Stop Demoing. Start Shipping.
Every week on ‘prototyping’ is a week competitors spend capturing market. I build production AI — RAG that answers in under a second, agents customers trust, and LLM integration that survives real traffic.
The AI Engineering Stack
PythonLangChain
PyTorch
Hugging Face
FastAPI
Redis
PostgreSQLPineconeAWS
NVIDIA
Docker
OpenAIPythonLangChainPyTorchHugging FaceFastAPIRedisPostgreSQLPineconeAWSNVIDIADocker
Sound Familiar?
The problems I solve every week.
Your RAG Won’t Scale
Works with 10 docs. Falls apart at 10,000 — hallucinations, latency spikes, no evaluation pipeline.
LLM Integration Is a Maze
Which model? How to cache? What about fallbacks? $5K API bills and still not production-ready.
MLOps Is an Afterthought
Models trained on laptops, deployed by copy-paste, monitored — if at all — by prayer.
Results That Speak
Real metrics from real engagements.
How I Work
From first call to production — no bureaucracy, no hand-offs.
Free audit & scope
We start with a short call. I dig into your current setup, find the real bottleneck, and scope the work with an honest timeline — no commitment, no pitch.
Start within 48 hours
Once scope is agreed I start shipping — flat $49/hr, no retainer, no minimum. You see progress from day one, tracked transparently.
Ship & handoff
Production-grade systems with runbooks, monitoring and knowledge transfer. You own everything and you’re never left stranded.
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What Clients Say
“Raphael rebuilt our entire deploy pipeline in three weeks. We went from dreading releases to shipping daily. Worth every dollar of the $49.”
“Our RAG prototype was stuck for months. He got it to production — fast, accurate, and it held up in front of our investors on demo day.”
“He cut our AWS bill by 40% without breaking a single thing. The engagement paid for itself in the first month.”
Frequently Asked Questions
Hybrid retrieval — dense vectors + sparse BM25 with re-ranking. Chunking tuned to your content, query rewriting for ambiguity, and a real evaluation pipeline before production. Caching, fallbacks and monitoring from day one.
Depends on the use case. Customer-facing → GPT-4o or Claude for quality with a fast fallback. Internal → open-source (Llama 3, Mistral) on your own infra. I build the switching layer so you’re never locked in.
Caching at every level — prompt, response, semantic. Smart model routing sends simple queries to cheaper models. Batch non-real-time work. Most teams cut 50-70% without losing quality.
Ready to fix it?
Book a free 30-minute call. No pitch — just a straight conversation about what you need.
Book Free AI Strategy Call
