How Suri used internal spreadsheets as evals for their semantic layer
Metric definition was Suri's biggest concern about moving to a warehouse. They've now certified every core metric at speed, and every dashboard and report pulls from that one set.
“Not long ago the answer to almost any question here involved some form of hand-edited spreadsheet export. In just shy of two months working with Rig we've flipped the script on how traditional data infrastructure projects normally go, centralising 95% of the company's sources and getting a solo data function outputting what would usually take a 10+ person team.”
TalAI and Data Strategy Manager @ SuriTal walked through this workflow in detail at Rig's client showcase.
Teams using Claude got different numbers for the same question
Ingestion was the easy half. Once 40+ sources were in one place and anyone could ask a question, the answer still depended on whose definition of the metric was reached for.
Siloed divisions, no shared view
Fast growth split the company into divisions that could not see each other's work. Commercial had no view of marketing or growth, and each function reported off its own spreadsheet, so the same metric meant different things to different people.
Metric definitions were the bottleneck
Getting to agreed definitions for net versus gross revenue, contribution margin, AOV and LTV was the hard part. Data consultancies quoted long, expensive, opaque engagements to do it.
Attribution broke at checkout
iOS checkouts severed the source chain. Northbeam and Triple Whale promised a clean cross-channel picture but shipped fixed dashboards that were hard to change and not actionable.
Your existing reporting is already the eval set
Suri already had a spreadsheet the whole company trusted: a monthly tracker finance kept, estimating where revenue was landing against target. Rather than treat it as legacy to be replaced, Tal treated it as ground truth, and the numbers it held became the test every new definition had to pass.
Existing dashboards went through Claude and Rig together to map the business logic behind every definition. Rig then rebuilt each metric from the warehouse and scored it against the tracker, only passing it to a human when the two landed inside an error boundary. Sign-off stopped being a quarter of back-and-forth between data and finance and became one 50-minute meeting.
How they did it with Rig
Rig ingests Suri's commercial stack into one governed context layer, joined on Shopify orders. Certification then ran as a loop rather than a project: every definition is mapped, rebuilt and scored before a human is asked to look at it.
Suri's existing dashboards go through Claude and Rig together, so the business context and the warehouse are read side by side, and the logic behind every definition across the company is written down rather than remembered.
Rig rebuilds each metric from the warehouse and scores it against the same figure in the monthly tracker. Agree inside the error boundary and it goes to a human; miss and it never reaches one.
Only then is the definition certified. Running the tracker as the eval set also caught errors in the tracker, including SKUs missing their cost of goods and costs being rolled up into revenue that did not belong there.
Then, on top of the certified layer
Once the definitions were signed off, the same layer carried two more things.
- Anyone can query the warehouse in Claude, with no queue behind the data team
- Certified definitions travel with every answer
- Numbers stay consistent across finance, marketing and growth
- Own cross-channel attribution model, geo-split MMM to read the incrementality of spend
- Tracks spend and volume around peaks like Prime Day, derived from Klaviyo
- Marketing, influencer and leadership summary dashboards
The impact
| Area | Before | With Rig |
|---|---|---|
| Metric definitions | Months of consultancy back-and-forth, and definitions drifting between teams | Auto-evaluated against existing reporting, human-reviewed only on match |
| Cross-team access | Everything queued behind the data team | Self-serve via Rig MCP in Claude, on certified metrics |
| Attribution | Fixed Northbeam and Triple Whale dashboards, with iOS gaps | Own cross-channel model with geo-split MMM for incrementality |
“Not long ago the answer to almost any question here involved some form of hand-edited spreadsheet export. In just shy of two months working with Rig we've flipped the script on how traditional data infrastructure projects normally go, centralising 95% of the company's sources and getting a solo data function outputting what would usually take a 10+ person team.”
TalAI and Data Strategy Manager @ Suri