QA data exists, but leaders do not know what to do next.
I turn review data into clear trends, actions, and ownership.
Quality leader • AI-supported QA • CX analytics
Brian Geiser
CX, Quality & AI Operations Leader
I help contact centers and service teams run with clearer QA rules, faster feedback loops, and reporting leaders can trust.
Problems I solve
From QA signals to action
Employer pain points
AI QA output exists, but teams do not trust the scoring.
I build validation and calibration workflows that make the output usable.
Coaching is happening, but it is not tied to defect reduction.
I connect QA findings, coaching, and follow-up into measurable improvement.
Calibration varies across reviewers, teams, or vendors.
I create a governance model that keeps scoring consistent and defensible.
Customer dissatisfaction is measured but not translated into root cause.
I link customer signals to operational themes and action plans.
What makes this different
Quality systems leaders can actually use
Differentiator
Most QA programs report what happened. My focus is building quality systems leaders can use while the work is still actionable: clear scoring rules, AI validation, calibration governance, coaching feedback loops, and reporting that turns customer interactions into action.
Best fit roles
Roles and environments that match this work
Recruiter focus
Target roles
Quality Manager
AI Quality Operations Manager
CX Performance Manager
QA Governance Lead
Contact Center Performance Manager
Implementation / Process Improvement Manager
Best fit environments
Contact centers, healthcare, insurance, telecom, customer operations, BPO/vendor operations, and teams implementing AI-supported QA or conversation analytics.
Representative outcomes
Representative outcomes from QA transformation, AI-supported evaluation, reporting automation, and coaching-loop improvement work.
How I work
How I turn quality signals into action
Operating model
Signal
Identify QA, VOC, CSAT/NPS, transfer, detractor, and risk patterns.
Validate
Confirm the signal through review, calibration, and exception handling.
Diagnose
Translate findings into root cause, customer impact, process gaps, or coaching needs.
Act
Assign ownership and connect findings to coaching or process improvement.
Measure
Track whether the action reduced defects or improved outcomes.
What this proves
Closed-loop quality operating model
Operational summary
The operating model demonstrates how quality signals move from detection to validation, root cause, ownership, coaching, and follow-up. It shows how QA performance, customer sentiment, coaching opportunities, transfer drivers, and risk themes can be translated into leadership-ready action without exposing client, employee, or customer data.
Interactive AI quality dashboard
Explore the Quality & AI Operations Dashboard
This dashboard is a sample 90-day operating view that connects QA performance, risk coverage, customer sentiment, coaching opportunities, and transfer drivers into leadership-ready action.
QA Score
Risk Coverage
Coaching Opportunities
Detractor Themes
94.2%
QA score
92%
risk coverage
18
coaching opportunities
3
priority themes
Selected impact
Representative outcomes from QA transformation, AI-supported evaluation, reporting automation, and coaching-loop improvement.
Key metrics
These metrics reflect operational quality work, not theory.
- Validated AI-supported QA outputs with accuracy above 95%
- Improved QA productivity by approximately 2x
- Reduced manual review and reporting time by more than 50%
- Built coaching, calibration, and feedback loops connecting QA findings to frontline performance and action plans
95%+
validated AI QA accuracy
2x
productivity improvement
50%+
manual reporting reduction
Coaching
feedback loops tied to performance
Portfolio highlights
Selected examples of quality systems, dashboards, and operating models
Body of work
Portfolio examples are anonymized and representative, designed to show operating approach, quality thinking, and leadership reporting without exposing client, employee, or customer data.
Signal report
Quality Signal Insight Report.
Signal, trend, impact, owner, and action. Used to convert QA findings into leadership-ready decisions.
Scorecard
Executive QA scorecard.
Translates evaluation results into trends, risks, and next steps. Helps leaders focus on what changed and what to do next.
Case study
Quality transformation case study.
Shows how AI-supported scoring can be governed through human validation and calibration.
The value was creating a trusted operating rhythm where AI output, human validation, calibration, coaching, and leadership reporting reinforced each other.
Governance model
Governance model.
Defines roles, calibration routines, exception handling, and review cadence. Shows how QA governance stays consistent as volume and complexity increase.
Dashboard
Interactive dashboard.
Shows QA trends, risk coverage, coaching opportunities, and leadership reporting in one view.
Explore Dashboard90-day plan
90-day plan.
Outlines how quality transformation starts, stabilizes, and scales. Built to guide a 90-day implementation path.
Contact
Let’s connect.
Open to Quality Manager, AI Operations Manager, and CX Transformation roles.
If my background fits your team, let’s talk.