The proof-of-concept looked great. Then real users arrived. Hallucinations. Latency spikes. Costs spiraling. The gap between 'AI demo' and 'AI production' is bigger than anyone told you—and your team can't close it.
The RAG demo was impressive. Real-world accuracy hovers around 60%.
Latency that was 'fine in testing' is killing user experience in production.
Inference costs are 10x what you budgeted. Finance is asking questions.
Your team can't debug it when things go wrong—they don't understand the internals.
I build and fix production AI systems. RAG pipelines that actually work. Fine-tuned models that fit your use case. Infrastructure that scales without breaking the budget.
Identify root causes with proper instrumentation. Hallucinations? Retrieval quality? Chunking strategy? Prompt engineering gaps?
Design for production requirements: accuracy, latency, cost, security, and observability
Implementation with proper evaluation frameworks—not vibes-based testing. Measurable quality gates.
Your team learns to operate and improve it. Full documentation, hands-on training, complete handover.
A systematic approach to building AI systems that survive contact with real users. Unlike demo-driven development, this methodology prioritizes accuracy, latency, cost, and maintainability from day one.
You have AI systems that work in demos but fail in production. You need someone who can debug at the infrastructure level, not just prompt engineering tweaks.
It depends on what's broken. Often, significant improvements come from fixing chunking strategies, retrieval logic, or prompt engineering—no rebuild needed. I'll diagnose root causes first and recommend the most efficient path to production-quality accuracy.
We establish evaluation frameworks with ground truth datasets specific to your use case. This includes answer accuracy, retrieval precision/recall, hallucination detection, and latency metrics. You'll have dashboards showing quality over time, not just vibes-based testing.
Prompt engineering first—it's faster and cheaper. Fine-tuning makes sense when you need domain-specific behavior, consistent output formats, or cost optimization at scale. I'll analyze your use case and recommend the approach with the best ROI.
Capability transfer is built into every engagement. Your team participates in implementation, receives hands-on training, and gets complete documentation. The goal is self-sufficiency—not permanent consultant dependency.
Costs vary widely based on volume and architecture: Cloud LLM APIs (GPT-4o) cost ~€100K/month at 10M requests. Self-hosted open-source models (Llama 70B) cost ~€15K/month for equivalent infrastructure. Optimized RAG with caching, query routing, and smaller models for simple queries can reduce costs 60-80% from naive implementations. We design architectures that balance quality, latency, and cost for your specific volume and budget.
Traditional search returns documents—users must read and interpret them. RAG retrieves relevant passages and uses an LLM to synthesize a direct answer, citing sources. This means natural language questions, contextual answers, and the ability to reason across multiple documents. The trade-off: RAG can hallucinate if retrieval quality is poor, which is why production RAG requires careful evaluation, monitoring, and guardrails that search engines don't need.
Yes. Most production AI systems need to integrate with existing tools—CRM, ERP, ticketing, document management. We design integration architectures using APIs, webhooks, and middleware. Common integrations include Salesforce for sales AI, SAP for process automation, ServiceNow for IT support, and SharePoint/Confluence for knowledge management RAG systems.
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