From Human Bottleneck to Agentic Copilot.

In under three months we replaced a legacy macro library with an LLM-powered workflow agent that reads intent, orchestrates data pulls, and auto-drafts resolutions—freeing enterprise support teams to focus on edge-case expertise.

Impact Snapshot

Phase 1: Defining the Situation.

In three sprints we replaced 11 copy-paste steps with a single agentic side-panel. Handle time dropped 37 %, agent NPS jumped 22 points, and finance signed off on $2.4 M in annual cost avoidance.

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• 1

  Create a free account

• 2

  Start building your website

• 3

  Deploy and enjoy

Post Starting Friction

Why Ops Were Drowning

Support reps toggled between 11 SaaS tabs, wasting 6 minutes per ticket just hunting data. Knowledge-base accuracy hovered at 58 %, tanking SLAs and morale.

It's free and no credit card required.

• 1

  Create a free account

• 2

  Start building your website

• 3

  Deploy and enjoy

Design Moves

Agent UX > Chat UX

An intent classifier maps incoming text to a task graph, a workflow orchestrator pulls CRM + log data, and the LLM Draft Builder surfaces a ready-to-send response. The agent sits in a side-panel—no context-switching, no blind chat window.

Outcomes

Metrics Speak Louder Than Mockups

Average handle time shrank from 15 → 9 minutes; first-contact resolution climbed 18 %; SLA breaches dropped 42 %. The pilot scaled to 4 regions in < 90 days.

It's free and no credit card required.

• 1

  Create a free account

• 2

  Start building your website

• 3

  Deploy and enjoy

Next Iteration

Phase 2: Defining the Situation.

Next up: real-time sentiment scoring, automatic PII redaction, and voice-channel hooks—all feeding a reinforcement loop to push accuracy past 95 %.