Summit Audience Segments

AI training & evaluation

Real conversations. Real outcomes.

Healthcare calls that started from a script and went wherever real patients took them—each linked to the disposition and fulfillment that followed.

The conversation layer

~15M

condition-specific inquiries with call-attempt history

~2.15M

inbound transfers—each one a connected call

Scripted

every call run from a script—recorded as it actually went

Figures are approximate. Recordings exist for answered calls; unanswered attempts carry status data only. Audio hours, transcript coverage, and year-by-year volumes are confirmed during scoping.

Linked end to end

One journey, five connected signals.

  1. 01

    Inquiry

    Condition-specific interest from supply marketing.

  2. 02

    Call attempts

    Timestamped attempts and contact status.

  3. 03

    Conversation

    Scripted call, recorded as it actually unfolded.

  4. 04

    Disposition

    Call-center and pharmacy status outcomes.

  5. 05

    Fulfillment

    Shipped, refilled, or stopped—over time.

Example dispositions

No answerNot interestedVerifying insuranceWaiting for RxRefill too soonShipped

Why it matters

Scripts tell you the plan. Recordings show what happened.

Scripted data alone doesn’t teach an agent what to do when the plan breaks. This corpus pairs the script with the real call and the outcome—so adherence, deviation, and resolution are all observable.

01

Agent training on real deviations

Every call began from a script. The recordings show where real conversations departed from it—and how they were resolved.

02

Playbook-adherence evaluation

Compare the intended script against the actual call, with the downstream outcome as the label.

03

Outcome-labeled conversation data

Link a conversation to what happened next: insurance verification, waiting on a prescription, shipped, or not interested.

04

Longitudinal journey modeling

Multi-year, often multi-product patient journeys from first inquiry through refills, subject to de-identification.

Commercial structure

Archive, feed, or benchmark.

  • Archive license

    Defined slice of the historical corpus for training or evaluation.

  • Continuing feed

    Recurring delivery of newly de-identified conversations and outcomes. Today, close to 100% of inbound calls are answered—coverage the feed carries forward.

  • Evaluation-only

    A held-out benchmark set used to test models, not train them.

Before anything is licensed

  • Expert-certified de-identification of audio, transcripts, and linked records.
  • Spoken identifiers removed; voice handling agreed per use.
  • Dates, locations, and prescriber links generalized to limit re-identification.
  • Permitted uses—training, evaluation, retention—written into the license.
See all licensing models

Start with a defined use case

Scope an AI data pilot.

Tell us the model, task, and volume you need. We’ll confirm fit, formats, and the de-identification pathway.

info@summitaudiencesegments.com