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01 / STRATEGY
AI Strategy & Readiness
Assessments, roadmaps, use-case prioritization, and build-vs-buy decisions.
02 / SYSTEMS
Custom AI Systems
LLM applications, AI agents, RAG pipelines, and bespoke ML models.
03 / INTEGRATION
AI Integration & Automation
Workflow automation, legacy system integration, and API orchestration.
04 / GOVERNANCE
AI Governance & Operations
Security review, compliance, model monitoring, and managed AI ops.
hello@apls.ai SOC 2 · ISO 27001 · Data residency on request
CS.04 Work — Retail

Support, bounded.

A multi-channel retailer automated order support with an agent given explicit refund authority limits — it resolves the routine majority and hands the rest to a person, with full context attached.

Sector
RETAIL
System
BOUNDED-AUTHORITY AGENT
Timeline
8 WEEKS TO PRODUCTION
01 / CHALLENGE

Support volume outgrowing the team.

Order-status questions, exchanges, and small refunds made up the large majority of contacts across the retailer's channels, but each still routed to a live agent regardless of how routine it was. Response times slipped during peak periods, and the team's attention was split evenly between simple and genuinely hard cases.

02 / APPROACH

Authority defined before autonomy granted.

The agent's authority was scoped explicitly before it touched a single live contact: it can check order status, process exchanges, and issue refunds up to a fixed ceiling per order — anything above that, or any contact showing signs of a policy dispute, escalates immediately. The boundary was set with the retailer's own finance and support leads, not assumed.

Every action the agent takes — a status check, a refund, an escalation — writes to the same audit log a human agent's actions would, with the reasoning attached.

03 / ARCHITECTURE

One agent, every channel, one order system.

The agent connects directly to the retailer's order-management system for live status and refund actions, and sits behind the same channels customers already use — chat, email, and messaging. Escalations carry full conversation and order context into the human queue, so agents never ask the customer to repeat themselves.

FIG. 01 — SUPPORT TOPOLOGY
04 / OUTCOME

The routine majority, resolved in seconds.

Routine contacts now resolve without a human touch, well inside the refund ceiling and fully logged. Escalations arrive at the support team with complete context, so the team's attention concentrates on the contacts that actually need judgment.

78%
of routine contacts resolved without human handling
0
refunds issued above the authorized ceiling
[ METRIC ]
reduction in average first-response time
100%
of agent actions written to the audit log

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