Vendor data integration and AI SaaS platform for Wealth Managers

On average fund managers spend more than 2 times the vendor subscription costs turning raw vendor data from the likes of Refinitiv or Factset into clean, investment-ready data. This is due to the complexity, variety and volume of vendor datasets. Complexity typically arises from many sources, such as symbology; the unique features of each vendor datasets; combining datasets into useful financial ratios & signals without falling into the many pitfalls; the extensive quality assurance to guarantee readiness for investment use.

With decades of experience in dealing with this complexity, Quant Machina has developed a globally scalable analytics & data management SaaS platform for equity fund managers. Both quantitative and fundamental managers significantly benefit from the platforms ability to quickly bridge the gap between raw data feeds from data vendors and having clean, usable data, with considerable time and money savings both at setup and ongoing costs. 

In addition to getting vendor datasets to the point of being investment ready, Quant Machina is able to significantly enrichen and add value with AI-powered signals and solutions, along with a suite of tools in Beta for back testing, portfolio management, execution and reconciliation. Additionally Quant Machina can consult on bespoke projects, including the ability to apply machine learning to new datasets, and artificial intelligence to automate processes.


Benefits of SaaS Platform

Significantly Cheaper than Doing It Yourself    Significantly Quicker to Market / Implementation
66% reduction in direct costs, including reduction in personnel required to maintain codebase 80% quicker to market (3 months vs 18 months), which also means less fund manager capital burn through, more time to establish track record / less opportunity cost
Constant improvements outsourced to the experts Multi-vendor data integration
Outsource to experts with deep domain knowledge, with constant updates as vendor delivery improves or changes, and quality assurance and notifications Integration across multiple vendors with differing symbology (requires sedol license), as well as third party data or software platforms (eg Calibre)
Bolt-on Data Modules for non standard datasets Bolt-on Value-Add Analytics and AI-powered Modules
Value add on data modules to integrate non-standard datasets (eg Refinitiv's StreetEvents or Factset EcoRev), straight out of the box Value add on analytic modules such as multi-factor modeller, event study, factor backtester, and portfolio analytics (straight out of the box)

Examples of problems we solve

Quant Machina's SaaS platform provides fund managers with easy-to-use data integration and analytics platform that significantly reduces both setup and maintenance cost to investment implementation in both time and money, and extracts significant extra value from vendor datasets, and provides beyond vendor-data tools to extra further value from internal investment workflows.

Equity Quant Fund Managers

  1. 1/ Out-of-the-box instant core platform that integrates prices, fundamentals and estimates. Includes ready made factors/signals. Radical reduction in time and cost to fund inception
  2. 2/ Add-on modules to integrate non-standard datasets like Refinitiv Street Events (text based) or Factset EcoRev
  3. 3/ Ability to mix and match across vendor datasets (eg Factset EcoRev/supply chain dataset with Refinitiv core products)
  4. 4/ Precanned backtesters (factors/events)

Equity Fundamental Fund Managers

  1. 1/ Rapid and cost effective deployment of single sourced financial datasets (eg Refinitiv Street Events, or Factset Estimates) including those with non standard delivery (eg API or FTP)
  2. 2/ Rapid and cost effective deployment of suite of financial datasets (eg prices, estimates and fundamentals from one of the major vendors). Save money at setup and ongoing (significantly lower dependence on IT support to maintain)
  3. 3/ Integrate data across vendors and to internal fund manager data
  4. 4/ Backtest of new strategies (requires generation of historical recommendations)

Data Vendors

  1. 1/ Increase likelihood of sale to fund manager with shorter sales cycle, and time in trial
  2. 2/ Reduce likelihood of fund manager dropping subscription due to poorly accessible datasets
  3. 3/ Independent White papers to demonstrate the value proposition of data content sets to fund managers
  4. 4/ Analytics platform to distribute value-add content sets and findings from White papers


  1. 1/ Front end manager of Vendor datasets (eg Refinitiv QA Direct)
  2. 2/ Easy management and accessibility of vendor data not on Wharton RDS (Refinitiv and Factset)

SaaS Platform Subscriptions  

Available now

  • 1/ Core integration platform for: 
    • 1.1/ Refinitiv QA Direct core datasets (prices, fundamentals and estimates)
    • 1.2/ Factset Standard DataFeed (prices, fundamentals and estimates)
  • 2/ Refinitiv add on module: 
    • 2.1/ Street Events are timely transcripts of English-based management conference calls

In Beta

  • 1/ Postgress implementation of QA Direct
  • 2/ Web Portal for Management of Refinitiv QA Direct or Factset Standard DataFeeds (notifications, quality assurance)
  • 3/ Refinitiv API calls on core datasets
  • 4/ Factset API calls on core datasets
  • 5/ Event backtester

Future Development

  • 1/ Across vendor integration module
  • 2/ User driven data overrides
  • 3/ Factor backtester (simplistic signal testing)
  • 4/ Optimised Portfolio Backtester (realistic portfolio testing with risk model implementation)

    Our partners


    The Founders

    The founders have combined 40+ years experience as Global Equity Brokers/Fund Managers and Developers, with deep knowledge of how correctly handling complex & disparate financial datasets and issues such as market data, fundamental data (accounts), analyst data, symbology, universes, factor calculation (eg quant signal as well as fundamental financial ratios) as well as workflow quality assurance. 

    Richard Lawson

    Prior to a stint in a number of startups, Richard had spent 20+ years as an quantitative investor/data scientist, specialising in the application of machine learning techniques to managing investments in global capital markets (listed equities). Started career on the sell side at top names such as JPMorgan and Macquarie, then broadened to funds management and hedge funds. At Schroders, was in a team managing more than 40 billion USD, where Richard specialised in deep dive industry specific research (embedding fundamental based investment principles into quantitative investment processes).

    Richard is also a co-founder of an Australian venture capital firm investing in Australia technologies linked to Artificial Intelligence. 

    Connah Cutbush

    Connah is co-founder and Chief Technology Office of CodeSource, a leading Australian software development agency, specialising in financial services solutions and function rich web platforms.

    Connah is widely regarded as one of Asia Pacific’s leading technology experts within financials services, specialising in the design and implementation of data analytics platforms. With more than 25 years experience within the sector, Connah has built analytics and trading platforms for numerous institutions including Macquarie bank’s global equities division as well as a number of Australia’s most successful hedge and quantitative fund managers.

    Connah is the CTO of Quant Machina and it represents the 5th generation of quantitative platform he has designed, built and implemented. More than $10 billion of assets are managed on systems Connah has designed and built.