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Rent the Runway: one live view of inventory across every location.

Rent the Runway runs one of the most complex supply chains in retail. We moved its data to Snowflake and built the layers on top of it.

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Fashion rental business

Four problems, four fixes.

Rent the Runway is a fashion rental business. It handles millions of rental transactions, and its inventory is always moving. Slow analytics kept teams from making decisions in real time. The legacy warehouse could not handle growing amounts of data, and the team had little insight into customer behavior or how well inventory was used.

  1. 01

    The old warehouse could not keep up.

    We led the full move from the legacy systems to Snowflake. It adds compute and storage as needed, for very large amounts of data.

  2. 02

    Answers came too slowly.

    Streaming pipelines bring fulfillment-center data in real time. E-commerce platforms connect through APIs, and legacy systems load in batches.

  3. 03

    The numbers did not agree.

    dbt keeps the transformation logic in reusable models, so the data stays consistent. Automated data quality checks run on top.

  4. 04

    Stock was hidden by location.

    Real-time inventory dashboards show stock across all locations. The analytics layer also covers customer behavior and supply chain optimization models.

The results, in numbers.

60%

Less analytics query time

90%

Faster daily reporting

Real-time

Inventory visibility across all locations

Business impact

  • Better inventory utilization
  • Faster decisions for merchandising
  • Better recommendations for customers
  • Room to scale for peak seasons
  • A base for advanced ML models

Built with

  • Snowflake
  • dbt
  • Airflow
  • Python
  • Tableau
  • Git

Our enterprise work starts the same way, with one trusted data foundation.

What an agent could add next.

An agent is only as good as the data under it, so these ideas start from this foundation.

  • Stock alerts across locations

    Flags items that sit idle in one location while another runs short, so teams can move stock.

  • A morning briefing

    Each morning, a short summary of yesterday’s inventory moves, ready before the first meeting.

  • Ask the data in plain English

    Teams ask a question and get an answer built from the same dbt models as the dashboards.

  • Watch the feeds

    If a feed goes quiet or a quality check fails, the agent tells the data team what it found.