Greg from Analytics
“How is GMV pacing this week?”
Pacing looks strong - roughly +10–15% WoW.
unsourcedPacing +12% WoW · on track for W19
+ metric: GMV+ source: dbt.metrics+ as_of: 2026-W19
Cut inference cost ~90%. Ground every answer in verified business context.
Context verified by your team, not guessed by the model.
Analytics teams spend ~$20k per employee per year on model APIs. Every prompt re-scans your warehouse, re-discovers your metric definitions, and re-hallucinates business context. Open weight models are cheap — but without a harness, they're just as blind.
“How is GMV pacing this week?”
Pacing looks strong - roughly +10–15% WoW.
unsourcedPacing +12% WoW · on track for W19
“What's my Q3 pipeline look like?”
Around $4M in open pipeline.
unsourced$4.2M open · 78% to quota · 3 at risk
“How is eng hiring tracking?”
Eng hiring is on track.
vague12 hired · 5 offers out · on pace for 18
Greg gets a different GMV story every time. Proprietary models re-invent your definitions instead of reading them.
Open weight models cost a fraction — but without routing and grounding, teams default to GPT-4 for everything.
Token spend compounds fast. Every analytics query re-scans your warehouse and re-pays for the same context.
Butter routes analytics queries to the cheapest capable open model and pre-attaches verified business context before inference. Consistent answers, zero hallucinated metrics, ~90% less spend.
See how onboarding worksRoutes each query to the cheapest capable model — Llama, Mistral, Qwen. No more paying GPT-4 prices for every analytics lookup.
Shared prefixes computed once, never re-paid. Your metric definitions and business context load at the edge.
Every answer anchored in verified context from your warehouse, dbt, Notion, and Slack — scoped per team.
From ~$20k to ~$2k per employee per year. Butter charges 10% of what you save.
We're rolling out access by team. Drop your details and we'll reach out within a few business days.