I build production-grade AI systems that organizations can trust.

My work goes beyond prototypes and demos — I focus on the engineering discipline required to make AI reliable in real-world environments: evaluation systems, calibrated confidence, self-correction loops, safety guardrails, and cost-aware architectures.

Most AI systems don’t fail loudly; they fail silently and convincingly.

A model works on five examples, hallucinates on the sixth, and there’s no infrastructure to catch it. I design systems for that reality.

• Evaluation is a system → regression testing, golden datasets, LLM-as-judge
• Confidence is engineered → calibrated scoring, uncertainty-aware escalation
• Safety is layered → deterministic checks + semantic review
• Cost matters → hybrid routing (frontier + local models)
• Impact is measured → A/B testing, calibration curves, failure analysis

→ https://github.com/Adeliyio/ai-system-debugger

LLM systems don’t fail like traditional software — they fail silently with plausible but incorrect outputs.

• Ensemble failure detection (LLM-as-judge + embeddings + rules)
• Root cause analysis (retrieval, prompt, model, context)
• Self-healing with regression-tested fixes
• Hybrid routing (GPT-4o + Llama via Ollama)
• Meta-evaluation of evaluator reliability

→ https://github.com/Adeliyio/SEC-filings-knowledge-copilot

A hallucinated financial figure is worse than no answer — correctness must be provable.

• Multi-agent LangGraph reasoning pipeline
• Claim-level grounding + hallucination correction
• RAGAS + regression-based evaluation
• Transparent confidence + citations
• Fully local deployment (Ollama)

→ https://github.com/Adeliyio/customer-support-copilot

The goal isn’t generating replies — it’s ensuring responses are safe to send.

• Agentic classification with deterministic fallback
• Hybrid retrieval (dense + BM25 + reranking)
• Dual-layer safety (rules + LLM review)
• Confidence-aware escalation
• Five-layer evaluation pipeline

→ https://github.com/Adeliyio/deal-intelligence-for-sales-teams

The real question isn’t scoring deals — it’s whether AI measurably improves sales decisions.

• Calibrated win probabilities (Platt scaling)
• Temporal feature engineering (deal behavior tracking)
• Evidence-grounded risk explanations
• Counterfactual simulations for strategy decisions
• A/B tested agent impact

LLM & GenAI OpenAI · Anthropic Claude · LangChain · LangGraph · LlamaIndex · Ollama

Retrieval & Search FAISS · Pinecone · ChromaDB · BM25 · Cross-encoders

Evaluation Systems RAGAS · LLM-as-judge · Golden datasets · Custom eval harnesses

ML / Modeling PyTorch · TensorFlow · scikit-learn · XGBoost

MLOps / LLMOps MLflow · DVC · GitHub Actions · Docker

Deployment AWS · FastAPI · Streamlit

• End-to-end ML pipelines (CI/CD + experiment tracking)
• Model versioning (DVC + MLflow)
• Real-time computer vision systems (YOLOv8)
• Scalable inference APIs (FastAPI + AWS)

• Why Most RAG Systems Fail in Production (And How to Fix Them)
• Continuous Integration for Data Science: Automating Model Building Pipelines
• Version Control for Machine Learning Models: Best Practices and Tools
• Automated Model Testing and Monitoring: The Bedrock of Startup MLOps
• Taming Data and Model Drift in Startup MLOps
• Building Scalable Machine Learning Pipelines in Startup Environments

→ https://medium.com/@tommyadeliyi

If you're building AI systems that need to be:

• reliable in production
• measurable and testable
• safe under real-world conditions

I’m always open to conversations around high-impact AI systems.