Most organisations do not have a clear picture of what their technology spend is buying them. Licences that nobody uses. Cloud that nobody right-sized. Contracts that renew on autopilot. We build that picture fast, quantify the saving, and give you the evidence to act on it.
Cost optimisation as a one-time exercise is the wrong approach. The right question is which costs are buying you outcomes and which are not. Our AI-led audit catalogues the spend, maps it against actual usage and business outcomes, and surfaces the gap.
Our platform pulls every licence, every cloud subscription, and every supplier contract into one source of truth. Usage data is mapped against entitlement. The result is a complete waste register, structured and prioritised before a consultant has written a single slide.
Senior consultants then test every finding. Is the licence actually unused, or is it carrying a dependency the data cannot see? Is the cloud spend optimisable, or is it the foundation of a value lever? The AI builds the picture; the partners apply the judgement before anything goes near a budget conversation.
On Project Manhattan, that approach identified approximately £400k of Autodesk licence wastage across six business units inside the diligence window. The investor banked the saving before the deal closed. At a Global SaaS client, €1m of Azure costs avoided through cost optimisation and right-sizing.
Technology spend splits across cloud infrastructure, software licensing, and operational delivery. We cover all three, and the recommendations across them are always connected to a single measured outcome.
AWS, Azure, and Google Cloud spend audited end-to-end. Right-sizing across compute, storage, and networking. Reservation and savings plan strategy. Idle and orphaned resource identification. FinOps maturity baseline with a structured roadmap to ongoing cost governance. Every recommendation is sized and sequenced against your operational constraints.
Application licences, productivity suites, and specialist tooling audited against actual usage telemetry. Entitlement mapped to consumption. Waste quantified and categorised by vendor, contract term, and remediation complexity. Contract renegotiation supported with an evidence pack that gives procurement real leverage in renewal conversations.
Cost-to-serve modelling across IT, operations, and managed services. We identify where structural inefficiency is embedded in the operating model rather than in individual line items, and build a roadmap for sustainable cost reduction that does not reverse when the initial audit attention fades. Benchmarked against industry comparators and our own delivery model.
A good cost audit does not just find the waste. It gives you the evidence to act on it, the structure to prevent it recurring, and a clear line from saving to P&L.
Every identified saving quantified, categorised, and prioritised. Cloud waste by service and account. Licence waste by vendor and user group. Operational inefficiency by process and contract. Structured so the CFO can read it, the CTO can action it, and the board can settle it against the investment thesis.
Vendor conversations are won with evidence, not intention. The audit produces a structured evidence pack for every material supplier relationship: usage data, market benchmarks, contract terms, and a recommended negotiation position. Procurement teams typically recover multiples of the audit cost in the first renewal cycle.
One-time savings erode without structural change. The audit concludes with a FinOps maturity roadmap and a cost governance framework that embeds ongoing visibility into the operating model. Spend is tagged to outcomes, reviewed on a regular cadence, and managed for benefits realisation rather than an annual budget cycle.
An AI-led cost diagnostic takes two weeks. The output is a quantified, prioritised view of the saving available across cloud, licences, and operating spend, with the evidence to act on it.