Document Intelligence Pipeline
Extracts structured information from messy corporate documents. Not a demo — ran in production, used by people who didn't know it was AI.
I'm a data science and AI professional with 10+ years of experience spanning the evolution of the field, from debugging Python on Stack Overflow to harnessing cutting-edge coding agents like Claude and Codex. More recently, my focus has been on enterprise AI adoption, helping organisations go from pilot to production with governance frameworks, safety guardrails, and pipelines that don't fall apart at scale. My PhD simulated 240,000 interacting agents on HPC clusters, and that systems thinking still drives how I design agentic AI, verification loops, and trustworthy automation. I help organisations turn AI from promising pilots into governed, operational capability. The shift AI is bringing is nothing short of transformative, and I want to be where it's built, not where it's watched.
Complex systems, real stakes, and getting AI to work inside organisations — not just in demos.
Ran AI/ML projects from idea through production, including an LLM document pipeline (Claude, OpenAI) that hit 99% extraction accuracy on messy, real-world files.
Led cross-functional teams and mentored junior data scientists through production delivery.
Built the MLOps layer — CI/CD with GitHub Actions, monitoring, model governance — so nothing stayed stuck in a notebook.
Technical implementation of an LSTM-based architecture on well sensor data to support detection of well integrity issues.
Owned data curation pipelines and quality frameworks — because models are only as good as what goes in.
Led EU AI Act and NIST AI RMF alignment across 120+ staff. Not a checkbox exercise — actual workflow redesign, risk classification, and safety guardrails.
Large socio-economic datasets, cross-institutional research, policy-facing outputs.
Python, SQL, Postgres, GIS. Worked across time zones and disciplines to support food security modelling.
Built agent-based models with 240,000 agents on HPC clusters to study how financial crises spread.
Published on systemic risk and contagion — peer-reviewed, cited, still relevant.
Taught econometrics and programming. Supervised student projects.
Market studies for South African energy and finance. Budget management, profitability analysis, client delivery.
Dashboards, workflows, websites, internal systems,I make things and keep improving them.
EU AI Act, NIST AI RMF, safety guardrails, data curation. I think about what goes into a system before asking what comes out.
My economics PhD trained me to see interactions, incentives, and emergent behaviour. That still shapes everything.
I take ownership, make decisions, and move work forward. I don't wait to be told what to do next.
Extracts structured information from messy corporate documents. Not a demo — ran in production, used by people who didn't know it was AI.
Agent-based model with 240,000 agents simulating how social learning and contagion interact in complex networks. Published in JEBO.
LSTM architecture on well sensor data to support detection of well integrity issues. Built on real operational data, presented at ADIPEC/SPE.
Comparison site for AI model pricing and capabilities. Automated weekly refresh via GitHub Actions, source-linked.
A multilingual information site for Cape Town — safety data, neighbourhood guides, local knowledge. Thousands of monthly readers.
Tracking and visualising economic and market bubble indicators — turning scattered signals into something you can watch over time.