Case Study · Automation

Customer Support AI Triage (n8n + FastAPI)

Incoming email/DM classification, AI draft response, ticket creation, audit logging, and mandatory human approval before sending.

ProblemSupport queues are slow and inconsistent across channels.

SolutionEvent-driven triage pipeline with SLA-aware routing.

ImpactFaster first response while preserving human control.

Architecture

Workflow Design

Built as a control system: intake contracts, policy-aware triage, deterministic fallback paths, and auditable human approval.

email/dm ingress -> normalize + redact -> classify + risk score -> routing policy
       |                  |                      |                   |
       v                  v                      v                   v
idempotency check   attachment scan        draft strategy      queue assignment
       |                                                           |
       v                                                           v
audit event log  -> approval gate -> ticket write -> notify -> SLA monitor

Why this matters: keeps automation fast while preserving compliance, reviewability, and safe failure behavior.

Ingress + Validation

Email/DM events are normalized, deduplicated, scanned, and enriched before any model action.

Policy Triage Engine

Classifier output is combined with risk and SLA policy to produce deterministic routing and escalation decisions.

Approval + Audit Layer

Draft, ticket state transitions, and human approvals are event-logged for replay, compliance, and incident review.

Evidence

Reliability Controls

Human-in-the-Loop

No outbound response without explicit approval action.

Auditability

Every decision and generated draft is logged with timestamp and actor metadata.

Operational Checks

Retry and dead-letter handling for connector failures.
Priority SLA alerts for urgent cases.
Fallback template when model confidence is low.

Presentation path: projects/support-ai-triage/presentations/upcoming/

Technical Peek

Policy-Governed Triage Orchestrator

def process_message(env: WorkflowEnvelope) -> WorkflowResult:
    if idempotency_store.seen(env.message.message_id):
        return build_cached_result(env.message.message_id)

    prepared = preprocess_message(env.message, pii_redactor, attachment_scanner)
    model_out = classifier.classify(prepared)
    decision = policy_engine.resolve(model_out, prepared, env.customer_profile)

    draft = draft_builder.build(prepared, decision, templates=template_library)
    ticket = ticketing.create_ticket(prepared, decision)

    if decision.requires_human_approval:
        approval = approvals.enqueue(ticket.ticket_id, draft)
        ticket.status = "pending_human_approval"
    else:
        approval = None

    audit.write("triage.completed", {
        "message_id": env.message.message_id,
        "ticket_id": ticket.ticket_id,
        "priority": decision.priority.value,
        "queue": decision.target_queue,
        "requires_human_approval": decision.requires_human_approval,
    })

    return finalize_result(ticket=ticket, decision=decision, draft=draft, approval=approval)

Why this matters: keeps response speed high while enforcing human approval and full auditability for sensitive customer communications.

Advanced Breakdown

Most Important Engineering Decisions

1. Confidence-Aware Escalation Rules

Classifier outputs are mapped to explicit escalation paths; low confidence and high-risk categories bypass auto-draft behavior and jump directly to human handling.

Why this matters: uncertain automation never acts as if it were certain.

Risk Control

2. Mandatory Human Approval Queue

Every outbound draft is staged for agent approval with SLA timers and ownership assignment before any customer-visible action is allowed.

Why this matters: response speed improves without losing accountability.

Human-in-the-Loop

3. Deterministic Fallback Templates

When intent or confidence fails thresholds, the system serves policy-approved templates instead of free-form generation to avoid off-brand or unsafe messaging.

Why this matters: worst-case behavior stays controlled and compliant.

Brand Safety

4. Idempotent Workflow + Dead-Letter Handling

Connector retries are idempotent by message ID and failures route to dead-letter queues with clear replay tooling, avoiding duplicate tickets and silent drops.

Why this matters: operational reliability is maintained under integration issues.

Reliability

5. Event-Sourced Audit Trail

Classification decisions, draft revisions, approvals, and dispatch actions are logged as immutable events with actor metadata and timestamps.

Why this matters: teams can reconstruct exactly what happened on any support case.

Traceability