DatabookGPT takes the power of Databook to the next level, delivering new trusted insights, proactive guidance, and personalized assets through a conversational AI interface you can access where you already work. Initially available via a conversational AI web application, DatabookGPT will also be available through integrations within sellers' existing workflows, including collaboration apps such as Slack and AI apps such as Microsoft Copilot for Sales in the near future. With DatabookGPT, sellers can quickly find the insights they need to save time on account research, pipeline generation, outreach creation, customer meeting preparation, and much more.

DatabookGPT helps sellers secure the right meetings by identifying the best accounts and buyers and the most relevant use cases and solutions to discuss with buyers. It also helps sellers prepare for those meetings so they can execute effectively in every customer meeting.

DatabookGPT, through an easy-to-use conversational interface, offers insights that help sellers increase opportunity conversion rates, generate more pipeline, and grow the average contract value (ACV) of their opportunities.

We believe that DatabookGPT's conversational AI allows it to combine different data points into sophisticated yet easy-to-understand insights perfectly suited to the use cases in which sellers have already used Databook. We have proof points showing that the use of the Databook web app is associated with higher ACVs and more pipeline for accounts where it is used, and we believe that DatabookGPT will offer even more value for enterprise sellers and sales organizations.

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:

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.

We have designed DatabookGPT to help enterprise sellers better understand their customers and prospects, empowering them to be more productive and effective in their roles. We believe that DatabookGPT, when used for its intended use cases, has a positive impact on our users, customers, and buyers who interact with them.

Databook is intended for a specific set of business use cases relating to enterprise sales. As such, some broader concerns about bias and misinformation in generative AI are less relevant. The design of our retrieval-augmented generation (RAG) application ensures highly relevant and accurate answers. We also have guardrails in place to mitigate against misuse of the product and have tested prompt injection attacks where a bad actor tries to misuse the product.

Databook follows industry best practices in relation to information security and data privacy. You can find more information about specific controls on our security portal. Our security controls are continuously monitored and audited for our SOC 2 Type II certification. A copy of our SOC 2 report is available on request.

Databook’s most recent annual SOC 2 Type II report was published before DatabookGPT was released in beta. This means the system description for our most recent SOC 2 certification does not include certain elements of the DatabookGPT product. However, DatabookGPT shares a common infrastructure with the existing Databook product, including databases where customer data is stored, which are already covered by our most recent SOC 2 Type II report.

We are also already taking steps for DatabookGPT to be included in our next annual SOC 2 report, including:

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 comply with data privacy legislation such as GDPR and CCPA. Databook completed a Data Protection Impact Assessment (DPIA) for DatabookGPT to identify, analyze, and minimize privacy risks.

Databook’s data is hosted by AWS in the us-east-1 region.

You can contact the Databook Privacy & Security team at [email protected].