Our AI Strategy

Building Intelligent Layers Across Every Level of the Business

Small Team · Mighty Impact · AI-Powered

The Three Pillars of AI

01

Productivity AI

Augmenting every team member's daily work with Copilot, Cursor, and Claude Code

02

Agentic AI

Autonomous agents that execute tasks — development, project management, customer service

03

Decision Intelligence

AI-driven analysis, anomaly detection, and reporting for smarter business decisions

These are not isolated initiatives. They are interconnected layers that compound in value as adoption deepens.

Pillar 1 — Productivity AI

Everyday AI Augmentation

Microsoft 365 Copilot

Drafting, summarizing, and prioritizing emails in Outlook
Real-time transcription, meeting summaries, and action items in Teams
Document creation, data analysis in Word, Excel, and PowerPoint
Surfaces institutional knowledge across SharePoint, OneDrive, and Teams

AI-Assisted Development

Cursor and Claude Code for AI-powered development
Record-speed feature delivery and dashboard creation
Every developer becomes more "well-rounded" — AI fills knowledge gaps across languages and frameworks
Automated process and code documentation — agents self-document commits and developed processes, published to Confluence for org-wide knowledge sharing and auditing

0

% Team Adoption

∞

Productivity Ceiling

Microsoft Copilot Cursor IDE Claude Code SharePoint AI Teams AI

Pillar 2 — Agentic AI

Autonomous Agents at Work

DTLR IQ — Knowledge Bot

Teams-integrated RAG bot ingesting Confluence, policies, handbooks
Powered by Azure OpenAI with vector search — accurate, cited answers
Embedded in DREAM intranet portal via web chat widget

Jr. Developer Bot (DevAgent)

Monitors Jira for AI-assignable tickets → clones repo → implements → opens PR
Safety controls: branch protection, file-change limits, human review required

Team Coordinator Agent

Deployed per department/team to facilitate priorities, daily check-ins, and task alignment
Escalating reminders, weekly digests, and follow-up — keeps things moving automatically

<strong>DREAM — Location Intelligence Hub:</strong> Internal dashboard built with AI-assisted development showing 442 active storefronts, fulfillment stats, BOPIS eligibility, and geographic distribution. Delivered at record speed using Cursor and Claude Code.

<strong>DTLR IQ Embedded in DREAM:</strong> The IQ web chat widget embedded directly in the DREAM intranet. A user asks 'top 5 selling styles last week' and gets a live database response with sales dollars and units — no SQL required, just natural language.

<strong>DTLR IQ — Knowledge Bot in Teams:</strong> Employee asks about PTO accrual policy and gets an instant, accurate response with full breakdown by role and tenure — sourced from the Employee Handbook via RAG vector search.

<strong>DTLR IQ — Product Release Query:</strong> Natural language question about upcoming product releases returns a structured table with 17 styles, release dates, vendor details, and retail prices — pulled live from the database via MCP.

<strong>Team Coordinator Agent — Monday Kickoff:</strong> Automated weekly recap delivered to the dev team — 25/30 check-ins completed, key progress items, and attention areas. The agent parses meeting notes and project data to keep the team aligned on priorities.

<strong>Team Coordinator Agent — Daily Check-In:</strong> Proactive daily standup form pushed to team members via Teams — captures accomplishments, today's plan, blockers, and mood. Automates the coordination overhead so teams stay focused on execution.

<strong>DTLR IQ — Context Map / Knowledge Architecture:</strong> Admin view showing all context sources feeding the AI assistant — 60 document chunks, 23 MCP tools, 5 Jira tools, and connected systems including Teams, API ingestion, database MCP, and Jira REST API.

<strong>DevAgent — Autonomous Ticket Resolution:</strong> Jr. Developer Bot picks up Jira ticket DV-467, analyzes the requirements, generates an implementation plan with file impact analysis, and asks clarifying questions before writing code — all autonomously.

Pillar 2 — Agentic AI

Commerce & Customer AI

🛒

Agentic Commerce

Product feeds sent to ChatGPT, Google Search AI, and Microsoft Copilot. Leveraging Universal Commerce Protocol (UCP) by Shopify — AI agents browse, recommend, and checkout for consumers.

🤖

Digital Genius

Automated customer service via email, chat, and voice. Integrated with DTLR systems via MCP and API for real-time order status, process documentation, and autonomous resolution.

🔍

FastSimon

AI-driven shopping assistant on dtlr.com — intelligent search, natural language product Q&A, and personalized recommendations based on behavior.

🌐

The Vision

Customers don't visit websites — their AI agents shop for them. DTLR is positioned ahead of the curve as agentic commerce rolls out industry-wide.

DTLR.com Agentic Checkout Flow

ChatGPT Shopping & Checkout

Microsoft Copilot Shopping & Checkout

Pillar 3 — Decision Intelligence

AI-Powered Analysis & Reporting

Analyst Reporting Agent

TaskManager background service with local LLM for continuous data analysis
SKU velocity Z-scores, week-over-week deltas, rank changes, store anomalies
Auto-generated reports delivered as interactive Adaptive Cards in Teams
Conversational follow-up — drill deeper into any data point with natural language
Custom subscriptions per team — daily flash, weekly flash, and department deep-dives already available
No pre-canned formats or static data — true anomaly detection that hunts for patterns and trends

Internal MCP Database Server

22 tools across 6 database connections — product, sales, RFID, e-commerce, middleware
Natural language querying — ask questions in plain English, get answers from live data
Used by devs for model generation and by stakeholders for ad-hoc KPI reporting

<strong>DREAM Analyst Report — Category Deep Dive:</strong> AI-generated executive summary with KPI cards (revenue, margin, units). Automated anomaly detection flags Women's Running decline and Kids Athletic momentum shift with root-cause analysis.

<strong>Mobile-Responsive Report View:</strong> Same analyst report rendered on mobile — stakeholders can review AI-generated insights on the go. KPI cards and executive summary adapt to any screen size.

<strong>Teams Adaptive Card Delivery:</strong> Analyst reports automatically delivered to Teams channels as interactive Adaptive Cards. Users can read the AI summary and ask follow-up questions directly in the thread using natural language.

AI at Every Layer

🌐 Customer-Facing AI Agentic Commerce · Digital Genius · FastSimon

👥 Employee-Facing AI Copilot · DTLR IQ Bot · DREAM Integration

⚙️ Operational AI Analyst Reports · Anomaly Detection · Order Routing

💻 Developer AI DevAgent · Cursor · Claude Code · MCP Tools

🗂 Data & Infrastructure MCP Servers · Self-Documenting Processes · Local LLMs

DTLR is building an interconnected ecosystem where every layer makes the others smarter.

Safety & Governance

🔒

Data Protection

Read-only MCP access to production data. No customer PII exposed without data processing agreements.

🧠

Human in the Loop

Every AI decision flows through human review gates — DevAgent PRs, report validation, approval workflows.

🛡

Microsoft Foundry

Private, local LLM models via Azure AI Foundry — keeping sensitive data in-house with enterprise-grade controls.

What's Next

Expand TaskManager Intelligence

Order routing, exception handling, monitoring — any background process gets AI

Power BI MCP Integration

Natural language analysis of existing reports when Microsoft rolls out their MCP server

Deepen Agentic Commerce

Full UCP adoption as the Universal Commerce Protocol matures across the industry

Scale DevAgent

Manual trigger → scheduled with approval gates → fully autonomous ticket resolution

Continuous Self-Improvement

Self-documenting systems get smarter with every interaction — recursive improvement loop

DTLR is building AI into its DNA —
not as a bolt-on, but as a fundamental layer of how we operate.