Operartis

We use machine learning to boost automated matching of invoices to receipts, trades to confirms, saving businesses $1000s of manual matching effort.

  • Stage Prototype Ready
  • Industry Financial Services
  • Location New York, NY, US
  • Currency USD

Company Summary

All current commercial solutions use rules to match transactions such as expenses to credit card, gl to bank movements etc - an approach which performs well with high quality data but with poorer quality data delivers low matching rates and mismatches, leading to expensive manual matching work and increased operational risk.
Our machine learning solution solves the poor data problem dramatically reducing the amount of manual matching effort.

Team

  • Tracey Lall
    Director of Research and Development

    An Oxford physics grad turned creative software developer and manager with 25 years of experience in the space and banking industries. She had an epiphany on how the latest technologies can take enterprise automation to the next level whilst working in trading settlement and studying for her PhD in machine learning and Artificial Intelligence at Rutgers.

  • Johanna Rustia
    Marketing, Sales and Design

    A creative director with 20 years of experience in the advertising industry delivering award winning multichannel advertising and design.

Advisors

  • Adrian Lall PhD
    Business Strategy
    Unconfirmed

Previous Investors

  • Tracey Lall
    Founder
    Unconfirmed
    Ranjit Lall
    Investor
    Unconfirmed

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