Innovate. Create. Excel Intelligently.
We architect Enterprise RAG Pipelines and Edge AI solutions that turn data into decision-making power — engineered by a physicist who builds systems that reason, act, and self-correct.
Five practices. One engineering culture.
Pick a single engagement or compose them into a quarterly program — every effort ends with your team owning the system.
From discovery to handoff, in four phases.
Discovery & Eval Audit
Day 1. We map your stack, audit existing evals (or build an emergency golden set), and write down exactly what we will ship and what we won’t.
Reference Implementation
Weeks 1-3. A working reference for the chosen practice area — RAG, eval harness, agent scaffold, MLOps topology, or strategic roadmap. Evaluated daily against golden set.
Production Handoff
Weeks 4-6. We pair with your engineers to land the system in production with on-call runbooks, dashboards, and a regression CI hook.
Open Knowledge Transfer
Throughout. Every decision is documented in your wiki. We hand off ownership, then leave.
Deployment-ready AI architectures.
From autonomous agents to high-frequency quant engines — every project is open-source and production-tested.
NewsAgent Pro: autonomous AI newsroom in 60 seconds
Built a fully agentic LangGraph workflow that researches trending stories via NewsAPI + Tavily, drafts articles with Llama 3.3, designs visuals with Flux.1, and enforces quality through a self-correcting Critic Agent loop — all end-to-end in under 60 seconds.
Axiom Engine V4: zero-hallucination document auditing
Engineered a deterministic multi-agent LangGraph circuit with an adversarial prosecutor node. Uses MCP Protocol to bridge local IDEs to a FastAPI cloud backend. RAGAS Faithfulness drops trigger cyclical retries — achieving 99.9% verifiable output on SEC filings and legal MSAs.
Sentinel: self-healing MLOps with physics-informed drift detection
A FastAPI microservice using physics-informed Z-score thresholds (>2.5σ) to detect data drift in production ML pipelines. Integrates an automated RAG agent powered by Gemini 2.5 for root cause analysis on error logs — no human escalation needed.
PolyMind: AI-gated prediction market arbitrage
Trained an XGBoost classifier on 5,000 trading scenarios to predict trade fill probability in volatile prediction markets. Avoids liquidity traps by gating every trade through confidence thresholds — simulated PnL of +$18,211.
Spectre: physics-informed deepfake audio detection
PyTorch ResNet-style CNN trained on Mel-spectrograms to detect synthetic audio artifacts and GAN-based deepfakes via real-time spectral analysis. Achieves 100% detection accuracy on benchmark test sets.
Architecture decisions, physics-informed MLOps, and agentic patterns.
Deep technical writing on what I build and why — no listicles, no half-baked takes.
The Architecture of Axiom Engine: Building a Zero-Hallucination RAG Pipeline
A deep dive into the 4-node LangGraph circuit powering Axiom Engine V4 — how the Librarian, Editor, Architect, and Prosecutor nodes collaborate to achieve 99.9% verifiable output with adversarial self-correction.
Physics-Informed MLOps: Using Z-Scores to Detect Drift Before It Breaks Prod
Why I use 2.5σ thresholds instead of arbitrary alert rules. A practical guide to building self-healing monitoring with physics-grounded anomaly detection and automated RAG-powered root cause analysis.
Bridging IDEs to Cloud AI with the Model Context Protocol
How MCP lets Claude and Cursor talk directly to a FastAPI cloud backend — enabling secure, context-aware AI architectures without fragile prompt engineering or manual context stuffing.
Why Your AI Agent Needs a Critic: Self-Correction Loops in Production
Practical patterns for building agentic workflows that don't silently fail. From NewsAgent Pro's 5-node pipeline to Axiom's adversarial prosecutor — how to bake verification into your LangGraph architecture.
Built by Owadokun Tosin Tobi.
Physics undergraduate turned AI Architect. I bridge theoretical physics and production MLOps — building deterministic multi-agent LangGraph systems, physics-informed anomaly detection, and enterprise RAG pipelines that don't hallucinate. My work spans autonomous content agents (NewsAgent Pro), deepfake audio detection (Spectre), quant arbitrage engines (PolyMind), and self-healing MLOps (Sentinel). I don't just use AI — I architect the intelligence behind it.
Read the full story →Let's Architect Your Intelligence.
Ready to move beyond basic automation? Whether you need an Enterprise RAG pipeline, a self-healing agentic workflow, or a specialized AI System — I'm ready to engineer the solution.