You Don't Have a Data Problem. You Have an Answers Problem.

Revenue teams have never had more dashboards. More pipeline views. More reports. And yet, the question that still stumps most sales leaders on a Tuesday morning is deceptively simple: What's actually going on in my forecast?

That gap between data and answers is exactly what a revenue answers platform should close. It is what we built Backstory to do. And last week, for the first time, we opened our roadmap and put our commitments in writing.

Here's what we covered.

What Is a Revenue Intelligence Platform?

A revenue intelligence platform automatically captures activity across email, meetings, and calls, then surfaces specific, actionable answers about deal health, pipeline risk, and forecast accuracy. The goal is not more data. There are fewer steps between a question and the right answer.

Why We Changed Our Name

Kimberly Gordon, our Head of Marketing, kicked things off with the story behind the rebrand from People.ai to Backstory.

The short version: People.ai was a brand she had tremendous respect for, but it was causing confusion. For new buyers trying to understand what we do. For champions trying to explain us internally. For anyone who needed to quickly grasp the value.

The name change wasn't cosmetic. It was about immediate clarity.

"We give you the story behind every deal, every forecast, and every number."

That is what the name Backstory captures. And it signals something about how we think about the market. Revenue intelligence, a term we have all heard approximately a thousand times, does not actually get you what you need. It gives you more data to interpret. We are building something different: revenue answers. Specific, traceable, actionable answers that show up where you are already working, not in another dashboard you have to go find.

One principle Kimberly made clear: you will not see "AI-powered" anywhere on our website. We are not here to add to the noise. We are here to show why our answers are materially better, and prove it.

Three Phases of Getting from Pipeline Data to an Answer

Sonya Subramaniam, our VP of Product, walked through the roadmap in a framework she called the three phases of getting to an answer:

1. Know Your Situation 2. Know What to Do 3. Make the Call

This isn't just a tidy slide structure. It's a real sequencing argument. You can't make a good call if you skipped the first two steps. And a lot of the tools in this market are rushing to automate the third step without building enough intelligence into the first and second.

Here's what's coming across each phase.

Phase 1: Know Your Situation — Pipeline Inspection and Activity Coverage Build the Foundation

This phase is about building the most complete, accurate picture of what is actually being said between your team and your customers. Better pipeline inspection starts with better data coverage. 

Expanded transcript coverage. We are adding Gong and Chorus integrations, plus support for manual transcript uploads. More sources means a more complete picture, which means better answers downstream.

Meeting categorization. We are automatically categorizing meetings: QBRs, demos, procurement calls, so the platform understands what kind of conversation happened, not just that one did. That context matters for how answers get constructed. 

Smarter activity matching. Our matching engine already connects activities to opportunities and accounts. We are making it more configurable, more adaptive, and capable of learning from corrections at the user level, not just org-wide. When we match better, we have a more truthful picture. When we have a more truthful picture, the answers are better. 

Signal health checks. When you are building custom signals in Backstory, you will now get feedback on the quality of what you are building. If you are passing in too many data sources, or if your instructions could be clearer, we will flag it. Tuning an answer to your specific business should not mean sacrificing objectivity.

Signal rationale. This one matters. When a signal gives you an answer, you will be able to see exactly how it got there: what it looked at, what it weighed, what it concluded. For decisions that end up in front of your CRO or your board, you need to be able to trust where the answer came from. 

Phase 2: Know What to Do — AI Sales Forecasting and Deal Risk Drive Action

This is where we move from "here is what happened" to "here is what you should do about it." This phase is where AI sales forecasting does its most important work: turning pipeline signals into decisions. 

Upside potential in forecasting. We spend time on deal risk signals. That is important, but it is still reactive. We are adding a view that shows deals you might be able to pull forward based on signals like champion engagement, procurement activity, and buying group construction. Not just what is at risk. Also what is going well.

Weekly forecasting briefings. A weekly summary delivered to your email, slack - or wherever you work. Commit movement, best case changes, key deal milestones. Not just the numbers, but the why behind them. The answer, not the spreadsheet. This is forecasting best practices made automatic: the right information, at the right time, without anyone having to build it. 

Situation Search. Already in beta. When a rep is facing a tricky situation: a late-stage procurement blocker, a skeptical stakeholder, a deal showing the patterns of why deals stall, they can describe the situation and Backstory surfaces how similar situations played out across your entire deal history. What worked. What did not. 

This is how you identify at-risk deals before they go dark and scale institutional knowledge at the same time. Not by writing a playbook nobody reads. By surfacing what actually happened, when it actually mattered. 

Seller daily view. Reps start their day with answers, not admin. Who am I talking to next? What does the deal need right now? What should I have already done? The goal is not to replace all the tools in their stack. It is to reduce context-switching so they can spend more time on the conversations that move deals.

Phase 3: Make the Call — Pipeline Risk Management Powers Decisions

This phase is about making execution faster, not just decisions smarter. Pipeline risk management closes the loop between what the forecast shows and what the frontline does. 

Action orchestration from answers. As an example: when a seller sees that a stakeholder is concerned and there is a strategy meeting coming up, they should not have to draft a de-risk email from scratch. With one click, Backstory drafts it: pulled from the actual conversation context, matched to the right contact, ready to review and send. The seller stays in the flow. The action gets done. This is only one of the many answers Backstory provides - we power follow-up emails, CRM updates, actionable next steps, etc. all wherever you work.

Leader-to-seller assignment. Backstory’s Headlines help leaders to see risk, assign it to the right rep, and have it show up in that rep's prioritized action list. It closes the loop between forecast visibility and frontline execution.

MCP server improvements. Backstory's MCP server is how answers get into the tools people are already working in: Salesforce, Slack, wherever. As new capabilities ship, they become MCP tools too. Including Situation Search, meeting prep, follow-ups, and deal monitoring -aAll triggered from wherever your teams already work.

What's Already Working in the Wild

Haya Kamal, who leads our CSM team, walked through two customer stories.

Red Hat uses Backstory to unify their forecasting process across 5,000 sellers globally. They needed answers going into forecast calls to stay current, not stale data from last week's CRM update. By running MEDDPICC scoring and evaluation through Backstory, they saw 50% higher win rates on scored deals and reclaimed over 1,000 hours of manager time previously spent chasing down and revalidating numbers. That is what it looks like to improve forecast accuracy at scale using a sales forecasting software built on complete activity data. 

AMD is transforming from a largely partner-led motion to one with much more direct end customer engagement. The question they needed answered: what does our go-to-market engagement actually look like right now? Backstory gave them full visibility into how much time sellers were spending with partners versus end customers, which accounts had real engagement gaps, and what activities were actually generating pipeline. Pipeline reviews that used to take two weeks were reduced to a single day. Since launching Backstory, they saw an 81% month-over-month increase in meetings on their top 100 focus accounts. That kind of sales performance analytics is what turns a pipeline review from a guessing exercise into a decision-making session. 

What This Means for You

If you are already a customer: reach out to your CSM, let them know what parts of this roadmap resonated, and sign up for early beta access, especially for Situation Search within our MCP server.

If you are evaluating Backstory: the customer stories above are a good place to start. What Red Hat and AMD needed was not more data. They needed answers they could act on. That is what we are building as a revenue answers platform. 

The recording of the full webinar is available on demand. Worth an hour.

Watch the Recording →