Aleph.

Aleph is an AI-native platform for Financial Planning & Analysis - the discipline of budgeting, forecasting, and reporting that sits at the center of how a company understands itself. Its core function is deceptively plain: pull financial and operational data from the many systems a business runs on, hold it in one governed place, and pipe it live into the spreadsheets analysts already build in.

The platform connects to more than 150 sources - accounting systems like NetSuite and QuickBooks, CRMs like Salesforce, data warehouses like BigQuery - through no-code connectors. That data lands in a single source of truth, then flows into Excel and Google Sheets through native add-ins that sync in both directions. On top sits a layer of AI that cleans data, maps accounts, explains variances, and answers questions.

The result is that the weekly grind of FP&A - exporting CSVs, fixing broken links, reconciling numbers that should already agree - largely disappears. What's left is the analysis that actually informs decisions.

It is an unglamorous problem, and that is rather the point. The durable software companies tend to be built on the tasks nobody enjoys.

Aleph's customers are the finance and FP&A teams inside fast-scaling companies - the analysts, controllers, and CFOs responsible for turning raw ledgers into forecasts and board decks. Named customers include Webflow, Turo, Notion, Zapier, Harvey, Chess.com, and Y Combinator itself, alongside hundreds of others.

The problem they share is structural. A modern business generates financial signal across a dozen disconnected systems. Reconciling those by hand is slow, error-prone, and unrelenting - a new close every month, forever. Legacy FP&A suites tried to solve it by making teams rebuild everything inside proprietary tools, often with months of consultant-led implementation.

Aleph's answer is to remove the migration entirely. Onboarding is measured in hours, not quarters. Data arrives live. And because the front end is still Excel or Google Sheets, there is no new modeling language to learn.

The company reports that the leverage is real: teams of one or two people now doing FP&A work that previously required five or six. That is not a headcount cut so much as amplification of the people already there.

Model. Aleph is a B2B SaaS company. It sells subscriptions to mid-market and enterprise finance teams, with rapid time-to-value - live data in hours - as a central selling point against consultant-heavy incumbents. Reported high win rates against legacy tools and near-perfect retention suggest the pitch lands once teams see their own data flowing.

Market. FP&A is a large, entrenched software category long dominated by names like Anaplan and Workday Adaptive Planning, and more recently contested by modern challengers such as Pigment, Cube, Mosaic, and Datarails. Aleph positions itself apart on two axes: it is spreadsheet-first rather than spreadsheet-replacing, and it is AI-native - designed from the data layer up around what models can now do, rather than adding a chatbot to older architecture.

Expertise. The company's edge is domain fluency paired with engineering depth. Its two founders come from the business and technical sides of the same problem, and the team is engineering-heavy and remote-first. That combination shows up in the product decision that defines Aleph: respecting how finance professionals actually work.

Where it fits. As AI reshapes back-office software, Aleph is a clean example of the pattern likely to win - tools that remove work rather than add features, and that amplify a company's best people instead of promising to replace them.