Backstory Launches Situation Search, Helping Sales Teams Win More Deals by Learning From Their Own Past Wins

SAN FRANCISCO, CA — July 21, 2026 — Backstory, the AI platform for revenue teams formerly known as People.ai, today announced Situation Search, a new capability that lets sellers describe a deal situation in plain language and surfaces the most comparable deals from their own company's history, along with what their team did next and how it ended. Situation Search is available now to new customers as well as existing ones through the Backstory MCP server.

Most sales organizations have already solved the deal situations their reps run into today, whether it's a procurement delay, a competitive threat, or a late-stage objection. The problem is that the knowledge of what worked isn't reachable in the moment a rep needs it. New reps build judgment slowly, through their own losses. Experienced reps carry context in their heads that doesn't transfer when they change roles or leave the company, and managers can't sit in on every stalled deal to pass it along. Many AI tools built to close this gap generate recommendations that reps can't trace back to a real outcome within their company, which limits how much reps trust and use them. Situation Search closes that gap differently, by grounding every result in the team's own deal history rather than a model's prediction.

"Our reps already know the answer to a tough deal usually exists somewhere in our history, but finding it has meant stopping to ask around or dig through the CRM. Situation Search puts that answer in front of the rep in the moment, with the context of what we did and how it played out,” said D.J. Cox, Senior Vice President of U.S. Sales at TransUnion. “For a team managing our deal volume, that's the difference between a rep guessing and a rep applying what we've already learned works and it means our customers get faster, smarter solutions to the problems they came to us to solve.”

Situation Search works inside a seller's existing chat client, with no new tool or workflow to adopt. How it works:

• It builds the situation description from the activity record. Rather than asking a seller to write a search query, Backstory assembles a structured description of the deal situation directly from emails, calls, meetings, and CRM data, then matches that against the organization's deal history. 
• It returns specific comparable deals, ranked by similarity. Situation Search returns up to four ranked deals, each with a deal profile, a summary of the similar situation, the actions the team took, and the outcome. The rep sees the evidence and decides what applies to their deal.

In practice, a seller can type something like “we're stuck at procurement and legal wants custom contract terms” into their MCP chat client. Situation Search returns the most comparable deals from within their own organization, ranked by situational similarity, scoped only to deals the user already has CRM visibility into.

“Most of the AI that's been shipped to sales teams asks a rep to trust a recommendation they have no way to see into. That's a hard sell for someone whose job is to read a room and make a judgment call, and it's a big reason so many of these tools stall after the pilot,” said Jason Ambrose, CEO of Backstory. “We built Situation Search to work the other way around. It doesn't tell a rep what to do. It shows them the deals their own company has already won in situations like theirs, and lets them decide what applies. That's a kind of AI reps actually trust, because they can see exactly where it came from.” 

Situation Search enables users to retrieve deals through vector search and have large language models describe situations and score similarity. The more important design decision is what gets matched: a structured situation description built from the underlying activity record, rather than the model doing the matching.

Users can retrieve and rank deal history using a series of large language models and other techniques working together. It is a tool a rep uses, with the rep staying in charge of the decision. The capability is designed to narrow the gap between what's actually happening in a deal and the data feeding any recommendation, by reading the full activity record rather than a handful of CRM fields.

To learn more, please visit: https://www.backstory.ai/platform/mcp 

About Backstory

Backstory is the leading AI answers platform for sales teams. With more than a decade of training AI models on billions of sales interactions, Backstory enables modern sales teams to ask questions and get the right answer in the moment directly within their existing AI workflows. Sales leaders from companies like Red Hat, Five9, Palo Alto Networks, Iron Mountain, TransUnion, Randstad and others rely on Backstory to get trusted answers wrapped in real business context helping them orchestrate deals more effectively and drive revenue.

By connecting every signal to the right accounts and opportunities, Backstory shows teams exactly what is happening and what to do next. Customers across industries including enterprise, cybersecurity, data and analytics, and defense use Backstory to drive trillions in pipeline and revenue.

Recognized by Gartner and Forrester, the Inc. 5000, Y Combinator Top Companies, and the Forbes AI 50, Backstory is based in San Francisco and backed by ICONIQ Capital, Andreessen Horowitz, Lightspeed Venture Partners, Akkadian Ventures and Mubadala Capital.

Learn more at Backstory.ai and follow us on LinkedIn.