Take AI from pilot to tipping point.
Massif is the platform where a company's AI work lives: what it already has, what each piece is worth, and how the next idea gets funded. Forward-deployed engineers use it to bring a company to the point where its own teams choose AI without being pushed. Then they move on.
Watch the film · 1:50
Pilots work. Scale stalls.
Every large company is running AI pilots, and most of them never reach the P&L. The models are ready. What stops the work is the company's capacity to absorb change: staff expect replacement, stewards expect blame, and sponsors expect another write-off. Most companies have no transformation team, so each win depends on an engineer staying.
of organizations are getting zero return on their generative-AI investment.
MIT NANDA, The GenAI Divide: State of AI in Business 2025, July 2025
Companies that break through share three conditions. Massif puts all three in place.
With all three in place, adoption compounds. Business units start choosing AI on their own, and the engineer's work moves from building to connecting.
They know where AI pays, in their own operating terms.
Read from the company's own systems, in days, with nobody filling in forms.
Finance agrees how value is counted before work starts.
Proven wins earn the next round of funding, and the proof comes from the ledger.
Teams publish AI that other teams find, trust and reuse.
Built once, used everywhere it fits, with the credit going to the team that built it.
Built so the people the change lands on can trust it: credit goes to teams, never to one person's output, and people hear about a change before it reaches them.
Software engineers build on, connected to the company's real systems.
Massif reads a company's systems directly, keeps one evidenced model of what it finds, and opens that model to people, tools and agents.
Reads the systems directly
The ledger, the service desk, contracts and cost models. What each source sent is kept exactly, and every row cites the file it came from.
Every figure traced to its source
Each number carries a confidence grade and walks back to the rows it was built from.
Reads contracts with citations
A contract reader, tested on 102 real contracts, extracts terms and quotes the clause each one came from.
Open to engineers and AI agents
An API and an agent interface let engineers and agents query Massif and build on it.
One product, three ways in.
Hyperscalers, frontier labs and deployment firms
An engineer's job becomes getting a company to its tipping point, then moving on to the next.
Companies running their own AI programs
AI that reaches the P&L, with value finance signs and governance from the first project.
Funds with an operating partner practice
A win at one company, priced and ready for the next one across the portfolio.
Operators who've run this work from the inside.
Alison Andrews Reyes
2x Global Director at Google, in Security Solutions Engineering, then Cloud Migrations and Solutions Center Engineering. She built the Solutions Center's agentic platform for cloud architecture and sales, launched it to 14,000 sellers and engineers worldwide, and helped accelerate $2B in pipeline.
- Five patents, four in agentic cloud systems at Google
- General Partner at 1843 Capital
- Operator through three exits: Vigilant to Deloitte, e-Security to Novell, eGrail to FileNet
- Dartmouth, BA Engineering Sciences
Greg Felice
Seven years at Google, most recently Senior Principal in the Financial Services Industry Value Practice. He built the migration ROI application that became Google Cloud's global standard for deal economics, behind more than $1B in migration revenue.
- Wrote Massif's engine
- Enterprise transformation at The New York Times, in the Office of the CTO, and at Altice USA
- Earlier, EY
- Contributor to Apache AGE, the graph database Massif runs on
Our early access program is open now.
The first cohort takes Massif into real companies: one company, one value stream, one quarter, alongside our engineers.