From 3% sampled QA to 100% coverage across a mid-market BPO — AI quality operations
A mid-market BPO's QA program sampled 3% of interactions and clients still argued the scorecards. pronix.ai stood up 100% AI-scored QA with human calibration and evidence linking — 4x more coach-worthy findings and a 22% CSAT lift across the top three programs.
Mid-market BPO, 8,500 seats · Genesys Cloud CX · AWS Bedrock · Snowflake
- Client
- Mid-market BPO, 8,500 seats
- Industry
- BPO
- Platform
- Genesys Cloud CX · AWS Bedrock · Snowflake
*Representative outcome; results vary by client, scope and platform configuration.
The challenge
The QA sample missed the interactions that actually mattered, coaching was reactive, and clients disputed 1 in 6 scores. Every new program required a scorecard rebuild in a spreadsheet.
Our approach
Scorecard-as-config
Modeled every client scorecard as versioned config — rubric, weights, evidence rules — so a new program went live in a day.
LLM scoring with citations
Every score linked to the transcript span that produced it — no black-box grades in a client review.
Human calibration loop
5% of interactions dual-scored by a QA analyst; drift monitored per rubric and re-trained monthly.
Coaching queue
Findings routed to supervisors as one-minute coaching cards tied to the exact call moment — not a weekly PDF.
Client scorecard portal
Read-only portal for the client with drill-down to evidence — disputes dropped by design.
Stack assumptions
The reference stack behind this program. Assumptions are what pronix.ai brought in on day one — swap-outs are common, and the implementation summary explains where the substitutions cost time or accuracy.
| Layer | Component | Assumption on day one |
|---|---|---|
| Contact center | Genesys Cloud CX | Voice + digital recording, transcripts and metadata streamed to Snowflake via Genesys AppFoundry connector. |
| LLM / scoring | AWS Bedrock (Claude 3.5 Sonnet + Haiku) | Haiku for classification / adherence; Sonnet for open-ended rubric items with citation extraction. |
| Scorecard config | Custom scorecard-as-code service | Rubrics stored as versioned YAML per client; new program live in one day with a scorecard PR. |
| Data platform | Snowflake + dbt | Every score, evidence span and dispute recorded; longitudinal drift tracked per rubric per program. |
| Coaching surface | Genesys native + Slack cards | One-minute coaching cards routed to supervisors with deep-links to the exact call moment. |
| Client portal | Retool (read-only) on Snowflake | Clients see scores, evidence and dispute status; SSO via Okta or Azure AD per client. |
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Brief details →Illustrative case study. Scenarios, metrics, quotes and client details are representative composites based on Pronix engagements and industry benchmarks unless a named client is shown with written consent. Outcomes vary by client, scope, data quality and platform configuration. Nothing on this page is a guarantee, warranty or professional advice. See our Terms of Use for the full disclaimer.
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