How KPMG delivers AI Platform Engineering
KPMG's approach to AI Platform Engineering reflects their broader delivery model: large teams, long timelines, and a scope that expands with the engagement rather than resolving it. Carillion collapse (2018): KPMG audited Carillion for 19 years before its £7B liability implosion — FRC investigation, £14.4M fine
AI Platform Engineering requires a specific kind of engineering precision that generalist delivery models do not produce. The capabilities required — Custom AI/ML system development, Compliance-native architecture, Multi-model orchestration — are not skills that scale with headcount. They require engineers who have delivered these systems in production environments.
How we deliver AI Platform Engineering
Our AI Platform Engineering practice deploys teams with production experience in the specific capabilities this service requires. Our AI teams come domain-qualified. They understand your regulatory landscape before they write their first line of code. Compliance is enforced automatically through ALICE at every commit.
Fixed-price delivery with defined milestones. The first milestone is always a working system component — not a document. The engagement closes with full IP transfer: source code, documentation, and the operational capability for your team to run the system independently.
KPMG vs. The Algorithm
Where AI Platform Engineering matters most
Compliance-Native Architecture Guide
Design principles and a structured checklist for building software that is compliant by default — not compliant by retrofit. For teams building in regulated industries.