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Founded Year



Series E | Alive

Total Raised




Last Raised

$150M | 2 mos ago



About AlphaSense

AlphaSense develops a market intelligence platform. The company's Artificial Intelligence (AI) technology aims to help professionals make business decisions by offering insights from private content, such as company filings, event transcripts, expert call transcripts, news, trade journals, and equity research. It serves consulting, energy, financial services, life science, and other sectors. The company was founded in 2011 and is based in New York, New York.

Headquarters Location

24 Union Square East 5th Floor

New York, New York, 10003,

United States

(646) 783-1995


ESPs containing AlphaSense

The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.

Financial Services / Capital Markets Tech

The institutional investment analytics and insights market refers to a range of software and services that provide investment managers with tools to help them make informed investment decisions and optimize their portfolios. These solutions typically include investment research, data analysis, risk management, and performance attribution capabilities. The market for institutional investment analyt…

AlphaSense named as Leader among 7 other companies, including SESAMm, Smartkarma, and Accern.


Research containing AlphaSense

Get data-driven expert analysis from the CB Insights Intelligence Unit.

CB Insights Intelligence Analysts have mentioned AlphaSense in 4 CB Insights research briefs, most recently on Oct 6, 2023.

Expert Collections containing AlphaSense

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

AlphaSense is included in 6 Expert Collections, including Unicorns- Billion Dollar Startups.


Unicorns- Billion Dollar Startups

1,228 items


Fintech 100

1,097 items

250 of the most promising private companies applying a mix of software and technology to transform the financial services industry.


Capital Markets Tech

956 items

Companies in this collection provide software and/or services to institutions participating in primary and secondary capital markets: institutional investors, hedge funds, asset managers, investment banks, and companies.


Market Research & Consumer Insights

721 items

This collection is comprised of companies using tech to better identify emerging trends and improve product development. It also includes companies helping brands and retailers conduct market research to learn about target shoppers, like their preferences, habits, and behaviors.


Artificial Intelligence

10,987 items

Companies developing artificial intelligence solutions, including cross-industry applications, industry-specific products, and AI infrastructure solutions.



8,123 items

Companies and startups in this collection provide technology to streamline, improve, and transform financial services, products, and operations for individuals and businesses.

AlphaSense Patents

AlphaSense has filed 24 patents.

The 3 most popular patent topics include:

  • computational linguistics
  • natural language processing
  • data management
patents chart

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Latest AlphaSense News

Best practices for developing a generative AI copilot for business

Nov 22, 2023

Chris Ackerson Contributor Chris Ackerson, formerly of IBM Watson, is currently the Vice President of Product at AlphaSense, a market intelligence and search platform, where he spearheads the development of AI and ML capabilities to deliver better data and insights to thousands of enterprise companies. Since the launch of ChatGPT, I can’t remember a meeting with a prospect or customer where they didn’t ask me how they can leverage generative AI for their business. From internal efficiency and productivity to external products and services, companies are racing to implement generative AI technologies across every sector of the economy. While GenAI is still in its early days, its capabilities are expanding quickly — from vertical search, to photo editing, to writing assistants, the common thread is leveraging conversational interfaces to make software more approachable and powerful. Chatbots, now rebranded as “copilots” and “assistants,” are the craze once again, and while a set of best practices is starting to emerge, step 1 in developing a chatbot is to scope down the problem and start small. A copilot is an orchestrator, helping a user complete many different tasks through a free text interface. There are an infinite number of possible input prompts, and all should be handled gracefully and safely. Rather than setting out to solve every task, and run the risk of falling short of user expectations, developers should start by solving a single task really well and learning along the way. At AlphaSense, for example, we focused on earnings call summarization as our first single task, a well-scoped but high-value task for our customer base that also maps well to existing workflows in the product. Along the way, we gleaned insights into LLM development, model choice, training data generation, retrieval augmented generation and user experience design that enabled the expansion to open chat. LLM development: Choosing open or closed In early 2023, the leaderboard for LLM performance was clear: OpenAI was ahead with GPT-4, but well-capitalized competitors like Anthropic and Google were determined to catch up. Open source held sparks of promise, but performance on text generation tasks was not competitive with closed models. To develop a high-performance LLM, commit to building the best dataset in the world for the task at hand. My experience with AI over the last decade led me to believe that open source would make a furious comeback and that’s exactly what has happened. The open source community has driven performance up while lowering cost and latency. LLaMA, Mistral and other models offer powerful foundations for innovation, and the major cloud providers like Amazon, Google and Microsoft are largely adopting a multi-vendor approach, including support for and amplification of open source. While open source hasn’t caught up in published performance benchmarks, it’s clearly leap-frogged closed models on the set of trade-offs that any developer has to make when bringing a product into the real world. The 5 S’s of Model Selection can help developers decide which type of model is right for them:

AlphaSense Frequently Asked Questions (FAQ)

  • When was AlphaSense founded?

    AlphaSense was founded in 2011.

  • Where is AlphaSense's headquarters?

    AlphaSense's headquarters is located at 24 Union Square East, New York.

  • What is AlphaSense's latest funding round?

    AlphaSense's latest funding round is Series E.

  • How much did AlphaSense raise?

    AlphaSense raised a total of $747.36M.

  • Who are the investors of AlphaSense?

    Investors of AlphaSense include Viking Global Investors, Goldman Sachs, CapitalG, Bond, BAM Elevate and 27 more.

  • Who are AlphaSense's competitors?

    Competitors of AlphaSense include Auquan, SESAMm, Amenity Analytics, Hebbia, Sentieo and 7 more.


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