Consumer brands are data-rich.
AI turns that into insights
and automated action.

The Problem

The support team was handling thousands of tickets per month with a 1,000+ ticket backlog and first reply times approaching 10 days. Agents were capable, but the workflow made speed impossible. Tickets arrived without the information needed to act on them — leading to manual triage, follow-up questions, and delays before any real work could start.

What We Built

• Structured intake via Formstack — collecting issue type, order number, and product before the ticket hits the queue
• AI tagging and classification for every incoming ticket, including those arriving through email
• Routing logic that sends tickets to the right agent, queue, or automation path
• AI summarization for tickets bypassing the form, so they can still be routed intelligently
• AI handles the first one or two responses autonomously — sending troubleshooting steps, tracking updates, or order status without an agent involved
• Most tickets resolve after a couple of exchanges, so AI carries the full resolution for a meaningful portion of volume
• When escalation is needed, the agent steps in with full context already surfaced

Impact

The Lesson

AI support workflows work best when paired with good intake design. Collect the right information upfront, and AI can do more than just classify and route — it can handle the first response entirely. For most common ticket types, that first response is enough. The goal isn’t to replace agents — it’s to resolve the solvable tickets automatically so agents can spend their time on the work that actually requires a person: complex situations, escalations, and the kind of attentive service that builds customer loyalty.

The Problem

The brand had grown quickly but customer understanding hadn’t kept pace. Data lived in seven different systems that didn’t talk to each other. Even when data existed, it wasn’t flexible enough to answer the questions teams actually had. Marketing needed campaign lists. Sales needed account context. Leadership needed to understand how the customer base was shifting. Every request went through the analytics team.

Phase 1 — The Foundation

• Built a Customer 360 in Snowflake connecting 7 source systems
• Identity resolution and deduplication across 1M+ customer records using recursive logic
• Profile enrichment with third-party demographic data
• Segmentation logic powering Klaviyo marketing audiences
• Dashboards for initial exploration — useful but not flexible enough

Phase 2 — Making It Useful

• Connected Snowflake Intelligence on top of the unified C360 and internal business documents
• Semantic models translate how the business actually talks about itself into something the system can reason about
• Cortex Search indexes PDFs, decks, case studies, and business updates
• Any team member can now ask customer questions in plain English and get an immediate, cross-system answer

Impact

The Problem

The wholesale team managed a growing network of multi-location retail accounts. When large bulk orders came in — a single invoice spanning multiple locations across several states — reps had to manually enter every line item into Salesforce. A single recurring monthly order could take hours. It was happening regularly, and it was the kind of work that had nothing to do with selling.

What We Built

• Custom invoice upload tool that parses bulk order data and auto-drafts Salesforce orders
• Automated orchestration handling submission for approval and downstream close-out steps
• End-to-end workflow from invoice to closed order — with human approval kept in the loop
• Built to handle the complexity of multi-location accounts with varying SKUs and quantities

Impact

What Made It Work

The hard part wasn’t the automation itself — it was making the tool flexible enough to handle the real-world messiness of bulk wholesale invoices: inconsistent formats, multiple locations, mixed SKUs. Getting that parsing right is what made the time savings real.