Data-driven Insight,

at Scale

Natural Language Processing and Machine Learning Solutions for Investment Firms and Corporates.

100+ client use cases around the world

Use Cases

Unique data insights for quantitative or fundamental investment, due diligence,
portfolio monitoring, market research and more.

Data Vizualisation
and Exploration

Text Analysis
API

Alternative Data
Streams

Data Science
for Investment

Video

QuantMinds - ESG insights for Quantitative Investment

Discover how Natural Language Processing is leveraged to create ESG datasets based on web data and generate time series for quantitative use cases.

Video

Fundamental Investment and Private Equity with Alternative Data

Complete presentation of our use cases including Sourcing and Idea Generation, Due Diligence, Risk Management and ESG.

Whitepaper-SESAMm-SignalReveal

Whitepaper

Using Machine Learning to Extract Alpha from Alternative Data

In this paper, we apply SESAMm’s proprietary SignalReveal Machine Learning pipeline on IHS Markit data to optimize a macro investment strategy with an efficient and high-value alpha creation process.

Products

We analyze, in real-time, billions of articles and messages from the web. We do this using advanced Natural Language Processing techniques. Finally, we provide Machine Learning tools to build analytics and investment strategies.

TextReveal
ESG-TextReveal

Natural Language Processing Engine

Generate Alternative Data from text using NLP algorithms on a massive, ready-to-use data lake. 

  • Sophisticated NLP requests on an industry-leading data lake
  • Sentiment, emotions, ESG and thematic analysis
  • APIs and visualization dashboards
SignalReveal logo
feature-selection

Data Science Engine

Create investment signals using Machine Learning algorithms.

  • Modular and open Machine Learning pipelines
  • Alternative Datasets evaluation & integration
  • Quantitative signal creation
Streams-logo
Training Prediction SignalReveal

Ready-to-use Data Streams

Curated, granular data streams for investment professionals:

  • Refined sentiment, ESG and retail trading time series for investment use cases
  • History starting in 2008, covering thousands of stocks
  • Daily data with financial identifier mapping