Finance meets AI in production
Domain expertise is the critical differentiator when working with AI. The tools accelerate implementation, but finance judgement drives architecture, quality, and business logic. Three projects show what that looks like in practice.
CFO AI Readiness Assessment
A structured framework that helps CFOs assess their organisation's readiness for AI across five dimensions: Data Foundations, Process Standardisation, Governance & Controls, Talent & Operating Model, and Value Realisation Discipline. Each dimension is scored against four maturity levels to produce a clear picture of where the organisation stands today.
The assessment translates into a sequenced three-horizon roadmap: strengthening foundations (0–6 months), targeted AI applications (6–18 months), and intelligent operations (18–36 months). Each horizon delivers standalone value while building readiness for the next — ensuring CFOs can show progress to the board at every stage, not just after a multi-year programme.
Designed for finance leaders in manufacturing, consumer goods, and multi-site operations who are under pressure to define an AI strategy but need to build the foundations first. The framework is deliberately framed from the CFO's perspective — not the IT department's.
Supply Chain Finance Platform
Built a production-grade analytical platform from raw transactional data — including multi-dimensional P&L, PVM revenue bridges, working capital analysis, and forecast accuracy tracking. Complete with automated executive report generation.
The platform features an 8-component Price-Volume-Mix revenue decomposition with FX isolation, zero-residual validation, and four comparison bases (MoM, vs Budget, vs Prior Year, vs Forecast). Multi-dimensional P&L views span regional, country, product, and channel dimensions, alongside balance sheet, cash flow, working capital analysis, gross-to-net waterfalls, forecast accuracy metrics, and expense ratio tracking — with AI-generated executive commentary on every page.
What would typically require a cross-functional team of analysts, data engineers, and BI developers over several months was delivered iteratively in weeks by combining deep finance expertise with AI tools.
Product Cost Intelligence Platform
A multi-tenant SaaS platform that forecasts product costs using machine learning, monitors BOM-level cost movements across commodities, labour, and FX, and separates realised margin impact from unrealised exposure — enabling proactive procurement decisions rather than reactive cost absorption.
The platform combines Snowflake analytics with LightGBM forecasting models, a contract gating engine that models supplier agreement mechanics (caps, floors, index clauses, reset triggers), and a shock impact analyser for scenario modelling geopolitical and market disruptions. A monthly automated learning cycle refines assumptions using Bayesian updates against actual purchase data. Cortex AI ingests supplier PDF agreements and extracts 17 structured fields with zero manual data entry.
Built as a production-grade application with Next.js frontend, FastAPI backend, Auth0 enterprise SSO, and full AWS infrastructure — demonstrating that deep domain expertise combined with AI can deliver enterprise software at a fraction of traditional development cost and timeline.
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