You need LLMs in your product but don't know where to start
I help you identify the right LLM use cases, build the integration architecture, and ship AI features your customers actually use — without the hype and without the risk.
Sound familiar?
If any of these hit close to home, you're not alone. This is where most technical leaders get stuck.
You know LLMs could improve your product but the landscape of models, frameworks, and approaches is overwhelming
Your team prototyped something with the OpenAI API but it's unreliable, expensive, and not ready for production
You're worried about hallucinations, data privacy, and vendor lock-in but don't have the expertise to mitigate these risks
Every AI vendor promises magic but you need an independent technical perspective, not another sales pitch
What is LLM Integration?
LLM integration consulting helps businesses evaluate, architect, and deploy large language model capabilities within their existing products and workflows — addressing model selection, prompt engineering, RAG pipelines, fine-tuning decisions, cost optimization, and production reliability concerns with independent, vendor-neutral guidance.
How we work together
A structured process that reduces risk and gives you visibility at every step.
Use Case Mapping
Identify which workflows and features benefit most from LLM integration, and which are better served by traditional approaches. Not everything needs AI.
Architecture Design
Design the integration architecture: model selection, RAG pipeline, prompt engineering strategy, caching, fallback handling, and cost controls.
Implementation
Build the LLM integration into your product with proper error handling, monitoring, and guardrails against hallucination and data leakage.
Optimization
Tune for cost, latency, and quality. Set up evaluation pipelines so you can measure LLM performance objectively and improve over time.
What you get
Concrete deliverables, not vague promises.
LLM features shipping in production with proper monitoring and guardrails
Vendor-neutral architecture avoiding lock-in to any single model provider
RAG pipeline for domain-specific accuracy, reducing hallucination rates
Cost-optimized deployment with caching, model routing, and fallback strategies
Proof it works
Real projects where this approach delivered results.
Knowledge Graph for Global HealthTech Product KB
Global HealthTech Company
14,800+ nodes and 24,900+ relationships mapped in the knowledge graph
Technologies I work with
Typical investment
Depends on scope, timeline, and complexity. Let's discuss your specific situation.
EUR 15,000 - 50,000
per project
Common questions
Ready to ship AI features?
Let's have a 30-minute conversation about your challenge. No pitch, no pressure — just an honest assessment of whether this is the right approach for you.
Let's Talk on LinkedIn