From secondhand updates to firsthand data.

Overview

Aidoc sells clinical AI into health systems - a highly regulated environment with a sales motion that happens mostly in person, on-site at hospitals. Most of their reps came from medtech companies like Philips and GE, not SaaS, which created a data problem. Years of incomplete signal, a CRM updated by hand, no call recordings, and an AI layer trying to run analysis on top of all of it. RevOps was making decisions based on whatever reps remembered to log. Nick Keeslar, AVP Revenue Operations, joined Aidoc knowing the problem, and built Backstory into a system to capture what was actually happening in every deal - then run RevOps like a lab on top of that data.

[testimonial]

[list#base]
Before Backstory

• CRM data biased and incomplete - reps manually logged Salesforce fields after in-person visits
• No call recordings - RevOps relied on reps' secondhand account of what happened on-site
• AI tools bought and deployed as one-time projects, not maintained or improved
• Individual experiments stayed siloed, with no mechanism to share or scale what worked
• Reps required to leave their workflow to find AI-generated insights
• Pipeline calls relied on seller narrative, with limited independent visibility into deal health
  [/list]

[tag#Use case 01] Data completeness

Building the corpus before the AI can be useful

Aidoc's environment made automated capture hard. Health system sales happen in hospital corridors and conference rooms, not on Zoom. For four years, Keeslar pushed for call recording without leadership buy-in, leaving Backstory to run on email and calendar activity plus whatever reps logged by hand.

[testimonial]

Within the last few months, Aidoc finally got that buy-in. Call recording became a third signal source alongside email and calendar, meaningfully expanding what the AI could work with - engagement scoring, stakeholder coverage, and deal analysis could now run on a fuller picture of what was happening in the field.

[testimonial]

[list#brand]
AFTER BACKSTORY

• Backstory captures email, calendar, and call signals automatically
• AI insights grounded in a more complete corpus of deal activity
• Foundation in place to run meaningful analysis across the pipeline
• Engagement scoring and stakeholder coverage reflect what's actually happening in the field, not just what's logged
  [/list]

[tag#Use case 02] The RevOps lab model

Testing, iterating, and shipping - instead of buying and hoping

Buying AI tooling, configuring it, and expecting it to work doesn't hold up in Keeslar's view - these tools need continuous building and maintenance.

[testimonial]

His answer was to run RevOps like a lab, with every team member's OKRs including a lab contribution: testing agents, validating outputs, improving what shipped to the field. Every project ran on the same foundation - the activity data Backstory captures automatically. When a seller built their own deal-quality agent on top of that data, Keeslar brought it into the lab instead of letting it stay a one-off, and the team improved it together.

[testimonial]

[list#img]
AFTER BACKSTORY

• Every RevOps team member has lab contribution goals built into OKRs
• Sellers who build their own agents get folded into the lab to improve them
• Backstory's captured activity data is the foundation every lab project builds on
• CRO gets directionally accurate answers in minutes, not days
  [/list]

[tag#Use case 03] Rep adoption without the fight

Pushing insights to where people already work

Getting reps to change how they work is the hard part of AI adoption, not the technology itself.

[testimonial]

Aidoc built for the ordinary rep to get extraordinary results: prebuilt prompts instead of asking reps to write their own, insights pushed to Slack and email instead of buried in a separate tool, next actions surfaced in the workflow reps were already in. When Backstory suggested a next action through the workflow instead of a manager assigning it, reps responded without the usual resistance - the data was telling them what to do, not a person. Reps started working two or three contacts per deal instead of one, and deal cycles started to shrink. The same shift showed up in pipeline governance: instead of extracting information from sellers to validate stage and risk, leaders could see engagement, stakeholder coverage, and activity patterns directly. Backstory became a neutral third-party signal, and pipeline calls moved from interrogation to problem-solving.

[list#accent]
AFTER BACKSTORY

• Insights pushed to Slack and email - no new tab, no new workflow
• Deal reviews start with engagement data and activity, not just rep narrative
• Next-action suggestions get acted on because they come from data, not a manager
• Pipeline reviews shift from "is this deal real?" to "what gets this deal back on track?"
  [/list]

What Aidoc achieved

The data is more complete than it's ever been. The CRO gets answers in minutes that used to take a week. Reps work two or three contacts per deal because the data tells them to, not because a manager asked. Aidoc didn't buy a transformation - they built a foundation, and the foundation is real. Keeslar's view of the RevOps role has shifted from report-maker to lab operator: the question isn't how fast you can deliver a dashboard, it's what you can build that changes how the team works.

[testimonial]

‍