Why this paper exists

A well-handled call can still leave a customer with incorrect information. At the wealth management firm featured in this paper, representatives searched across disconnected knowledge sources while manual quality reviews covered fewer than 1% of calls. There was no mechanism to verify whether the information shared matched the organization’s knowledge base. This paper details how Altimetrik built a dual-agent AI platform on Snowflake to provide grounded answers during calls and evaluate service quality and factual accuracy afterward.

Inside the paper

01

Connecting fragmented knowledge

Bringing Salesforce Knowledge, SharePoint and PolicyBase into a unified, searchable knowledge base.

02

Supporting representatives during live calls

How the Advisor Knowledge Agent retrieves grounded, cited answers through a Model Context Protocol (MCP) plugin.

03

Evaluating call quality and factual accuracy

Automated scoring against a 17-item rubric, with claim-level verification against the organization’s knowledge base.

04

Engineering and validating the platform

PII redaction, Snowflake architecture, evaluation methods, adversarial security testing and implementation trade-offs.

Industries covered

●

Wealth Management

●

Financial Services

Who should read it

Service operations, contact center and quality assurance leaders looking to improve knowledge access and call review coverage. Data, AI and engineering leaders building grounded AI applications within an existing governance framework.

Who wrote this

Author

Shivkumar Krishnan

Contributing Architects and Technical Leads

Mahesh Varavooru

Amol Brahmankar

JB Johnson

Shravan Gopalakrishnan

Shashwat Jha

Vidhi Tralsawala

Aditya Chobe

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AI-Powered Service Intelligence for Wealth

Connecting fragmented knowledge

Supporting representatives during live calls

Evaluating call quality and factual accuracy

Engineering and validating the platform

Shivkumar Krishnan

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