In 2025, AI Wild West: scattered tools, hacked-together prompts, sensitive data sent to third-party APIs. In 2026, companies with proper AI infrastructure — Graph RAG, sovereign LLMs, stateful multi-agent orchestration — are pulling ahead. AIveloLabs builds yours.
The gap between a company that "does AI" and one that gains a measurable competitive edge comes down to infrastructure, not tools.
Sensitive data on ChatGPT — Your contracts, IP and HR data are training OpenAI's models right now.
Naive RAG that hallucinates — Fixed-size chunking, no re-ranking, no awareness of relationships between documents.
Uncontrolled API spend — Your LLM costs triple every quarter. 60% of tokens are wasted on redundant calls.
Agents without memory or planning — LLM scripts dressed up as "agents," incapable of multi-step reasoning or self-correction.
Invisible ROI — AI "does stuff" but nobody can measure what it actually delivers.
On-premise LLMs (vLLM, TensorRT-LLM) — Llama 3.3 / Mistral on your servers. Zero data leaves your network. GDPR-native.
Graph RAG + hybrid search — Knowledge graph (Neo4j) + vector store (Qdrant). Multi-hop reasoning. +35% precision vs. naive RAG.
LLM cost engineering — LiteLLM routing, Redis semantic caching, model distillation. −30% to −60% on your API bill.
Stateful agents (LangGraph) — Persistent memory, dynamic planning, self-correction loops. Agents that actually finish what they start.
Measurable ROI from day 7 — Metrics defined before deployment, performance dashboards, 30-day ROI report.
Techniques that make the difference between a demo that impresses and a system that runs in production.
Combines knowledge graph (Neo4j) with hybrid vector search (BM25 + dense). The model traverses entity relationships — not just the nearest chunks.
The agent decides when to retrieve, generates hypothetical documents (HyDE), filters with Cohere Rerank. +35–50% precision vs. naive fixed-chunking pipelines.
Deploy Llama 3.3 70B, Mistral Large, DeepSeek-R1 on your infrastructure. GPTQ/AWQ quantization, served via vLLM or TensorRT-LLM, OpenAI-compatible API.
LangGraph agents with persistent state (Redis/Postgres), dynamic planning, MCP (Model Context Protocol) for real-time connection to your APIs and databases.
Intelligent LiteLLM routing (right model for each task), Redis semantic caching (zero API call for similar queries), context compression, model distillation.
Domain-specialized models trained on your corpus. RLHF alignment via GRPO and DPO for precise, reproducible behavior — beyond the reach of generic models.
Holds a degree in artificial intelligence, with research experience in advanced LLM architectures and RAG pipeline optimization. I follow publications from DeepMind, Meta FAIR and Mistral Research and implement techniques directly from papers into production — not Jupyter notebook demos. Prior to AIveloLabs, I founded and exited an e-commerce brand generating several million euros in revenue. I understand the business context behind every technical decision. Based in Nice, available throughout France and remote.
Every project starts with a 48h diagnostic. Every architecture is custom — no off-the-shelf templates.
A network of 38 franchisees drowning in repetitive questions — procedures, supplier schedules, opening checklists. The support team answered the same things every week.
8-partner law firm in Paris with strict GDPR compliance — client case files could not be sent to cloud APIs. Required a high-performance LLM specialized on French legal corpus.
Series A SaaS spending €8,400/month on OpenAI API with uncontrolled growth. GPT-4o used indiscriminately across all requests, including trivial classification tasks.
4-person HR team manually cross-referencing CVs, LinkedIn, sector references and job criteria before every first call. 3 hours of work per candidate before a single interview.
Analysis of your workflows, available data and tech stack. Deliverable: Automation Heatmap with estimated ROI per use case and technical complexity rating.
Custom infrastructure designed and deployed. Integrated with your existing tools, documented, tested. Zero stack changes required on your end.
Your teams use the system from Day 3. Performance dashboards, actual gains measured, adjustments over 30 days, quantified ROI report.
We analyze your processes, available data and tech stack. You get a concrete technical action plan with estimated ROI per use case.