AI Agents for Retail Operations
Agents that run operations. With a human in the loop.
Scoped agents that read live commerce context through MCP tools, recommend or take action inside your systems, and log every step. Built for accountability, not a demo.
Where agents help
The recurring, judgment-light work.
Agents earn their place on the high-volume decisions that follow rules but still eat a person's day. The hard judgment stays with your team.
Demand forecasting & replenishment
Agents read sales velocity, lead times, and stock positions, then draft transfer and purchase recommendations for a planner to approve.
Anomaly triage
Inventory drift, price errors, and fraud signals surfaced with a probable cause and a suggested fix — before they reach a customer.
Returns & refund triageBuilt
Each request classified against policy, the response drafted, and the genuine edge cases escalated to a human with context attached. Already built — we configure it to your policy rather than starting from scratch.
Merchandising & catalog enrichmentBuilt
Descriptions, attributes, and taxonomy drafted from product data, then queued for review — never published unattended. Already built — we point it at your catalog.
Support escalation routing
Tickets read in full, intent identified, and routed with a draft reply and the order, fulfillment, and history already pulled.
Supplier & PO exceptions
Late shipments, short receipts, and mismatched invoices flagged with the upstream cause traced across OMS, WMS, and ERP.
How it stays accountable
Every action is scoped, approved, and logged.
The same loop runs behind every workflow. The human-approval gate is where trust is set — and raised over time.
Human approval model
Every action above a confidence threshold pauses for a person. You set the threshold per workflow, and raise it as trust earns it.
Scoped data access
Agents reach data and tools through MCP servers with explicit permissions — read-only until a write is justified and logged.
Guardrails & evals
Each agent ships with a regression suite. Prompt and model versions are pinned; a bad change fails the eval before it ships.
Audit logs
Every read, decision, and write is recorded with the context behind it. You can replay exactly why an agent did what it did.
Example workflows
A trigger in, a traceable path out.
Stockout risk detected
Read velocity + stock + lead time → draft transfer / PO → route to planner → write on approval
Return request arrives
Classify against policy → draft response → auto-resolve clear cases → escalate edge cases with context
Margin drop on a SKU
Trace price, cost, and promo across systems → identify the cause → recommend the corrective action
Pilot roadmap
One workflow to production in about 7 weeks.
We earn the right to automate. Shadow mode first, supervised action second, expansion only once the first workflow holds.
- 01Weeks 1–2
Scope & access
Pick one workflow with clear value. Stand up MCP access, read-only. Define the approval threshold and the eval set.
- 02Weeks 3–4
Build & evaluate
Build the agent and its guardrails. Run it against the eval set until it holds. No production writes yet.
- 03Weeks 5–6
Shadow mode
Agent recommends; humans decide. Measure agreement and catch the gaps before any action is automated.
- 04Week 7+
Supervised action
Turn on writes above the threshold, with approval below it. Expand to the next workflow once the first holds.
Models predict. Agents act. Destm builds both — with guardrails.
The intelligence — forecasting, pricing, fraud, search — lives in AI & Deep Learning. This page is the operational layer that puts those models to work under human approval and a full audit trail.
We run on this
Destm's own delivery runs on agents. That is where the guardrails came from.
The approval gates, the eval suites, and the audit trail on this page are not theory — they exist because we needed them ourselves. Building CuberiQ this way cut first-draft authoring from about 45 minutes to 7 per page, generated SEO metadata and JSON-LD with no engineer in the loop, and now runs the lead flow behind our own AI-readiness audit.
Read the build journeyWhere we built
AI-powered inventory optimization for a multi-channel retailer.
3xInventory efficiency — ML demand forecasting replacing manual stock planning
Read the case studyHow an engagement starts
Three steps to a partnership
Intake call
30 minutes. We listen, you talk. No deck.
Diagnostic
We audit the surface, name the bottleneck, propose a path.
Kickoff
Senior engineer in your standup by week two.