Luke
Scaling Customer Support with Structured AI
How Aident AI and Cossistant structure support workflows
Generating a plausible reply is only one part of customer support. A production workflow also needs consistent inputs, policy grounding, escalation rules, approvals, and a record of what happened. Aident playbooks and Cossistant can be combined to make those requirements explicit.
Why Structure Matters
An open-ended prompt can vary with wording and context. A support contract narrows that uncertainty by defining:
Required inputs, such as account, product, issue type, and customer message
Approved policy and knowledge sources
A response schema, such as summary, resolution, and next step
Conditions that require clarification or human escalation
Actions the workflow may take without approval
Structure does not guarantee correctness. It makes the intended behavior testable and gives operators a place to revise the process when a run fails.
How the Pieces Fit Together
Cossistant provides structured support interfaces and response conventions. Aident provides the playbook that connects those structures to integrations and execution steps.
A support playbook might:
Normalize the incoming request into a known schema.
Classify the request against a maintained set of issue types.
Retrieve only the relevant policy or product sources.
Draft a response with citations or internal evidence.
Escalate when confidence is low, policy is missing, or the requested action is sensitive.
Record the outcome for later review.
Start with One Support Scenario
Choose a frequent, bounded case such as an onboarding question or a known billing issue. Create representative test cases for normal requests, missing context, conflicting policy, angry language, prompt injection, and account-specific actions. Require a person to review the first production runs.
Track metrics that reveal quality rather than just speed:
Escalation precision and missed escalations
Reopen rate
Policy violations
Drafts substantially edited by agents
Time to resolution
Failed or duplicate downstream actions
Only expand the workflow after the evidence shows where it is dependable. Avoid claiming general accuracy or time savings from a small internal sample.
Handle Edge Cases Intentionally
The workflow should ask a clarifying question when a required field is missing, refuse to invent account state, and escalate when the source material conflicts. Refunds, account changes, privacy requests, and security incidents should follow dedicated policies with explicit permissions.
For event-driven ticket intake, read Trigger Workflows the Moment Work Happens. For the feedback loop after each case, see Loops Need the Real World.
