AI-driven simulation and assessment used to hire better, ramp faster, and validate readiness before agents touch real customers or patients.

Eliminate:

  • Long ramp times for new hires

  • Inconsistent readiness at go-live

  • Hiring people who “interview well” but fail in production

  • High early attrition (first 90 days)

What’s included:

  • Scenario-based simulations (calls, chats, role-plays)

  • Pre-hire and post-hire readiness scoring

  • Risk-based scenario design (high-impact interactions)

  • Performance baselines by role and queue

Outcomes:

  • ↓ Ramp time by 20–40%

  • ↓ Early attrition

  • ↑ Confidence and consistency at go-live

A modern QA + analytics layer that turns interactions, behaviors, and activity into objective, defensible performance insight—not subjective scorecards.

Eliminate:

  • Manual QA that doesn’t scale

  • Inconsistent scoring across evaluators

  • Little correlation between QA and real outcomes

  • Leaders “flying blind” on performance drivers

What’s included:

  • AI-assisted QA and speech/text analytics

  • Targeted evaluation frameworks

  • Behavior-to-outcome correlation

  • Performance dashboards for leaders and coaches

Outcomes:

  • ↑ QA coverage (5–10x without more staff)

  • ↑ Score consistency

  • Clear identification of top/bottom performance drivers

Eliminate:

  • Managers overwhelmed by coaching expectations

  • Coaching that’s generic or inconsistent

  • No proof coaching actually changed behavior

  • Training, QA, and ops working in silos

Closed-loop coaching that tells agents exactly what to practice, how to practice it, and proves improvement—without relying on managers’ bandwidth.

What’s included:

  • Personalized coaching recommendations

  • Simulation-based practice tied to real QA gaps

  • Learning paths by role and proficiency

  • Coach effectiveness measurement

Outcomes:

  • ↑ Speed to proficiency

  • ↑ Agent engagement

  • ↑ Measurable behavior change tied to QA