> Source: https://builder-docs.ema.ai/getting-started/effective-discovery/query-to-resolution
> Title: Query to Resolution Discovery Guide

# 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:

AI Employee

Purpose

**Customer Support (Chatbot)**

Handles Tier-1 support interactions autonomously in chat environments.

**Agent Assist**

Drafts high-quality email responses and suggestions for support agents in real time.

**QA Automation**

Evaluates support transcripts against SOPs to flag gaps and automate quality evaluations.

**Knowledge Base Augmentor**

Detects outdated knowledge articles and automates updates using ticket data.

**Insight Finder**

Identifies trends across tickets, chat logs, and resolution notes for coaching or root cause analysis.

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

AI Employee

Metric

Description

Customer Support (Chatbot)

Accuracy

Percentage of responses not rated negative.

Customer Support (Chatbot)

Adoption

Active users per day or week; session completion rate.

Customer Support (Chatbot)

Deflection

Percentage of tickets resolved without agent intervention.

Agent Assist

Coverage

Percentage of tickets with AI-suggested draft.

Agent Assist

Accuracy

SME-rated quality of suggestions.

QA Automation

Coverage

Percentage of SOP or QA criteria evaluated by AI.

QA Automation

Alignment

Percentage of AI evaluations matching human QA.

QA Automation

Throughput

Percentage of total conversations evaluated.

KB Augmentor

Update Volume

Number or percentage of stale docs updated.

KB Augmentor

Efficiency

Time saved via automated maintenance.

Insight Finder

Insight Scope

Topics and patterns extracted.

Insight Finder

Analyst Time Saved

Hours saved monthly.

## 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

Role

Responsibility

Executive Sponsor

Business alignment, budget approval.

Platform Admin / Champion

Manages console, configuration, role mapping.

Support SME

Provides process insights and escalation knowledge.

Knowledge Owner

Owns article updates and tagging.

IT / Security Lead

Governs access, compliance, integrations.

End Users

Validate usability and provide real-time feedback.