Augment provides an AI‑powered assistance platform for customer support teams, using a deep‑learning engine to suggest real‑time responses to queries. The tool claims to increase agent productivity by about 30% and boost customer satisfaction, while also helping businesses uncover new revenue opportunities through more efficient interactions.
- Artificial Intelligence
- Data & Analytics
- Enterprise Software
Funding
Funding not disclosed
Founders
Product
Problem
Customer support teams often struggle to maintain high response quality and speed during peak demand, leading to lower productivity, reduced customer satisfaction, and missed upsell opportunities.
Solution
Augment offers an AI‑powered assistance platform that analyzes an organization’s internal customer data to generate real‑time suggested replies for support agents. By presenting the most relevant answers instantly, the system helps agents handle inquiries more efficiently, boosting productivity by roughly 30% and improving satisfaction scores. The platform also identifies relevant products or services based on a customer’s history and recent interactions, enabling agents to surface growth opportunities during support conversations. This AI augmentation reduces onboarding time for new agents and enhances job satisfaction for existing staff, supporting higher retention rates.
Target Audience
Primary customers are mid‑size to large enterprises that operate customer support centers and seek to improve agent efficiency, satisfaction, and revenue generation through AI assistance.
Features
- Deep‑learning engine that scans internal knowledge bases and past tickets to suggest context‑appropriate response drafts in real time
- Integrated product recommendation module that proposes relevant upsell or cross‑sell items based on customer behavior and support history
- Performance analytics dashboard showing agent productivity gains, satisfaction metrics, and revenue impact
- Seamless integration with common ticketing and CRM systems via API and plug‑in connectors
- Adaptive learning loop that continuously refines suggestions from agent feedback and outcome data