Plural is an AI start-up based in London, building a data science platform for finance. We're on a mission to reinvent knowledge work, by helping finance professionals make better and quicker investment decisions.

Our systems automatically mine web data in order to compute answers to complex questions on-demand such as 'what's the size of this market' or 'which companies are most likely to be acquired next year'. You can dynamically define the market you're interested and other parameters, and run the computations live before exploring the workings.

Our customers are corporate finance professionals (investment funds / banks), who use our web platform to research companies and markets, and automatically track interesting companies for potential deal signals.

Our long-term vision is to become the default interface for knowledge work, allowing business users to interact and manipulate data at scale, without knowing how to code.

Think of it as the evolution of data analysis and web sea...
Plural is an AI start-up based in London, building a data science platform for finance. We're on a mission to reinvent knowledge work, by helping finance professionals make better and quicker investment decisions.

Our systems automatically mine web data in order to compute answers to complex questions on-demand such as 'what's the size of this market' or 'which companies are most likely to be acquired next year'. You can dynamically define the market you're interested and other parameters, and run the computations live before exploring the workings.

Our customers are corporate finance professionals (investment funds / banks), who use our web platform to research companies and markets, and automatically track interesting companies for potential deal signals.

Our long-term vision is to become the default interface for knowledge work, allowing business users to interact and manipulate data at scale, without knowing how to code.

Think of it as the evolution of data analysis and web search (which, although powerful for generic questions, is a 20yo paradigm, and breaks down for advanced analysis use cases).

Engineers and data scientists have been able to use tools such as IDEs and notebooks to automate away their workflows, manipulating increasingly complex functions instead of machine code. As a result, they can operate at higher levels of abstraction, and solve ever more complex problems.

By contrast, knowledge workers are stuck in low-level abstractions: financial modelling is based on manipulating individual spreadsheet cells, or manually copy-pasting information to a new document.

Imagine if, instead of individual numbers and assumptions, you could manipulate an entire company or market, and instantly run thousands of forecasting scenarios - that is our ambition. We want to allow business users to import data, explore it, build advanced models/visualizations on top of it - all without writing code.
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Investors

M:Tech
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M:Tech Free legal mentoring and advice for early-stage tech companies

Employees

Camille Rougié
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Camille Rougié CEO 10y of experience across finance (IBD and PE) and fintech startups, turned entrepreneur. Oxford, Sciences Po, Wharton graduate