With DatabookGPT, you can ask any questions related to your account across various parts of the sales cycle, including:

• Account Management: Company-specific insights for your account research and planning.
• Strategic Narratives: Develop a plan to engage senior executives by addressing their urgent challenges.
• Sales Outreach: Customize outreach messages for faster deal closure with impactful messaging.
• Industry & Market Insights: Gain deep insights into trends and challenges in your customers’ industries.

In general, users tend to ask short informal questions that are at the top of their minds related to the above use cases. Take a look at some of our top recommended prompts here.

DatabookGPT is built for enterprise sellers to help them across their sales cycle by providing relevant, up-to-date trusted insights. The key advantages that DatabookGPT provides over ChatGPT (and other foundational LLMs) include:

• Trusted Insights: Databook utilizes Retrieval Augmented Generation (RAG) to anchor conversations in our verified data, enabling us to evaluate any response from the language model against our high-quality financial, firmographic, and buyer datasets.
• Proactive Nudges: Unlike ChatGPT and other Language Models (LLMs) that only respond to user prompts, DatabookGPT proactively suggests questions to ask, saving sellers time and ensuring they don’t miss key insights.
• Workflow Integrations: DatabookGPT is accessible not only through a browser, but also, in the near future, will be seamlessly integrated with key applications within sellers’ existing workflows.

Depending on your environment, we use various AI services, such as OpenAI or AWS Bedrock.

We use Enterprise APIs to generate responses. Data shared with AI service providers is not shared with their customers, made available to the service providers, and not used to train their models.

You can read more about data privacy with OpenAI here.

You can read more about data privacy with AWS Bedrock here.

A complete list of sub-processors for the Databook service can be found here.

No. Your data would not be used to train OpenAI models. Please see this information about data privacy with OpenAI.

We have built DatabookGPT as a Retrieval Augmented Generation (RAG) application to reduce hallucinations to near zero, and improve the accuracy and relevancy of DatabookGPT responses. In simple terms, the RAG app uses a foundational large-language model (LLM – for example GPT-4o) to understand the intent of the user’s query and plan how to answer it. DatabookGPT will then find relevant data to ‘ground’ its response from Databook’s high-quality datasets including premium financial and firmographic data and proprietary data such as strategic priorities and management intent. The LLM will then synthesize the data into a response for the user.

Relating to the proprietary and third-party datasets that are used to ‘ground’ DatabookGPT responses, Databook has measures in place to ensure high data quality when ingesting and processing data. For datasets such as company strategic priorities or financials we are able to trace their origins back to authoritative sources such as company investor documents, earnings transcripts or company filings. Key datasets are refreshed daily. Databook and its third-party data suppliers also have automated data checks to verify the accuracy and recency of the data used to inform DatabookGPT responses. Databook also has its own program of quality assurance checks by human analysts to provide additional reassurance.

This approach is different from conversational AI apps such as ChatGPT or Perplexity that rely on general web searches to answer some queries, especially those that relate to recent developments after the LLM was trained and so the answers won’t be found in the LLM’s training data. For example, for queries relating to company strategic priorities these conversational AI apps will look at sources beyond the company’s official documents (for example, media reports or SWOT analyses by MBA students), thus increasing the chance of inaccurate data being included in a response.