Premium Retailer
Confidential ClientWe connected siloed data across POS, CRM, ecommerce, and inventory systems — then built AI agents that answer operational questions no human on the team could. Including one that saved the owner from a $3.5MM loss.
The client
A sizable multi-location retailer selling premium, high-ticket goods. Each item requires significant floor space for display and customer experience. The business operates across brick-and-mortar stores and ecommerce channels — but the data infrastructure had never kept pace with the growth.
The challenge
- Data was completely siloed: multiple POS systems (accumulated through migrations), CRM, Shopify, inventory management, newsletter platform, Google Analytics, Instagram, and dozens of spreadsheets.
- The owner faced two compounding pressures: grow revenue by acquiring and retaining customers, and cut inventory costs — with high rent and high-value goods that can't sit on shelves indefinitely.
- The retail chain wasn't large enough to justify even a small business analytics team.
- Even basic operational questions were unanswerable: "What's the best-selling color in socks over the last 12 weeks?" required manually pulling data from multiple systems.
- Strategic questions like "How do I increase inventory turn rate from 2.0 to 2.6 for FY 2026?" were completely out of reach. Accountants could report on financial position, but operational insights were impossible.
What we shipped
- Lakehouse data foundation connecting all siloed data sources — POS systems, CRM, Shopify, inventory, newsletters, Google Analytics, and Instagram — into a unified architecture with nightly syncs
- Real-time BI dashboards giving the owner immediate visibility into business performance across all channels — brick-and-mortar and ecommerce — with daily sales and inventory movement across multiple stores
- Agentic analytics system that breaks down complex business questions into subtasks, orchestrates specialized AI agents, and calls external tools on the fly — including SageMaker DeepAR for demand forecasting and SKU-level predictions
- Web-enabled research agents with access to external data sources — city bylaws, municipal meeting minutes, market research — to provide forward-looking intelligence beyond what's in the transactional data
- Hyper-personalized newsletters powered by linking past purchase history, seasonal buying behavior, and external data to generate individualized content for each member
The $3.5MM mistake averted
In December, the owner was presented with an opportunity to acquire a similar retail store for $3.5MM. She was given five years of income statements and balance sheets, and also requested five years of transactional data.
Her accountant reviewed the books and advised that the business was highly profitable — the aggregated financial data told a compelling story.
Our agentic analytics system reached the opposite conclusion.
The AI agents spotted unusual transaction patterns in the most recent 12 months: customers were repeatedly walking in and purchasing every size, color, and model of the same brand. When we investigated manually, we discovered these transactions were driven by overseas resellers — a violation of the brand's retail agreement and fundamentally unsustainable revenue.
The agents also conducted independent web research — scanning city bylaws, municipal meeting minutes, and construction project filings — and identified a major subway construction project that would effectively eliminate foot traffic to the store for the next two years.
Neither of these findings appeared in the income statements or balance sheets. The construction project was forward-looking intelligence that no financial review would surface.
The seller had insisted on a decision by January 31st. On February 1st, the city issued public notice that the entire street in front of the store would be closed to all vehicle traffic for 12 months — with the city's history of delays, likely 24 months. The store's annual rent was $720K. Excluding the unsustainable reseller revenue, the underlying business was declining.
The store went bankrupt weeks later. The owner avoided a $3.5MM loss.
Results
- $3.5MM loss avoided on a single acquisition decision — AI spotted what the accountant's financial review missed
- 17% inventory cost reduction through demand forecasting and SKU-level predictions
- 8% sales increase within 2 months of deploying hyper-personalized customer newsletters
- Real-time operational visibility across all channels and stores — questions that took days now take seconds
- Strategic decision support for complex questions like inventory turn rate optimization, powered by AI agents that can call forecasting models on demand
Why it worked
- Data foundation first: without connecting the siloed systems, no amount of AI would have helped — the Lakehouse design gave us clean, unified data to work with
- Agentic architecture: complex questions get decomposed into subtasks handled by specialized agents — some do analysis, some call ML models, some research the web
- Deep business context: we invested significant time understanding the client's business model, revenue streams, cost structure, industry landscape, supply chain, and operational constraints — then encoded that knowledge into the system so agents reason with real business context, not just raw data
- Traceable and human-in-the-loop: every agent action and reasoning step is tracked and auditable — and the system is designed to surface unusual findings back to humans for input before acting on them, so while it eliminates 90% of the analytics workload, human judgment and insight stay in the loop at every critical decision point
- Forward-looking intelligence: the system doesn't just analyze historical data — it actively researches external factors that could impact the business
- Right-sized for the business: the owner gets enterprise-grade analytics without needing to hire a data team