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 — with a focus on security, predictability, and scalability.
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 proper integration, LLM-based systems may:
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 is designed to support:
Data & Knowledge Integration
We connect LLMs to:
Often via:
Governance, Safety & Compliance
Enterprise-oriented LLM integration typically 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 possible integration strategies.
FAQ
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.
We use prompt engineering, guardrails, context constraints, RAG architectures for grounding, confidence thresholds, and fallback logic. We also design system instructions designed to improve factual grounding and domain consistency. Hallucination reduction mechanisms are built into the integration architecture.
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.
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.
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.
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.
LLM integration consulting for companies operating production AI systems. We support organizations with LLM integration, prompt engineering, and AI architecture based on the specific technical and regulatory context of each project. All services are delivered individually and depend on system requirements and constraints.
LLM-based systems are probabilistic by nature. While architectural controls, retrieval mechanisms, and governance significantly improve reliability and contextual grounding, outputs may vary depending on data quality, system configuration, and model behavior. LLM integrations support workflows and decision-making but do not replace human judgment, validation, or responsibility.