You promised the board a number.
Massif is how you hit it.
One model, from the data room to the exit. Tiger Mountain reads the target's technology: every system, what each one costs, when each contract ends. It builds one AI-native model of all of it. That model prices your deal, becomes your plan, and tracks every dollar to the person who owes it. When the board asks where the savings stand, you open it and show them.
Watch $6.8M fall through the cracks of one ordinary deal
We pointed Massif at a reference deal. Diligence found nine problems and priced them into the deal. Five made it into the plan with an owner. Four went to nobody, and those four were worth $6.8M. On most deals, that money is never seen again. Here is Massif catching it:
The Void, in the product. Every diligence finding, and whether it reached the plan. Handoff integrity 56%. Dropped: $6.8M across four findings, each one named, priced and traceable to the document it came from. Financial and commercial findings drop as a class, because those two categories have no natural owner on the operating side. Fictional deal, real engine.
Read the four figures left to right. The first two are ours and specific to one estate. The second two are the industry's, from 350 integration leaders. They measure the same seam from opposite ends and they agree: a third to a half of the value you underwrite stays on the table.
Why one model beats five tools stitched together
Every deal changes hands four times: diligence to close, close to integration, integration to operations, operations to exit. At each handoff, a new team rebuilds its own model from the last team's documents, and value quietly falls out. Better tools inside each phase can never fix a leak that sits between the phases. One shared model can.
Why this holds for thirty years. Deals will still have phases, and phases will still have boundaries. As long as a boundary exists, something has to carry across it intact. That is the business, and it outlives any particular tool on either side of the boundary.
The AI works now. $2.5T of dry powder is waiting.
Three things changed, and the order matters. The technology cleared first, then the money moved to operations, then the buyers were handed a mandate. Any one of the three alone would make a worse business.
1. The technology cleared
AI can finally read the messy, mislabelled, half-complete data every real company runs on. Five years ago that was a research problem, and it is the single reason every earlier attempt at this category died.
2. The money moved to operations
$2.5T+ of private-equity dry powder is waiting. H1 2026 closed 67% fewer transactions at roughly 10% higher aggregate value: fewer deals, larger, each one carrying far more underwriting weight than it would have in 2024.
3. The buyers have a mandate
Value-creation teams, CIOs under cost pressure and integration offices are being asked the same question this year. Show the number, and show where it came from. That request describes this product.
Sources: BDO 2026 Private Equity Predictions, FTI Consulting, PwC US Private Equity Deals 2026 Midyear Outlook.
The work that priced the deal becomes the plan that delivers it
Corporate transformation and private-equity value creation run the same work. Only the terminal objective differs: synergy and accretion for the enterprise, IRR and MOIC for the fund. One engine, two scoreboards, one codebase.
Diligence is the first draft of the plan
The work that justified the price becomes the plan that realizes it. Your pre-close synergy case becomes your post-close baseline, so somebody can be held to it. Run the engine outward at exit and it writes your vendor due diligence from the same graph.
A stitched toolchain keeps its seams
Assemble a screening tool, a diligence data room and a PMI tracker and you have one platform with two boundaries inside it. We compete as the absence of those two boundaries rather than as a better tool for any of the three jobs.
Connect your systems once. Then ask about value, risk, controls, continuity.
Massif answers the value question. Four more modules answer the risk, controls, continuity and platform questions, and all five read the same model, through lenses tuned to the three people who run a deal: the managing partner, the operating partner and the integrator. Enter a system once and every module knows it. Nobody types anything in twice, and no two modules ever disagree about a number.
Count the integration cost module by module. Value reads eight sources. Risk adds four. Controls and continuity add zero, because a control resolves against systems and identity, and a recovery objective against services and telemetry, and both were connected for the first two modules. Module names are working names, pending clearance.
One graph, three companies, coloured by duplication. The estate map holds every system in the deal, not only the platform company's. Where two estates run the same capability, orange marks the duplicate and blue marks the instance that survives. On this deal that is $1.98M a year of duplication, $1.70M of it already decided, worth $20.4M of equity value at the entry multiple. It sits outside the underwritten case, because the add-ons were priced on their own EBITDA with no integration case. Fictional deal, real engine.
We read the systems you already chose and compete one layer up, on the shared model and the lineage a point tool leaves behind. Starting requires you to replace nothing.
What works today, and what the same engine does next
We publish this table because a good demo will otherwise imply more than it should. The top row is what you can watch working this week. The bottom row is what the same engine does next.
What "costed" now means. Ask the product what a use case costs and it answers with the phases, the roles, the allocation per phase and the rate for each role, then adds contingency by a published rule. Change the rate card and every figure in the plan moves. On a fund-level master agreement at 12% off external labour, this plan moves from $8.07M to $7.43M, and you can watch it happen.
Massif found $560K a year on the first estate it read
The engine is live, with a traced number
$560K/yr of realizable run-rate saving on one mid-size estate, every dollar traced to a named system and its disposition. That is what the engine found, rather than a model of what it would find.
Operated rather than prototyped
GitOps-managed Kubernetes estate, federated MCP gateway, gated auth, semantic tool discovery. Underneath a demo like this an investor usually finds a notebook.
Paying work funds the build
Consulting engagements pay for the product while design-partner conversations run. A hyperscaler co-sell motion is built and the anchor account is selected.
Pipeline arrives warm
Named accounts across regulated Fortune 500 estates and private-equity portfolios, reached through relationships rather than outbound.
Pay to connect once. Each module after that costs less.
Onboarding
Discovery, source mapping, connector configuration, secure tenant stand-up.
Platform and SaaS
Module subscription by scope under management, plus a managed service wrap.
Value-add consulting
Project work delivered on the platform. Priced to the outcome rather than to hours.
Commercial terms are scoped per engagement today; a formal rate card is in progress.
One of us built the engine. Both of us saw the market.
We met this problem at Google Cloud, from opposite sides of the same customer conversations: Alison on the global solutions engineering side, Greg on the value engineering side. Same enterprises, same nine-figure technology decisions, and the same value leaking between the deal and the delivery. We agreed on the market before we agreed to start the company.
Investor, operator, founder and product leader. Two director roles at Google Cloud on the global solutions engineering side, where she took an agentic AI platform to 2,000 weekly active users, a 20× efficiency improvement and billions in pipeline. Led product at Vigilant through its sale to Deloitte.
Primary author of the Massif engine, built to do his own job better: Greg does technology diligence and post-merger integration for a living. At Google, on the value engineering side, he scaled the methods enterprises use to analyze ROI on strategic technology investment and optimize IT TCO. Massif is that method, productized.
Greg wrote the engine. The conviction is joint. One founder is the customer: he runs these deals for Fortune 100 clients today. The other has been the investor, the operator and the platform product leader, and has priced, funded and shipped this category from every other side of the table. We knew who needs this, and what it is worth to them, before a line of it existed.
Bring one deal. Massif shows you the money inside it.
Engagements are fixed-fee and gated. The first phase stands up the model on your estate: every system, what each one costs, where the duplication sits, and a costed plan to capture it. The output is yours either way, and every phase after the first has to earn itself with the numbers from the one before.
See it running, on a real estate →Confidential, for discussion. Company and module names are under trademark screening and clearance is pending. Screenshots show a fictional fund and fictional companies; the figures illustrate how the engine renders and are not customer data.