Building secure and impactful artificial intelligence and data systems for regulated industries.
I design private and scalable artificial intelligence infrastructure that assists organizations in transitioning from experimentation to dependable business value.
I operate at the intersection of strategy, software engineering, and applied artificial intelligence. My primary focus is assisting teams in migrating from cloud reliant experimentation to robust and governable systems that execute efficiently within internal environments. This is particularly relevant in finance, insurance, and data intensive operations. My objective is to ensure artificial intelligence is practical, secure, and valuable, particularly in environments where privacy, regulatory compliance, and operational reliability are paramount.
Note: Several enterprise systems architected during my tenure with past employers remain proprietary and are accordingly marked as private repositories.
| Domain | Skills & Technologies |
|---|---|
| 🤖 Enterprise AI Architecture | Local Language Model Orchestration, Retrieval Augmented Generation, Agentic Systems (Ollama, LlamaIndex) |
| 💻 Software Engineering | TypeScript, Node.js, Python, React (Next.js/Vite), WebSockets, Application Programming Interfaces |
| ☁️ Data & Infrastructure | Data Centralization, Docker, AWS, Azure, GCP, Nginx, PostgreSQL, Web Workers |
| 📈 Business Value Delivery | Financial Reconciliation, Risk Workflows, Document Processing (OCR), Data Quality |
Automated financial reconciliation combining deterministic matching with local artificial intelligence semantic fallbacks.
- Problem: Manual matching of Bank versus General Ledger entries is prone to error and consumes significant time. Furthermore, financial data is often too sensitive for external cloud language models.
- Approach: Designed a strict two phase matching architecture. The initial phase enforces exact one to one matches and extracts patterns from noisy narratives. The subsequent phase applies a circuit breaker aggregation algorithm for complex matching scenarios. Incorporated a local language model for the semantic matching of ambiguous records to ensure complete data privacy.
Multimodal pipeline automating data extraction, reporting, and risk assessment for insurance claims.
- Problem: Manual processing of handwritten and printed insurance forms is slow, resulting in elevated overhead costs and compliance risks.
- Approach: Constructed an offline system utilizing local vision and reasoning models for document parsing. Implemented role based access control portals to facilitate human oversight for both customers and claims handlers.
Programmable application programming interface and web application for bulk processing, standardizing, and geocoding unstructured addresses.
- Problem: Unstructured Nigerian address data limits operational efficiency and geographical insights.
- Approach: Engineered a hybrid pipeline integrating Google enterprise services with deep Nigerian domain logic. Increased throughput significantly by utilizing a concurrent asynchronous worker pool and an internal memory cache layer.
A desktop application that retrieves, analyzes, and visualizes air quality data from the Astra database or local files.
- Problem: Air quality data was isolated in remote databases, making it difficult for stakeholders to visualize and analyze information promptly.
- Approach: Built an interactive Python graphical user interface connected to a remote Cassandra database. Utilized data manipulation libraries to dynamically query and plot metrics, providing users with immediate visual feedback.
