We work with four types of companies — each at a different stage, facing different pressures, with one thing in common: they need a clearer connection between operations, AI, and P&L performance.
Companies with revenue — sometimes significant revenue — but margins not reflecting the commercial activity being generated. The business is busy, but the P&L is not improving in proportion to the effort invested.
Revenue growth conceals structural problems in the cost model, operating efficiency, or pricing architecture — until the cash flow reality becomes impossible to ignore.
They focus on growing revenue to outrun the cost problem rather than fixing the operating model. Revenue growth without cost structure improvement is a path to a larger version of the same problem.
A clear understanding of where profit is leaking from the business, a redesigned operating model that reduces cost-to-serve, an AI roadmap targeting the highest-ROI improvement opportunities, and a financial performance cadence that keeps improvement on track.
Portfolio companies where the investment thesis depends on margin expansion, operating leverage, or profitable scaling within a defined timeline. Board scrutiny is high, the hold period creates urgency, and every dollar of operating spend needs to justify itself.
AI has entered every PE board conversation — but most portfolio companies are deploying AI without the financial discipline to connect it to the EBITDA targets the board is tracking.
Treating AI as a separate workstream rather than integrating it into the EBITDA improvement plan. AI initiatives not connected to specific P&L lines are invisible to the board and cannot be defended in quarterly reviews.
A financially grounded AI roadmap connected to the EBITDA plan, board-ready reporting on AI's contribution to margin improvement, a prioritized list of operating model changes ranked by financial return, and a management cadence that keeps the EBITDA trajectory on track.
Companies scaling quickly on the back of strong product-market fit, but without the operating discipline or financial visibility to ensure the growth is profitable. The burn rate is accelerating and the unit economics are not improving at the rate the business plan requires.
Assuming operating discipline can wait until they are "bigger." By the time the operating model needs to be rebuilt, the organization is too large for the redesign to be quick — and the burn rate makes the delay expensive.
Unit economics clarity, a repeatable operating model designed for scalable growth, AI use cases prioritized by impact on burn rate and contribution margin, and the KPI infrastructure to track improvement as the business scales.
Corporate teams or new product divisions with access to resources, technical capability, and executive buy-in — but struggling to connect AI experimentation to measurable business outcomes that the broader organization can hold up as evidence of value.
Measuring activity rather than outcomes — number of pilots launched, tools deployed, workshops run. The financial stakeholders who control the budget measure profit improvement. The gap between those two measurement systems is where innovation mandates get lost.
A prioritized AI roadmap connected to business unit P&L impact, a governance model that defines accountability and measurement standards, a structured path from current pilots to production-deployed AI, and board-ready reporting on AI's contribution to financial performance.
Financial diagnostic and operating model redesign before any AI initiative.
AI connected to the investment plan and board-reportable financial improvement.
Operating discipline and AI leverage designed for profitable, sustainable scaling.
A governance model and financial measurement framework to justify continued AI investment.