Platform Juggling & Context Switching
Agents constantly toggled between the CRM, legacy ticket databases, and product manuals during live calls, risking losing the thread of customer conversations.
Customer Overview
Customer Overview
- Organization
- Global consumer-services company
- Scale
- ~3,500 support agents operating across six contact centers
- Content Estate
- CRM records, ~9 million historical support tickets, 1,200+ product manuals, and a policy knowledge base
Executive Summary
Executive Summary
To reduce customer hold times and address agent burnout caused by platform juggling, a global consumer-services company deployed a real-time AI agent copilot built on unified enterprise search. Rolled out to the first agent cohort in 12 weeks, the solution embeds directly into existing agent desktops to synthesize context-aware answers and next-best actions from ~9 million tickets and 1,200+ manuals. The implementation reduced average handle time for complex tickets by 40% (from 18.4 to 11.1 minutes), accelerated new-agent ramp times from 6 weeks to under 2 weeks, increased first-contact resolution from 61% to 78%, and boosted customer satisfaction (CSAT) scores from 3.9/5 to 4.6/5.
Business Context
Business Context
Operating six contact centers worldwide with 3,500 agents, the organization faced rising handle times on complex customer issues. Institutional knowledge was fragmented across disparate databases, forcing agents to manually dig for technical fixes or policy rules while live customers waited on hold. This constant operational friction hurt customer satisfaction scores and drove high agent attrition, triggering ongoing, expensive retraining cycles for incoming staff.
The Challenge
The Challenge
Unsearchable Historical Knowledge
Over 9 million historical tickets contained solutions to recurring issues, but lexical search limitations made this prior art unsearchable under live call pressure.
Escalating Costs & Burnout
Extended resolution times increased hold durations, dragged down CSAT scores, and burned out support staff, leading to high turnover and multi-week onboarding periods for new hires.
YenDigital’s Approach
YenDigital’s Approach
Rather than adding another tab to the agent desktop, the strategy focused on embedding a real-time copilot directly into the existing workflow. The architecture leverages live conversation context (voice speech-to-text or chat feeds) to trigger agentic, multi-step retrieval across a unified index before the agent even types a query, combining intent detection with continuous human-in-the-loop feedback loops.
The Solution
The Solution
- Unified Hybrid Index: Merges CRM records, 9M+ tickets, 1,200+ product manuals, and policy documents into a single ACL-aware index pairing vector and keyword retrieval.
- Real-Time Context & Speech-to-Text: Live voice transcription and chat feeds auto-detect intent and entities as conversations unfold to pre-fetch relevant answers.
- Agentic RAG for Multi-Step Troubleshooting: The copilot plans multi-step retrieval, verifies account status, pulls relevant manual clauses, matches past tickets, and drafts grounded responses with inline citations.
- Next-Best Action Guidance: Proposes concrete playbook actions (e.g., issuing policy-compliant credits, dispatching technicians, or escalating) directly alongside suggested replies.
- Semantic Caching & Active Feedback Loop: Frequently asked questions are served from a semantic cache in milliseconds to reduce latency, while agent accept/edit/reject actions continuously tune the reranker.
Business Outcomes
Business Outcomes
| Metric | Before Implementation | After Implementation |
|---|---|---|
| Average Handle Time (Complex Tickets) | 18.4 minutes | 11.1 minutes (-40%) |
| New-Agent Ramp to Full Productivity | ~6 weeks | Under 2 weeks |
| First-Contact Resolution | 61% | 78% |
| Customer Satisfaction (CSAT) | 3.9 / 5 | 4.6 / 5 |
Technologies Used
Technologies Used
- Architecture Pattern
- Real-Time AI Agent Copilot with Agentic RAG
- Search & Indexing
- ACL-Aware Unified Hybrid Index (Dense Vector + Keyword Search)
- Real-Time Processing
- Live Speech-to-Text (Voice Calls), Chat Feed Integration, Intent & Entity Detection
- Performance & Feedback
- Semantic Caching (Millisecond Response), Continuous Reranker Tuning (Agent Accept/Edit/Reject Signals)
- Automation & Guardrails
- Grounded Response Synthesis with Citations, Playbook-Mapped Next-Best Actions
Why YenDigital
Why YenDigital
In-Workflow Desktop Integration
Native integration directly inside existing agent interfaces, eliminating context switching without introducing new tool friction.
Agentic Multi-Step Reasoning
Advanced RAG orchestration capable of checking account states, matching past ticket history, and executing multi-step troubleshooting paths autonomously.
Rapid Scale & Deployment
Execution delivering a fully functional cohort rollout in 12 weeks, followed by a seamless global phased expansion.
Project Highlights
Project Highlights
- Zero-Search Initiation
- Real-time speech and text listening triggers retrieval automatically before an agent types a single character.
- Millisecond Latency
- Integrated semantic caching handles high-frequency queries almost instantaneously, reducing compute cost and latency.
- Compounding Model Quality
- Continuous feedback loops automatically refine reranking accuracy based on live agent interactions.
