Hallucinated policy and refund statements cut by 81% after the first evaluation identified knowledge-base grounding gaps.
ENTERPRISE CONTACT CENTER / FINANCIAL SERVICES
Pre-launch testing cut AI-agent hallucinations by 81%.
A financial services organization had AI agents handling billing inquiries, policy explanations, and refund requests, but no consistent way to evaluate their performance. PivotX created a release process that measures accuracy and compliance, identifies knowledge-base gaps, and evaluates each new agent version before production.
Industry
Financial Services
Function
Customer Experience, Compliance
Metrics
42
Delivered In
Nine weeks
RESULTS
81% fewer hallucinations
FCR from 58% to 73%
First Call Resolution improved by 15 points after escalation errors and failed tool calls were identified and corrected.
Release gate delivered in nine weeks
Agent versions must clear the EvalIQ scorecard before production.
The organization can compare agent versions by metric and give compliance and audit teams the underlying evidence.
THE SITUATION
AI agents were serving customers without a consistent evaluation standard.
Traditional quality assurance relied on sampled interactions and subjective scoring. It could catch obvious failures after they happened, but it could not reliably detect drift or compare each new agent version against the same standard.
The contact center used AI agents to answer policy questions and process refund requests. Without consistent testing, the company could not confirm that the agents provided accurate information, followed policy, or escalated cases correctly.
01
No consistent evaluation standard across agent versions
02
Hallucination and policy compliance measured anecdotally
03
Subjective QA scoring that varied between reviewers
04
No release gate before new agent versions reached customers
THE WORK
What we built
01
A consistent standard for accuracy and compliance
Before working with PivotX, the organization had no reliable way to determine whether an AI agent had retrieved the correct source, followed the appropriate policy, or used its tools properly.
PivotX deployed an evaluation system called EvalIQ with 42 metrics covering retrieval accuracy, conversational coherence, tool use, escalation logic, and policy compliance. The scorecard places particular emphasis on hallucinations, policy violations, and inaccurate refund information. It also compares AI-agent performance with human-agent performance.
The organization gained a consistent standard for measuring agent performance and defining the threshold an agent must meet before release.
02
Knowledge-base gaps identified and fixed
The first evaluation identified gaps in the knowledge base that were causing hallucinated policy and refund statements.
PivotX connected each scorecard finding to the underlying failure cases, allowing the engineering team to identify which problems to address first.
Hallucinated policy and refund statements fell 81%. First Call Resolution increased from 58% to 73% after the team corrected escalation errors and failed tool calls.
03
Every new agent version evaluated before release
A one-time evaluation would not account for changes to the knowledge base or problems introduced by later agent versions.
PivotX turned the EvalIQ scorecard into a standing release requirement in nine weeks. Every new version must meet the required performance threshold before reaching customers.
Compliance teams can review results by metric, agent version, and failure case, providing evidence for corrective action and regulatory audits.
WHERE THIS TRANSFERS
Where this applies
Your AI agents are live, and your compliance team can't fully explain how they made their last hundred decisions.
Quality assurance is based on sampled interactions and reviewer judgment, not consistent measurement.
Every time you ship a new agent version, you're not certain it performs better than the one it replaced.
Proof
Pilots that became operating procedure.
PivotX has taken companies from initial assessment to production-grade AI in weeks, not quarters. The cases below aren’t proofs of concept. They’re actual production.
Which procurement workflow is creating the longest delay?
We can review the manual steps, handoffs, and systems involved and identify a practical place to begin.