Backstory (formerly People.ai) is an AI revenue intelligence platform for sales teams, positioned as revenue AI that goes beyond dashboards and directly answers the hardest questions in the sales pipeline: which deals are real, which are at risk, and what to do next.
I. Main Capabilities Backstory provides direct answers about every deal and surfaces them where salespeople already work. Specifically, this includes: seeing the true progress of the quarter, seeing the real situation for each customer, qualifying every deal and customer based on facts, asking questions in natural language and getting real answers, and gaining insight into activity, pipeline, and execution to support faster decisions and more predictable outcomes. The platform also supports seeing buyer behavior and accelerating action, identifying the risk in every deal (not just the one mentioned on a call), and proving marketing's impact.
II. How It Is Used and Integrations Backstory automatically captures activity from the tools teams already use, and salespeople do not need to manually log anything new; whether the data is updated in the CRM does not affect capture. The company lists connectable systems including Salesforce, Microsoft Dynamics, Oracle, Gmail, Outlook, Zoom, Teams, and Slack; activity is automatically captured and matched to the correct accounts, opportunities, and contacts. Users do not have to work in Backstory to get value: answers can be accessed in its web app, in the CRM, or in commonly used AI tools. Through MCP Server, answers can flow into the LLMs teams already use (such as Claude, ChatGPT, and Gemini), and can also work with CRMs and internal systems. Backstory supports connecting to multiple CRMs at the same time, aggregating sales activity from different geographic regions, subsidiaries, or verticals into a single view, making it easier to uncover cross-sell and upsell opportunities and maintain consistent sales actions.
III. Use Cases and Target Users Backstory is primarily aimed at leaders such as CROs and VPs of Sales who are responsible for the revenue number and need to report to the board when the number changes; the company explicitly states that it is not a sales rep productivity tool, but is built for leaders making commitment decisions, pipeline judgments, and driving revenue strategy. It also serves roles such as revenue operations. The company mentions that AVEVA uses Backstory to optimize its sales cycle and says it is trusted by 100,000+ salespeople.
IV. Differences from CRM, Forecasting Tools, and LLMs The company explains: the accuracy of CRM depends on what salespeople entercontent, prediction tools make predictions based on equally incomplete data, and call recording tools only capture calls rather than emails, meetings, or the gaps in between; none of these tools were built to give direct answers. Most prediction tools give predictions based on CRM data (that is, what salespeople remember to record), while Backstory's answers are based on what actually happened—every email, every meeting, and every call is automatically captured and matched to the correct deal, which is the difference between a model's guess and a defensible number. As for the approach of "pointing an LLM directly at the data," the official view is that a single question is fine, but facing thousands of users every day is different: the model builds the answer from scratch each time, the same question returns different answers to sales, managers, and the CRO, and the full context must be reprocessed and paid for every time; Backstory matches and distills the data once, and then returns one answer on demand.
V. AI Costs and Build-Your-Own Considerations Officially, consumption pricing is charged per token the model reads, and when one hundred salespeople ask the same question in one hundred ways, you pay one hundred times; Backstory generates an answer once, and every subsequent query returns that answer without rerunning the expensive call, so cost follows workload rather than the number of questions asked. For teams with engineers, officially, building the first version is quick, but the subsequent costs continue: connected platforms change their APIs, matching rules drift, and long after launch someone still needs to be responsible for maintenance; the Backstory team builds the custom parts together with the customer, so the workflow belongs to the customer without the maintenance burden.
VI. Speed to Get Started and Limitations Officially, most teams go live within 2–4 weeks, and there is no ramp-up period—Backstory analyzes two years of deal history on day one, so real answers are available immediately.
VII. Name Explanation Officially confirmed: Backstory is People.ai, and People.ai has now been renamed Backstory. The product is the same, the data infrastructure is the same, and it has simply been repositioned around what revenue leaders actually need (answers rather than more data).
VIII. Information Not Yet Provided The materials do not provide specific price figures, free trial policy, detailed technical specifications for each integration feature, data storage locations, or a compliance certification list and other details. This content is not yet provided.
