Query to Resolution Discovery Guide

This guide provides a structured framework for conducting discovery for AI Employees that improve customer support operations -- from initial query through resolution, quality assurance, and insight extraction.

Product Suite Overview

The Query-to-Resolution suite addresses multiple stages of the customer service lifecycle:

Each AI Employee can be deployed individually or in combination depending on support maturity and infrastructure.

Discovery Preparation

Required Knowledge

• Familiarity with tools like Zendesk, Salesforce Service Cloud, Intercom, or Freshdesk.
• Understanding of support team structures including triage, escalation, and automation flows.
• Awareness of how documentation, macros, and bots are used in current support operations.

Stakeholders to Involve

• Support Operations or Platform Admins.
• Team Leads or SMEs.
• Knowledge Managers or Documentation Owners.
• IT or Security Teams.
• End users (agents, analysts).

Recommended Pre-Work

• Request process maps, escalation matrices, and SOPs.
• Collect real support tickets with transcripts if possible.
• Confirm initial AI Employee scope and outcomes.
• Ensure test or sandbox environment access.

End-to-End Workflow Mapping

Process Discovery

Understand how the support organization handles customer inquiries from first contact to resolution. Go beyond surface-level steps to uncover decision points, manual handoffs, and exceptions.

What to capture:

• Sequence and structure of key workflows: ticket creation, triage, assignment, resolution, escalation, and feedback.
• Roles and responsibilities at each step and how handoffs are managed.
• Sources of friction or inconsistency (SLA violations, resolution delays).
• Exceptions and escalation logic.
• Segmentation logic by product, issue type, or customer tier.

Discovery methods:

• Live ticket walkthroughs with SMEs narrating their decision-making process.
• Agent shadowing via recordings or real-time observation.
• Process map review annotated with what actually happens.
• Group whiteboarding sessions to highlight edge cases.

Knowledge and Content Sources

Map knowledge assets that AI Employees will rely on for response generation, evaluation, or documentation updates.

What to capture:

• Types of content used: articles, macros, SOPs, wikis, escalation paths, changelogs, historical cases.
• Source systems: Zendesk Guide, Notion, SharePoint, Google Docs, homegrown wikis.
• Update workflows and ownership.
• Content access, structure, tagging, and versioning.
• Content reliability: known contradictions, stale pages, orphaned documents.

Tooling and System Integration

Map all technical dependencies that influence feasibility, scope, and time to deploy.

What to capture:

• System inventory across the support stack (ticketing, KB, chat, CRM, analytics).
• Intended actions: read-only vs. create/update operations.
• Authentication and access pathways (API tokens, OAuth, SSO).
• Environment separation (test/dev environments for safe prototyping).
• Compliance and observability requirements.
• Approval chain for integration access.

Success Metrics

Pre-Launch Evaluation Checklist

• Golden datasets of sample queries and expected responses prepared.
• Accuracy thresholds defined (e.g., chatbot >= 85%).
• SME review workflows established.
• Test sandbox configured.
• Evaluation rubric documented.

Stakeholder Roles