Rigid "press 1" phone trees bottlenecked global support. Now, localized AI agents guide complex step-by-step blind installations.
A global leader in custom window coverings operating a portfolio of ecommerce brands across 11 countries.
Online customers seeking help with measurements or installation were forced through rigid "press 1, press 2" phone trees that felt outdated and...
“The Decagon team consistently finds ways to make things work. They really understand what we’re asking for, get creative in how we solve problems, and keep pace with how quickly we want to stand things up.”
Manufacturer of window coverings and architectural building products.
Generative AI platform for automated enterprise customer support and workflows.
Hunter Douglas's Customer support is part of this use case:
Related implementations across industries and use cases
Peak season required 300 temps to manage 9,000 daily cases. An AI agent now handles the volume across 19 countries with zero backlog.
Previously, agents manually handled all simple info requests—30% of contacts. Now, an AI agent resolves these queries automatically.
Technicians faced long wait times for support. Now, an AI assistant queries manuals and IoT data instantly to speed up repairs.
Global time zones delayed urgent phone support. Now, conversational AI agents handle first-line troubleshooting and route complex cases.
Rigid phone trees forced staff to handle routine calls. Now, AI resolves common queries in 5 languages, freeing humans for complex issues.
Minor updates required 3.5-hour re-recordings. Now, teams use AI avatars to regenerate training videos from slides in 30 minutes.
Complex multi-tab spreadsheets limited early bots. Now, an extraction pipeline structures this data for AI to reliably resolve queries.
Manually evaluating high call volumes was slow and inaccurate. Now, AI extracts sentiment so sales teams can refine their outreach.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.
Rigid "press 1" phone trees bottlenecked global support. Now, localized AI agents guide complex step-by-step blind installations.
A global leader in custom window coverings operating a portfolio of ecommerce brands across 11 countries.
Online customers seeking help with measurements or installation were forced through rigid "press 1, press 2" phone trees that felt outdated and...
“The Decagon team consistently finds ways to make things work. They really understand what we’re asking for, get creative in how we solve problems, and keep pace with how quickly we want to stand things up.”
Manufacturer of window coverings and architectural building products.
Generative AI platform for automated enterprise customer support and workflows.
Hunter Douglas's Customer support is part of this use case:
Related implementations across industries and use cases
Peak season required 300 temps to manage 9,000 daily cases. An AI agent now handles the volume across 19 countries with zero backlog.
Previously, agents manually handled all simple info requests—30% of contacts. Now, an AI agent resolves these queries automatically.
Technicians faced long wait times for support. Now, an AI assistant queries manuals and IoT data instantly to speed up repairs.
Global time zones delayed urgent phone support. Now, conversational AI agents handle first-line troubleshooting and route complex cases.
Rigid phone trees forced staff to handle routine calls. Now, AI resolves common queries in 5 languages, freeing humans for complex issues.
Minor updates required 3.5-hour re-recordings. Now, teams use AI avatars to regenerate training videos from slides in 30 minutes.
Complex multi-tab spreadsheets limited early bots. Now, an extraction pipeline structures this data for AI to reliably resolve queries.
Manually evaluating high call volumes was slow and inaccurate. Now, AI extracts sentiment so sales teams can refine their outreach.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.