How AI Agents Replaced 6.5 FTEs at a Series D Logistics Company

What Was the Challenge?

A Series D logistics company needed to scale operations without proportionally scaling headcount.

The company had raised a significant Series D round and was scaling rapidly. Revenue was growing 40% year-over-year, but the operations team was struggling to keep pace. Every new customer meant more support tickets, more orders to process, and more data to reconcile across systems.

The VP of Operations faced a difficult choice: hire 6–8 more operations staff at a loaded cost of $80K–$100K each, or find a way to handle the increased volume with the existing team. Previous attempts with basic automation tools like Zapier had helped with simple triggers but couldn't handle the nuanced, multi-step workflows that consumed most of the team's time.

Three workflows were identified as the biggest bottlenecks: customer ticket triage and response, order processing and validation, and daily cross-system data reconciliation. Combined, these workflows consumed the equivalent of 8+ full-time employees' worth of manual effort every week.

What Was Agentic Edge's Approach?

Agentic Edge conducted a 2-week assessment, then deployed three AI agent workflows over 8 weeks.

Mustafa Bayramoglu began with a comprehensive operations audit. Over two weeks, he shadowed the operations team, documented every manual touchpoint in each workflow, and mapped the decision trees that experienced staff used to handle edge cases. This wasn't a surface-level survey — it was a deep technical analysis of how information flowed between Zendesk, the company's custom OMS, Salesforce, and their internal reporting tools.

The assessment revealed that approximately 85% of customer tickets followed predictable patterns, 90% of orders could be validated automatically with the right integration logic, and 100% of the daily reconciliation process was rules-based. These findings shaped a three-phase implementation plan, with each workflow building on the integrations established by the previous one.

The CorePiper platform served as the orchestration layer, coordinating AI agents across multiple systems simultaneously. Rather than building brittle point-to-point integrations, each agent was designed to understand context, make decisions based on defined rules, and escalate to humans only when genuine judgment was required.

How Did the Ticket Triage Agent Work?

The ticket triage agent classified, routed, and drafted responses for 85% of incoming support tickets.

The first workflow to go live was customer ticket triage. Previously, a team of three dedicated staff members would manually read every incoming Zendesk ticket, classify it by type and urgency, route it to the appropriate team, and draft an initial response. Average first-response time was 47 minutes during business hours.

The AI agent now processes every incoming ticket within seconds. It classifies the ticket using natural language understanding trained on 18 months of historical ticket data, routes it based on the company's existing SLA tiers and team assignments, and drafts a contextual response that pulls relevant information from the customer's account history in Salesforce.

For the 85% of tickets that match known patterns, the agent handles the entire workflow end-to-end. The remaining 15% — complex issues, escalations, and edge cases — get routed to human agents with a pre-populated context brief that reduces their handling time by 40%. Average first-response time dropped from 47 minutes to under 2 minutes. This single workflow replaced 3 FTEs of manual effort.

How Did Order Processing Change?

Order processing time dropped from 12 minutes to 45 seconds per order with AI agent validation.

The second workflow targeted order processing and validation. Each incoming order required manual verification against inventory systems, credit checks against the customer's account status, address validation, and compliance checks for regulated shipments. The process averaged 12 minutes per order across two full-time team members.

The AI agent now performs all validation steps simultaneously rather than sequentially. It queries inventory in real-time, runs the credit check against Salesforce account data, validates shipping addresses against the company's logistics partner APIs, and flags any compliance concerns. Clean orders are approved and pushed to fulfillment automatically. Orders with exceptions get flagged with specific issue details for human review.

Processing time dropped from 12 minutes to 45 seconds per order — a 94% reduction. The exception rate held steady at 8%, meaning 92% of orders now flow through without human intervention. This freed up the equivalent of 2 FTEs.

What Changed in Reporting and Data Reconciliation?

Daily data reconciliation that consumed one full-time analyst now runs automatically every 4 hours.

The third workflow was daily cross-system data reconciliation. One full-time analyst and a part-time contractor spent their entire day pulling data from the OMS, Salesforce, Zendesk, and the logistics partner's system, then manually reconciling discrepancies. Reports were generated in spreadsheets, shared via email, and frequently contained errors that took additional hours to correct.

The AI agent now performs automated reconciliation every four hours instead of once daily. It pulls data from all systems via API, identifies discrepancies using the same logic the analyst used (now codified as rules), and generates structured reports that are automatically distributed to stakeholders. When discrepancies require human investigation, the agent creates a Jira ticket with full context attached.

The analyst who previously spent full days on reconciliation now spends approximately two hours per week reviewing the agent's exception reports and handling the small percentage of discrepancies that require human judgment. The part-time contractor role was no longer needed. Combined savings: 1.5 FTEs.

What Were the Final Results?

6.5 FTEs of manual work replaced, team reallocated to strategic initiatives, zero layoffs.

The operations team that was previously drowning in manual work now focuses on strategic initiatives: improving customer experience, optimizing logistics routes, and developing new service offerings. The AI agents handle the volume while humans handle the complexity.

Total implementation time from assessment kickoff to all three workflows running in production: 10 weeks. The company achieved full ROI on the engagement within the first quarter.