How AlphaSense Added Generative AI to Its Existing AI Stack
AlphaSense has used artificial intelligence technologies for over ten years, as a way to compete against the likes of Bloomberg in the market intelligence industry. It recently launched its first generative AI application, Smart Summaries, which provides summaries of content such as earnings calls.
We spoke to VP of Product Chris Ackerson, who previously worked at IBM on the Watson project, about how AlphaSense introduced generative AI to its AI stack — and how the company intends to expand its product into a full-on AI personal assistant.
AlphaSense started as a platform to search for information in company-issued documents, such as regulatory filings and earnings calls. From the beginning, Ackerson said, AI was leveraged to support semantic search. As deep learning became more prominent in the 2010s, AlphaSense was able to organize and classify vast amounts of content using what he termed “pre-generative AI systems.”
“And then now with generative AI, we’re sort of feeding all of that structure and organization that we’ve created into these generative models, that aren’t just good at organizing or classifying content, but now can actually generate written language for users,” Ackerson said.
AlphaSense has been using language models in production for many years and is now developing its own large language models (LLMs). Since the introduction of BERT (Bidirectional Encoder Representations from Transformers) by Google in 2018, Ackerson says that AlphaSense has been leveraging the latest open source models and fine-tuning or training them on their financial content. It then optimizes the trained models for specific tasks, such as summarization or sentiment analysis.
Smart Summaries
AlphaSense released its first generative AI product, called “Smart Summaries,” in April. I asked Ackerson how customers are using this product.
One of the main benefits customers are experiencing with Smart Summaries is the ability to track a significantly larger number of companies, he replied. For example, hedge fund analysts may now be able to monitor 20 companies in their portfolio in a much more comprehensive manner.
He said that Smart Summaries achieves this by summarizing key information from various sources, such as earnings calls, sell-side research, and expert network interviews. For each company being tracked, AlphaSense provides users with a condensed overview — which Ackerson compared to a table of contents.
AlphaSense is working on other generative AI initiatives, to extend the functionality of Smart Summaries. Traditionally, he said that AlphaSense has been a pull-based experience, where users come to the product when they have a specific question or problem to solve. Based on generative AI, he believes they can evolve the product into more of a push-based experience. He wants AlphaSense to, eventually, proactively understand what is important to users, by analyzing top-end trends and automatically surfacing relevant information.
He added that it’s important to not just organize information for users, but also provide recommendations and personalization. Currently, Smart Summaries focuses on individual companies, but AlphaSense plans to extend that capability to summarizing trends across portfolios and sectors in the coming months.
Furthermore, AlphaSense is working towards enabling a more freeform chat-like interface, allowing users to interact with the system in a conversational manner. While the current system supports semantic search and the ability to ask questions, Ackerson noted, the company is investing in creating a more interactive dialogue, where users can have follow-up conversations with the system.
BloombergGPT
Needless to say, AlphaSense’s competitors are also investing heavily in generative AI. None more so than Bloomberg, which at the end of March announced BloombergGPT, an LLM for finance. Earlier in our conversation, Ackerson mentioned that AlphaSense is creating LLMs as well. I asked whether they will be something similar to what Bloomberg announced, or different.
“So we are developing multiple LLMs for various tasks in the product,” he replied. “What Bloomberg announced was one large model — it was a research paper, sort of replicating some of the GPT-3 approach to training a model.”
He added that AlphaSense hasn’t seen BloombergGPT in any products yet, so “from our perspective, it’s a research paper.”
According to Ackerson, both AlphaSense and Bloomberg are focusing on fine-tuning and training their models to understand the language and domain of finance and market intelligence. The difference, he said, is that “we’re very much focused on rolling out real products and real features built on top of those LLMs.”
For its part, Bloomberg has stated previously that it plans to integrate BloombergGPT into its terminal software.
What Developer Tools AlphaSense Uses
I noted my recent article on the use of tools like LangChain by developers, to help use LLMs in their applications. I asked Ackerson about AlphaSense’s developer stack for AI.
He first acknowledged that it is still the early days of building software tooling around LLMs. But he said that LangChain is one of the tools and frameworks that his team is evaluating for potential use. In general, the company is tracking improvements in efficiency and capabilities of open source models.
Ackerson pointed out a change in the market over the past six months. Initially, it was expected that large models from a few dominant players would prevail. However, he said now there is a consensus that smaller models can achieve comparable performance, but with the advantage of having full control of the stack.
“Not having to worry about data flowing back and forth over the internet, between APIs and things, is an enormous advantage,” he said. “And so we’re leaning heavily into leveraging what’s coming out of the open source community, and then obviously tuning and developing on top of those to support our needs.”
What about storing all the data that AlphaSense collects?
The company receives content from various sources, he explained — including sell-side research, company documents, their own expert network with transcribed interviews, and news feeds. It has hundreds of millions of documents, which it stores in a combination of document search and vector databases. He added that this hybrid approach enables support for different workflows, including document search and Q&A chat functionalities.
Data Integrity
I brought up the issue of hallucination in generative AI and asked how AlphaSense deals with this in its products.
Ackerson explained that AlphaSense takes a different approach compared to consumer-focused generative AI solutions. Firstly, it curates the content that flows into AlphaSense, ensuring that it comes from authoritative sources. Secondly, it places emphasis on auditability, by allowing users to see where an answer came from.
Lastly, AlphaSense leverages the structured data it has extracted from documents, such as key performance indicators (KPIs) from earnings calls. This structured data allows them to compare and validate the accuracy of the generative AI answers.
“So with those validation steps, we’re able to reduce that hallucination risk down to extremely minimal,” he said.
The Future: AI Personal Assistants
As for the future of generative AI in his company and industry, Ackerson said that in the short to medium term, it will lower the barriers to entry for knowledge professionals who need quick access to insights contained in complex documents. Looking further ahead, he sees generative AI evolving into intelligent assistants that help plan and organize a user’s days.
AlphaSense is already exploring the concept of providing personalized morning briefings, by summarizing relevant topics and companies of interest for a user. Eventually, he said, a user will be able to give the AI system tasks and instructions throughout the day, beyond just answering questions.
You will be able to interact with your AI assistant, Ackerson said, “much like you would a [human] assistant or an intern.”
