CASE STUDY
Unblocking Revenue: Leveraging AI to solve quote failures with the Totogi Ontology
Key Facts
- 01 Customer
-
Tier-1 CSP
North America - 02 Product
-
Totogi Ontology
- 03 Use case
-
Quote failure remediation
- 04 Systems
-
CloudSense CPQ
Salesforce CRM - 05 Time frame
-
7 days
In brief
A Tier-1 multinational quad-play CSP in North America grew by acquisition. One acquired unit ran its CPQ on CloudSense and Salesforce, and a routine change request broke quotes, mismatched prices, and failed orders. Deals stalled and close to $300,000 in booked orders could not be delivered.
Totogi deployed the Totogi Ontology as an overlay on the operator’s existing systems, no rip-and-replace. A specialized AI agent read the broken quotes, corrected them, and wrote the fixes back into CloudSense. It went live in 7 days and cleared the backlog in under 24 hours.
Impact
7 days from engagement to a live AI agent resolving broken quotes
<24 hrs to clear a backlog that had taken days of manual work
<30 min to fix each quote, down from 5+ hours
~$300K in stalled orders unblocked and delivered
In just 7 days, Totogi’s AI agent was live – and in under 24 hours, it wiped out the backlog that had crippled sales for weeks – work that had previously taken days of manual effort
Challenge
The operator had grown by acquisition, and one deal brought in an advanced CPQ environment built on CloudSense and Salesforce. A change request introduced new functionality, and it went wrong. Quotes broke, prices came back mismatched, and orders errored out.
The damage compounded quickly. Each broken quote took more than five hours to repair by hand, so the backlog grew faster than the team could clear it. Sales deals stalled, reps grew frustrated, and support tickets piled up.
The revenue exposure was concrete. Close to $300,000 in booked orders could not be configured or delivered while the quotes stayed broken. IT teams and their vendors were under intense scrutiny to restore order, and the operator needed a fast, reliable way to clear the backlog and get sales moving again.
Solution
Totogi started with the mechanism, not a migration. It built an AI-generated data layer that mapped the operator’s business processes into a digital twin of its environment, running on top of the existing CloudSense CPQ and Salesforce CRM. This is the Totogi Ontology, an overlay that adds a shared understanding of the operator’s systems without invasive changes or rip-and-replace.
On that layer, Totogi built a specialized AI agent aimed squarely at the broken quotes. The agent used optical character recognition to read source documents such as DocuSign agreements, parsed them, and detected the discrepancies behind each failure. It then generated precise corrections, validated them, and wrote the fixes directly back into CloudSense.
Because the work was grounded in the shared data layer, the agent kept pace as new tickets arrived, handling fresh waves without a rebuild.
Business
impact
The Totogi Ontology delivered results almost immediately. The AI agent went live in 7 days and cleared the backlog that had stalled sales for weeks in under 24 hours, work that had previously taken days of manual effort.
The per-quote economics changed too. Fixing a broken quote dropped from more than five hours of manual repair to under 30 minutes, and the close to $300,000 in stuck orders was released for configuration and delivery. With the crisis contained, IT and sales teams moved off firefighting and back to higher-value work.
The engagement also left a reusable asset behind. The same AI framework and shared data layer can extend to other operational problems across the operator’s environment, turning a one-time rescue into a repeatable capability.
About the customer
The customer is a Tier-1 multinational quad-play CSP serving millions of broadband, TV, mobile, and fixed-line customers across North America. It has grown through both organic expansion and acquisition, adding IT systems and enterprise applications with each deal. The operator is anonymized in this case study.