Agent assist across 1,200 BPO seats — a co-sell success
A global BPO wanted to introduce agent assist across three of their largest client programs without disturbing the existing SLA structure. pronix.ai delivered the platform, the change program and the client co-sell motion — 22% AHT reduction, 14% QA lift, and a repeatable playbook the BPO now sells into their own book.
- Client
- Global BPO, 1,200 seats across three programs
- Industry
- BPO
- Platform
- NICE CXone · Kore.ai · Azure AI Foundry
*Representative outcome; results vary by client, scope and platform configuration.
The challenge
Three programs on NICE CXone, three different clients, three different QA rubrics — and a contractual expectation that any AI rollout would not shift the SLA calculus. The BPO also wanted to keep the resulting IP as a differentiator for future client pitches.
Our approach
One platform, three tuning tracks
A single Kore.ai and Azure AI Foundry deployment with per-client prompt libraries, knowledge sources and QA models. Shared infrastructure, isolated content.
SLA-safe rollout
Shadow-mode for 21 days per program, then A/B by team for 21 more. No agent went to production suggestions until each program hit the pre-agreed guardrails.
QA lift, not QA replacement
Automated QA scored 100% of interactions and flagged the bottom decile to human reviewers — QA team capacity became a lever, not a constraint.
Co-sell packaging
We packaged the platform, the change model and the results into a two-page co-sell brief the BPO now uses in every new-logo pitch.
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 | NICE CXone | Voice + digital already live across all three programs; agent desktop is CXone-native. |
| Conversational AI | Kore.ai XO | One tenant, per-program bots and knowledge sources; shared intent taxonomy for reporting. |
| LLM / evaluation | Azure AI Foundry (Azure OpenAI) | GPT-4o-class model for suggestions; cheaper 4o-mini for classification; prompt caching enabled. |
| Knowledge | Per-program SharePoint + curated FAQ | Grounding sources versioned per client; retrieval scoped by tenant. |
| QA & eval | Azure AI Foundry evaluations + human calibration | 100% automated QA; 5% analyst dual-score for drift monitoring. |
| Data & attribution | Snowflake + BPO's existing BI | Per-program AHT, QA, containment and cost surfaced weekly to client ops reviews. |
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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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