG architectures for grounding, confidence thresholds, and fallback logic. We also design system instructions that enforce factual accuracy and domain consistency. Hallucination prevention is built into the integration architecture.
Can you integrate LLMs with our existing systems?
Yes — we integrate LLMs with databases, APIs, CRM/ERP systems, document stores, knowledge bases, and internal services. We use RAG architectures, controlled retrieval, and role-based access to connect LLMs to your existing infrastructure securely.
How do you handle GDPR and data privacy?
We implement data isolation, access control, logging, data minimization, and GDPR-aware data handling. We use EU-based infrastructure where required, ensure data boundaries are respected, and provide audit trails. All LLM integrations are designed with compliance from the start.
How long does LLM integration take?
A basic LLM integration (architecture + prompt engineering + basic governance) typically takes 4-8 weeks. Complex integrations with multiple systems, extensive RAG architectures, and enterprise governance can take 12-20 weeks. We start with an architecture review to define scope and timeline.
Can you help us avoid vendor lock-in?
Yes — we design vendor abstraction layers, use standard interfaces, and implement fallback strategies that allow switching between LLM providers (OpenAI, Anthropic, local models) without rewriting your integration. This gives you flexibility and cost control.
Related Services
We provide LLM integration consulting services for businesses across Germany. Our Berlin-based team specializes in Large Language Model integration, LLM architecture, prompt engineering, RAG systems, enterprise AI integration, and production-ready LLM implementations.