Systems, dependencies, capabilities, controls and costs as a single model. The first lens, live today, is value creation: compare the estate to a reference, decide the fate of every piece, sequence the work by the cost of waiting, and track projected against realized. Every dollar of freed cash traces back to the specific move that captures it.
Enterprises and their investors make nine-figure decisions about their technology estate on stale spreadsheets and consultant slideware. In M&A the cost is highest and the data is worst. Origination, diligence, close, integration and exit are each run by a different team, in a different tool, on a different spreadsheet. Diligence findings evaporate at close. The synergy case nobody is later held to. The 100-day plan reinvented from scratch.
Corporate transformation and private-equity value creation look like two disciplines. They are not — they run the same work; only the terminal objective differs, synergy and accretion to the parent for the enterprise and IRR and MOIC for the fund. One codebase, two markets, no fork.
Diligence is the first draft of the plan. The work done to justify the price becomes the plan to realize it — nothing is rebuilt at close, and the pre-close synergy case becomes the post-close accountability baseline. The lifecycle is symmetric: at exit, vendor due diligence is the same engine run outward. One model carries from the data room, through Day 1, to the exit.
A competitor stitches together a screening tool, a diligence data room and a PMI tracker, and the value dies in the handoffs. We remove the handoffs: the diligence model, the integration plan and the scoreboard are the same object.
| Tier | What it means | Use cases |
|---|---|---|
| Working | Demonstrated end to end on a real industry reference model | Capability analysis · onboarding · estate rationalization — risk-aware, prioritized, costed |
| Engine ready | Shared machinery built and tested; needs the specific reference wired in | Compliance alignment · M&A synergy mapping · PMI planning |
| Designed | Requirements captured in depth; built on the same engine next | Platform roll-up · deal screening & pipeline · due diligence · PE value creation & exit |
The expensive, risky core is built and proven. Most of the roadmap is reach, not invention.
One-time. Discovery, source mapping, connector configuration, secure tenant stand-up.
Recurring ARR. Module subscription by scope under management, plus a managed wrap.
Project work delivered on the platform. Priced to outcome, not to hours.
Connectors built once during onboarding are reused by every subsequent lens — the second lens costs the customer less and earns us more. Typical ranges, scoped per engagement.
Alison Andrews Reyes — CEO. 25+ years across operator, investor and technical leadership. Two director roles at Google Cloud, where she built an agentic AI platform to 2,000 weekly active users, a 20× efficiency improvement and billions in pipeline. Five patents — four in agentic cloud systems, one in threat intelligence. GP at 1843 Capital: 38% IRR, 6.6× multiple, $200M+ raised. Operator through three exits — Vigilant/Deloitte, e-Security/Novell, eGrail/FileNet. Dartmouth, BA Engineering Sciences.
Greg Felice — CTO. Built the Massif engine and runs the production estate it lives on. At Google, scaled the methods enterprises use to analyze ROI on strategic technology investment and optimize IT TCO — Massif is that method, productized. Managing Partner and CTO at Pipeline Strategy Partners, doing IT due diligence and post-merger integration for Fortune 100 executives at JPMorgan Chase, Disney and Deutsche Bank. Enterprise transformation in the Office of the CTO at The New York Times; EIT strategy at Altice USA. Earlier, Ernst & Young.
One founder has sat on all three sides of this transaction — operator, investor, and the platform leader who built the AI. The other does technology diligence and post-merger integration for a living, and built the engine to do his own job better.
We are already generating revenue and choosing when to add fuel. Raising a $3–4M seed to widen the reference library, deepen the financial models, build the live ingestion layer, productize the interface, add the risk and resilience lens, and convert two to three design partners into product ARR — the flip from services-majority to product-majority revenue.