AI CONTACT CENTER SOLUTION

PART 1: CONTEXT & CHALLENGES

1. Current Situation

Contact Center inbound service is facing a large volume of repetitive queries accounting for 40–60% of total interactions.
The Voice of Customer and NPS collection process is still manual, time-consuming, does not accurately reflect actual customer needs, and depends on personal judgment.
There is no comprehensive customer data analysis tool to predict behavior or proactively suggest engagement.
The call center team handles a large volume of calls manually, affecting performance, consulting quality, and limited upsell capability due to lack of necessary information about customer purchasing behavior, mainly relying on experience and recommendations from the fieldforce.

2. Issues to Address

Lack of intelligent automation (AI automation) in inbound/outbound to optimize productivity.
Fragmented data, difficult to integrate between CRM systems, hotline, Zalo OA, and e-commerce channels, manual order placement and order recording steps affecting work efficiency.
No AI-powered quality control (QC) monitoring tools leading to subjective evaluation, not strict enough and inconsistent.
Lack of real-time consumer behavior analysis capability to suggest suitable products, increase upsell/cross-sell.
Sales capability differences between individuals lead to results heavily dependent on consulting ability, monitoring, and easily influenced by personal emotions in sales activities.

3. Urgency

Competition in the FMCG industry is increasingly fierce, customers demand fast response, personalization and omnichannel.
Competitors in banking, telecommunications, and e-commerce have successfully deployed AI Contact Center with operating costs reduced by 30–40%, productivity increased by 20–30%, and NPS increased by 5–10%.
AI integration becomes a mandatory condition to maintain competitive advantage, reduce personnel costs, and flexibly scale services in the context of limited resources.

PART 2: OBJECTIVES & SOLUTION REQUIREMENTS

4. Core Objectives of the Solution

Stating the most important objectives that the solution needs to achieve

Increase operational efficiency through AI automatically handling 30–50% of call volume and repetitive interactions.
Improve service quality & NPS with faster, more personalized responses, 24/7.
Optimize costs & headcount, reduce operating costs by 20–25% through schedule optimization and automated monitoring.
Indirectly increase revenue through intelligent AI product recommendations and identification of upselling/cross-selling opportunities.
Standardize & improve agent training quality through AI QC & speech analytics.

5. Key Results to Achieve

Please share the desired output results. E.g.: Business effectiveness, operational efficiency, scalability and integration,...

Objective	Desired Result	Target Timeline
Reduce repetitive request volume	20–30%	Q3/2026
Increase agent productivity	+5–10%	Q4/2026
Optimize personnel budget	~10%	Q4/2026
Improve average NPS	+5 points	Q4/2026
Increase indirect order volume	+5.5K HLs	Q4/2026

PART 3: DIRECTION & SUPPORTING RESOURCES

6. Technology Direction

Does the enterprise have any specific direction?

AI Layer integrating Contact Center Platform (Genesys & Microsoft Dynamics, Zalo OA, External AI platform/module):

Create an intermediate AI layer (AI Hub) connecting customer data, transaction history, and interaction channels to automatically respond, suggest actions, and intelligently route.

Main AI Applications (Anticipated Use Cases):

AI Inbound Automation: Automatically handle common requests, integrate intelligent IVR & Zalo OA chatbots.
Conversational AI for Telesales: Support agents in real-time decision making, suggest SKUs, and personalize sales scripts.
AI Quality Monitoring: Analyze 100% of calls, automatically detect process deviations & suggest training.
Predictive Engagement: AI predicts churn risk or repurchase potential, activate proactive care campaigns.

Data Infrastructure & Integration:

CRM Cloud (Microsoft Dynamics)
Speech Analytics (Genesys, Google Speech-to-Text API, Open AI, Gemini)
Unified Data Lake serving reporting & machine learning models

7. Supporting Resources from the Enterprise (if possible)

What resources can the enterprise provide to potential startups that the enterprise wishes to collaborate with?

From internal enterprise:

Contact Center Transformation project team (RTC, D&T, Marketing, Finance)
Customer history data, call records, tickets, call logs, chat logs

From AI partners & APAC AI Hub:

Technology consulting & deployment roadmap
Support for pilot models and quantitative KPI measurement
Financial/budget support in the initial phase to ensure scalability

We sincerely thank you for your cooperation! This detailed information will be a solid foundation for creating breakthrough AI solutions