AI products built by an engineer
who's shipped in production
We at Empower AI Labs bring 15 years of Fortune 500 infrastructure experience. Now building AI products, GPU infrastructure, and open-source tools. No theory. Real systems.
Products
Everything we build ships to production. Real users. Real infrastructure. Real code.
LawScout AI
AI-powered legal research platform
276,970+ legal document vectors across federal case law and CUAD contracts. Hybrid search with semantic embeddings + BM25 + cross-encoder reranking. AI answers with legal citations in under 2 seconds.
GPU Infrastructure Lab
Bare-metal AI infrastructure on a Dell R640 with Tesla T4 GPU. Deployed and benchmarked production inference engines, monitoring, and HPC job scheduling.
vLLM benchmarked at 5.2 req/s · 260 tokens/s
SGLang & TensorRT-LLM hardware analysis
DCGM + Prometheus + Grafana monitoring
Slurm GPU scheduling · NCCL benchmarks
FastAPI gateway with auth & rate limiting
LangChain RAG pipeline with FAISS
yt-notes
CLI tool that downloads YouTube videos and auto-generates structured Markdown notes with chapter parsing, TOC, and clickable timestamps.
pip install git+https://github.com/iminierai-aig/yt-notes.git
Lab Results
Real benchmarks from real hardware. No cloud credits. No simulated environments. A Dell R640 with a Tesla T4 in my office.
vLLM on Kubernetes
Deployed vLLM inference server on K3s with NVIDIA device plugin. Serving microsoft/phi-2 (2.7B parameters) with OpenAI-compatible API.
Benchmarks
5.2 requests/second at concurrency 10. 260 tokens/second throughput. ~1.87s average latency — consistent under load thanks to PagedAttention.
Inference Engine Comparison
Tested vLLM, SGLang, and TensorRT-LLM. SGLang and TensorRT-LLM require sm80+ GPUs (A100/H100). vLLM has the broadest hardware compatibility. Documented the trade-offs so you don't have to.
GPU Monitoring
DCGM Exporter → Prometheus → Grafana. Real-time dashboards for GPU utilization, temperature, memory, and power draw. Production-grade observability.
RAG Pipeline
LangChain + FAISS + vLLM. Document ingestion, chunking, embedding, semantic retrieval, and LLM-powered response generation. End-to-end.
HPC & Autoscaling
Slurm configured with GPU GRES scheduling. NCCL collective operation benchmarks (~121 GB/s). Kubernetes HPA for inference workload scaling.
Stack
The tools we use to build and ship.
AI / ML
- LLMs (Gemini, Prompt Eng.)
- RAG Architecture
- Vector Databases (Qdrant)
- LangChain · FAISS
- vLLM · SGLang · TensorRT-LLM
- Embeddings (MiniLM-L6-v2)
Infrastructure
- Kubernetes (CKA Certified)
- Docker & CI/CD
- AWS, GCP, Render
- Cloudflare CDN
- NVIDIA GPU (T4, A100)
- DCGM · Prometheus · Grafana
Languages
- Python (FastAPI, Flask)
- Next.js · React
- Tailwind CSS
- Bash / Shell
Pre-Sales & Consulting
- Solution Architecture
- Technical Demos & PoCs
- Executive Briefings
- Customer Enablement
About
We at Empower AI Labs are AI Solutions Engineers. Not researchers. Not prompt influencers. Engineers who build AI products and ship them to production.
Our team brings 15 years at Dell EMC as Senior Principal Engineers, designing and delivering enterprise infrastructure solutions for Fortune 500 clients. We led the technical PoC strategy that drove revenue from $5M to $160M across 3 major accounts. Hundreds of executive briefings. Hundreds of technical demos. The team that builds the solution AND sells it.
Now we build AI products — RAG applications, inference infrastructure, GPU monitoring systems, and open-source tools — from a bare-metal lab in South Florida.
Certifications
- NVIDIA Certified Associate: AI Infrastructure & Operations
- Certified Kubernetes Administrator (CKA) — CNCF
- Red Hat Certified Engineer (RHCE)
- Kubernetes Fundamentals — The Linux Foundation