MarketMind’s GenAI Beta for Retail Investor Boost

Harnessing the power of Redis' real-time data platform, MarketMind's GenAI platform bridges the gap between public disclosures and the demands of retail investors.

MarketMind Technologies Inc. unveiled a generative AI investor relations platform. The development of this groundbreaking technology is supported by investment from the Google Cloud for Startups program. Marketmind is backed by Alpha Transform Holdings, a prolific investor and advisor in financial technology and digital assets. The founders of MarketMind had leading corporate development and strategy roles at Nasdaq and the Toronto Stock Exchange where they lead the launches of investor relations products and services for their respective exchanges.

MarketMind integrates cutting-edge AI technology from Redis, Google, and Arhasi, a Redis partner and provider of platforms and products that enable secure, governed and compliant AI solutions. Engineering development for the platform comes from Cloudbench, a Google Cloud Partner specializing in AI and web3 technologies, utilizing its joint Frisco-based AI Foundry with Arhasi.

Retail investors now hold the majority of publicly-traded shares globally. This is creating a challenge for public companies as smaller investors are too numerous to engage with to the same degree as larger investors. As AI-generated content proliferates, all investors face an increased risk of being exposed to misinformation from third-party sources such as social media, message boards and third-party web commentary.

“MarketMind is solving this problem by building a network of generative AI-based chatbots and APIs that help public companies engage with each shareholder individually” commented Jeff Maser, CEO and Founder of MarketMind.

“The chatbots are trained to provide additional, non-material commentary beyond what is available in public filings, akin to what larger investors benefit from in management meetings. This value-added content is generated via integration with companies’ investor relations and compliance workflows” he continued.

MarketMind IR’s chatbots are placed on issuers’ web sites as well as a third-party network of financial research sites, professional trading terminals, AI robo-advisors and Investor Relations technology platforms. Through these investor interactions, MarketMind helps companies build profiles of their investors and perform a suite of AI-driven analytics to provide a wide range of analysis on their shareholder bases. In doing so, it informs public companies on how to craft their corporate communications and gives them an unparalleled level of control over their messaging.

Key components of the MarketMind platform are provided by Redis. Used by 58% of Fortune 500 companies, Redis’ technology provides for low latency and seamless GenAI search capabilities. Compared with other vector databases, Redis provides 20X faster responses for Retrieval Augmented Generation (RAG) and integration with top GenAI frameworks like LangChain and LlamaIndex. Redis also reduces LLM costs by over 30% with their semantic caching.

Key features of Redis Vector Algorithms include:

1. HNSW (Hierarchical Navigable Small World) in Redis:

  • Algorithm: Redis leverages the HNSW algorithm for approximate nearest neighbor (ANN) search, known for its efficiency and scalability.
  • Performance: HNSW in Redis provides fast query times with logarithmic complexity, making it suitable for high-dimensional data.
  • Scalability: Redis can handle large datasets with HNSW, leveraging its in-memory architecture for quick access and updates.
  • Flexibility: The HNSW implementation in Redis allows dynamic updates, such as inserting or deleting vectors, without significant performance degradation.

2. Indexing and Search:

  • Indexing: Redis uses vector indices that can be dynamically updated, allowing for real-time indexing and search.
  • Search Options: Redis supports various search queries, including exact and approximate nearest neighbor searches, providing flexibility based on accuracy and performance needs.
  • Integration: Redis integrates vector search with other data structures, enabling complex queries combining vectors with other data types (e.g., hashes, sets).

“Investor Relations is a relatively untapped use case for generative AI, despite public companies spending over $30 billion annually on it. Data infrastructure, including the fastest available vector database, plays a critical role in the success of AI solutions. Developing GenAI apps with Redis is simple, intuitive and easy to scale,” said Srikanth Matcha, Americas Channel Sales Leader, Redis. “Redis’ mission is to help companies build fast apps, fast so partners like Arhasi and innovative companies like Marketmind quickly bring the best products to market faster, in important industries like investor relations.”

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