Alcherry_
FIG. PR1 — Proof

Measured.
Not promised.

Reference architectures based on typical deployments. Conservative estimates — actual ROI depends on volume and workflow complexity.

>>>>>>>MeasuredGuardrailsAudit Logs
REFERENCE ARCHITECTURE A

Support Triage + Draft Replies

Reduced support workload by 60% while keeping escalation rate stable

E-Commerce · 3,000 tickets/month · 8-person support team

Before

  • ✗Manual tagging and routing — 30+ sec wasted per ticket
  • ✗Agents writing every reply from scratch
  • ✗4.2-hour average first response time
  • ✗45 escalations/week, many avoidable
  • ✗No visibility into deflection or resolution rates

What We Shipped

  • [✓]Auto-classify + prioritize + route (confidence-scored)
  • [✓]AI draft replies grounded in KB with citations
  • [✓]Thread summaries for complex tickets
  • [✓]Human-in-loop approval for all AI outputs
  • [✓]KPI dashboard with real-time metrics
  • [✓]Monitoring + failure alerting to Slack
Measured Outcomes
Auto-resolution rate0%78%
Avg first response4.2 hrs12 min
Escalations/week4511
Hours saved/month—125 hrs
Cost savings/month—$3,750
How We Kept It Safe
Approve before sendConfidence threshold (>0.85)VIP escalation rulesRetry logic + idempotencyFull audit trail
Stack
ZendeskOpenAISlackPostgresCustom dashboard
REFERENCE ARCHITECTURE B

Invoice Matching + KPI Automation

Automated 92% of invoice matching and eliminated 12 hrs/month of manual reporting

SaaS Finance Ops · 500 invoices/month · $2M annual spend

Before

  • ✗Manual invoice-to-PO matching — error-prone and slow
  • ✗2-day average processing time per invoice
  • ✗60 exceptions/month requiring investigation
  • ✗Manager spending 3+ hrs/week building KPI reports
  • ✗No anomaly detection — overbilling caught too late

What We Shipped

  • [✓]Automated invoice-PO matching with anomaly detection
  • [✓]Exception flagging with confidence scores
  • [✓]Dual approval workflow for flagged items
  • [✓]Auto-generated weekly/monthly KPI reports
  • [✓]Slack/email delivery with trend alerts
  • [✓]Full reconciliation audit log
Measured Outcomes
Auto-match rate0%92%
Processing time2 days15 min
Exceptions/month608
Report prep time12 hrs/mo0
Cost savings/month—$600 + error prevention
How We Kept It Safe
Amount thresholdsDual approval for anomaliesIdempotent retriesAudit logsMonitoring alerts
Stack
QuickBooksHubSpotSlackWebhooksCustom dashboard
REFERENCE ARCHITECTURE C

Refund Workflows + Thread Summaries

Cut refund processing from 6 min to <2 min per case with near-zero errors

DTC Brand · 300 refund cases/month · High chargeback risk

Before

  • ✗Manual refund review — 6 min per case average
  • ✗No structured approval flow — errors common
  • ✗Long ticket threads with no summary — agents re-read everything
  • ✗High chargeback rate from slow processing
  • ✗No audit trail for refund decisions

What We Shipped

  • [✓]Guided refund/replacement flow with rule-based approvals
  • [✓]Thread summaries + action items for every ticket
  • [✓]Amount thresholds + chargeback rules
  • [✓]Human approval required for all monetary actions
  • [✓]Full audit trail with decision logs
  • [✓]Monitoring alerts on error rate spikes
Measured Outcomes
Processing time6 min/case<2 min/case
Error rateHighNear-zero
Hours saved/month—43 hrs
Chargeback rateElevatedReduced 60%
Cost savings/month—$1,300+
How We Kept It Safe
Human approval gatesChargeback prevention rulesAmount thresholdsIdempotent retriesDecision audit logs
Stack
ShopifyGorgiasStripeSlackCustom dashboard

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