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Enterprise AI That Ships — Not Just Demos
We architect AI systems thatreason, act, and self-correct.

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Ship AI that holds up in production.

Book a 30-minute architecture review. We'll audit your data layer, identify the highest-ROI AI surface, and leave you with a concrete plan.

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LX
What's driving Q3 churn in enterprise accounts?

Three factors across 412 tickets:

  • pricing fit
  • onboarding gaps
  • retrieval latency
Verified by RAG · 4 sources cited

p95 Latency

0.9s · within SLA

Source Verified

4 docs cited

RAG pipelines, deployed.

By the numbers

Proof over promises. Every system we ship is scored the way your stakeholders will audit it — retrieval latency, source fidelity, and uptime. The numbers behind the hero.

0.9s

p95 Retrieval Latency

4

Sources Cited per Answer

250+

RAG Pipelines in Production

99.9%

Uptime Guarantee

What we ship

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.

01

Enterprise RAG & AI Auditing

~8 weeks

Evidence-gated retrieval with zero-hallucination guarantees.

Multi-agent LangGraph pipelines that cross-reference documents line-by-line. Our adversarial prosecutor node forces cyclical retries if faithfulness drops below 90% — delivering 99.9% verifiable output.

  • Hybrid retrieval architecture (pgvector + semantic)
  • Adversarial verification node with RAGAS telemetry
  • CI regression hook for eval-first deployment
02

Agentic Workflows

~8 weeks

Autonomous multi-agent systems with self-correction loops.

From research agents that write and design content in under 60 seconds to structured extraction agents that parse raw text into validated JSON — we build LangGraph-orchestrated systems with critic-agent feedback loops.

  • Agent orchestration design with tool registry
  • Critic-agent feedback loop for self-correction
  • Observability dashboard with trace-based monitoring
03

MLOps & Self-Healing Infra

~6 weeks

Physics-informed monitoring that detects drift before it breaks.

Z-score thresholds (>2.5σ) for anomaly detection, automated RAG-powered root cause analysis, and containerized FastAPI microservices with lazy-loaded ML singletons — sub-second cold boots, 2.5GB container savings.

  • Drift detection with physics-informed thresholds
  • Automated RAG investigation agent for root cause analysis
  • Optimized container orchestration + deployment topology
04

AI Evaluation & Testing

~5 weeks

Risk-aware automated QA that finds edge cases humans miss.

Gemini 2.5-powered test generation using Z-Score and Isolation Forest for risk prioritization. We build frameworks that evaluate chain-of-thought reasoning and validate model outputs against golden benchmarks.

  • Automated test generation with risk-aware prioritization
  • Model evaluation rubric with golden dataset curation
  • CI/CD integration for pre-deploy regression testing
05

Specialized AI Systems

~10 weeks

From deepfake detection to quant arbitrage — purpose-built AI.

Physics-informed CNNs for audio forensics, XGBoost models for prediction market arbitrage, and unbiased LLM-powered recruitment screening. We architect niche AI that solves problems general-purpose models can't touch.

  • Domain-specific model architecture
  • Training pipeline with evaluation benchmarks
  • API wrapping for production deployment
How we work

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 the 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.

Engineered Intelligence

Deployment-ready AI architectures.

From autonomous agents to high-frequency quant engines — every project is open-source and production-tested.

AI / Content Automation

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.

<60sEnd-to-end latency
5-node pipelineAgent nodes
6GitHub stars
Legal / FinTech

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.

99.9%Citation accuracy
2.5GBContainer savings
<1sCold boot time
MLOps

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.

2.5σDrift threshold
<500msResponse time
100%Automated RCA
Quant / Trading

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.

5,000Training scenarios
+$18,211Simulated PnL
XGBoostModel
Cybersecurity

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.

100%Detection accuracy
ResNet CNNArchitecture
Mel-spectrogramsDomain
Field Notes

Architecture decisions, physics-informed MLOps, and agentic patterns.

Deep technical writing on what I build and why — no listicles, no half-baked takes.

Engineering

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.

Owadokun Tosin Tobi12 min read
Engineering

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.

Owadokun Tosin Tobi8 min read
Research

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.

Owadokun Tosin Tobi9 min read
Architecture

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.

Owadokun Tosin Tobi10 min read
Who picks up the phone

Built by Owadokun Tosin Tobi.

Owadokun Tosin Tobi

Owadokun Tosin Tobi

Founder & Lead AI Engineer

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 ship

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.

Discovery & Eval Audit
Reference Implementation
Production Handoff
Open Knowledge Transfer