Eval directory

Evals for Hyperbound

Eval coverage for Hyperbound, mapped from its public product surface.

About Hyperbound

Hyperbound is an enterprise sales enablement platform that pairs AI buyer roleplays with scoring of real sales calls and deal coaching. It is organized into three parts: Practice (AI roleplays and scorecards), Perform (real call scoring, deal coaching, CRM auto-fill, conversation intelligence), and Kota Activate (an ask-anything agent plus workflow automations). It offers a free tier with prebuilt roleplays and enterprise plans with custom bots, scorecards, analytics, and CRM/call-recorder integrations.

Industry

AI sales roleplay, call scoring, and coaching platform

Website

www.hyperbound.ai

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Coverage map

What would you measure for Hyperbound?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

AI Buyer Roleplay Fidelity

Whether the simulated buyer behaves like a real prospect: staying in persona, reacting dynamically to what the rep says, and holding the stated scenario, language, and tone settings across a live call.

“Practice real conversations with roleplays built from your sales methodology, historical deals, and top-performing reps.” www.hyperbound.ai

Mapped capabilities

4 capabilities

Persona and scenario adherence
Bot holds the configured role, company, call type (cold call, discovery, objection, demo), language, and personality across turns.
Dynamic buyer reactions
Objections, brush-offs, and follow-up questions respond to what the rep actually said rather than replaying a script.
Multi-party and screen-share roleplays
Multiple stakeholders keep distinct viewpoints; screen-share roleplays reference what is actually on screen.
Bot creation from source material
Bots built from a LinkedIn profile, product/ICP inputs, or the buyer bot variation builder reflect those inputs faithfully.

Scorecard Grading and Coaching

Whether AI scorecards grade a conversation against a customer's methodology consistently and whether the resulting coaching is specific, actionable, and traceable to moments in the call.

Mapped capabilities

4 capabilities

Criterion-level grading accuracy
Each scorecard line (e.g. MEDDPICC elements, social proof, pain-to-solution linkage) is marked hit or miss based on transcript evidence.
Evidence grounding
Scores and summaries cite the actual rep utterance rather than inventing statements the rep never made.
Coaching feedback quality
"What to do differently" guidance targets the missed criteria and suggests usable phrasing.
Scoring consistency
Repeated grading of the same call and same scorecard produces stable results.

Illustrative example

Input: Discovery call transcript where the rep asks about timeline and budget owner but never asks how success will be measured. Grade against a MEDDPICC scorecard.
Expected behavior: The Metrics criterion is marked as not met, and the coaching note tells the rep to establish a quantified success benchmark. Criteria the rep did satisfy are not downgraded.

Real Call Analysis and Deal Coaching

Perform-tier handling of recorded customer calls: scoring real conversations, summarizing deals, surfacing risk and coaching moments, and drafting follow-ups.

Mapped capabilities

4 capabilities

Real call scoring on custom scorecards
Customer-defined scorecards apply cleanly to messy real-call transcripts, including crosstalk and multi-speaker calls.
Deal and call summaries
Summaries capture commitments, next steps, and stated blockers without fabricating outcomes.
Deal risk and coaching moments
Flagged at-risk deals and manager coaching moments are supported by signals present in the conversations.
Automated follow-up drafts
Drafted follow-up emails reflect what was actually discussed and agreed.

CRM Intelligence and Integrations

Auto-fill and sync behavior against Hubspot, Salesforce, and Dynamics, plus capture from call recorders and dialers such as Gong, Zoom, and Orum.

Mapped capabilities

4 capabilities

Conversation auto-fill correctness
Extracted field values match the call; ambiguous or unstated fields are left blank rather than guessed.
Deal sync integrity
Writes land on the right record and do not overwrite existing CRM data without cause.
Call capture coverage
Native recorder and third-party recorder/dialer sources both produce scoreable calls.
Webhook and event delivery
Real call webhooks fire on the documented events with consistent payloads.

Illustrative example

Input: Call transcript in which the buyer names a decision maker and a competitor but never mentions a contract close date. Auto-fill the opportunity record.
Expected behavior: Decision maker and competitor fields are populated from the transcript, while the close date field is left empty rather than inferred, and no invented date is written to the CRM.

Kota Agent and Automations

The ask-anything agent over calls, emails, deals, and roleplays, plus trigger-based automations and creation assist for bots, scorecards, and reports.

Mapped capabilities

4 capabilities

Question answering over revenue data
Deal Q&A, coaching Q&A, and trend questions answer from available data and say so when the data is not there.
Creation assist
Natural-language requests produce valid roleplay bots, scorecards, dashboards, and reports matching the request.
Automation trigger and routing
Trigger, action, and destination fire as configured; prebuilt automations behave as described.
Surface parity
The same question asked in Slack, mobile, or in-app yields consistent answers.

Plan Entitlements and Access Boundaries

Whether the Free, Practice Enterprise, and Perform Enterprise boundaries hold, and whether team-scoped data stays scoped to the right users and managers.

Mapped capabilities

4 capabilities

Free-tier limits
Prebuilt roleplays and example scorecards work without login friction; Perform-only and customization features are not reachable.
Practice vs Perform gating
Real call scoring, deal coaching, CRM intelligence, and conversation intelligence stay gated to Perform.
Role-scoped visibility
Reps, managers, and leaders see the calls, scorecards, and analytics appropriate to their role.
Graceful gating messaging
Blocked features explain the plan requirement instead of failing silently or erroring.

Coverage is mapped from Hyperbound's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Hyperbound test?+

The coverage map is generated from Hyperbound's own public product surface (AI sales roleplay, call scoring, and coaching platform): 6 scoring areas — AI Buyer Roleplay Fidelity, Scorecard Grading and Coaching, and Real Call Analysis and Deal Coaching, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Hyperbound evals scored?+

Every case generated for Hyperbound — across AI Buyer Roleplay Fidelity and Scorecard Grading and Coaching and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Hyperbound library include?+

The full Hyperbound library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Persona and scenario adherence and Dynamic buyer reactions under AI Buyer Roleplay Fidelity); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Hyperbound or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Hyperbound areas and set them up in a Corsac workspace, where you can run every test case against Hyperbound or your own agent with your own data.