Snowpilot

What do they actually do

Snowpilot is a web app that looks and feels like a spreadsheet but runs live queries against your data warehouse and third‑party tools. It connects to systems like Salesforce, Zendesk, Gong, and Mixpanel/PostHog and lets non‑engineers join and analyze those sources without writing SQL (YC profile, Fondo launch).

Users connect their tools, pick columns, join tables, deduplicate or group rows, and compute simple aggregates in the UI. The result is a live table or view used for concrete tasks (e.g., deal context, feature impact, ticket prioritization) that stays up to date because it links back to the underlying systems/warehouse (YC profile, Fondo launch).

The company is early stage with a small customer base focused on PM, sales, support, and marketing roles. Public materials include a demo/app link, but there is no published pricing page or customer logos yet (Fondo launch, YC profile, LinkedIn).

Who are their target customer(s)

• Product managers at SaaS or data‑driven companies: They need cross‑system answers to prioritize work and measure feature impact but often wait on engineers to join analytics, CRM, and conversation data into one reliable table (YC, Fondo).
• Sales reps and revenue operations teams: They need deal‑level context (CRM records + call notes + support history) to triage and forecast; assembling that context today requires manual exports or engineering help (Fondo).
• Customer support and success teams: They must prioritize tickets by account value, usage, and recent interactions, but bringing those signals together is slow because it relies on one‑off reports or data teams (YC).
• Marketing and growth analysts: They want to join product analytics with attribution and CRM to measure campaign impact without writing SQL; they often export data and stitch spreadsheets, which slows iteration (Fondo).
• Operations/business analysts at small/mid‑market companies without dedicated data engineers: They need recurring, up‑to‑date reports but end up recreating CSVs or depending on scarce engineering bandwidth to keep reports current (LinkedIn, YC).

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led pilots with PM, RevOps, and support leads at companies using Salesforce/Zendesk/Gong/Mixpanel; wire up connectors in hands‑on sessions to deliver one repeatable live table for a concrete question, then convert to paid seats with testimonials (YC, Fondo).
• First 50: Publish a small library of one‑click templates (deal context, feature impact, ticket prioritization) and run short how‑to webinars in PM/RevOps communities; use early case studies and referrals to drive self‑serve trial/demo signups (Fondo, LinkedIn).
• First 100: Add marketplace listings and consulting partner channels, hire CS/sales engineers to run paid pilots for mid‑market teams, and ship SSO/audit/billing so repeatable pilots convert into org licenses at scale (YC, Fondo).

What is the rough total addressable market

Top-down context:

Snowpilot participates in both BI/self‑service analytics and data‑integration/iPaaS spend. Recent reports size BI around the high‑$30Bs and data integration around the mid‑teens (2023–2025 base), implying a practical combined pool of roughly $45–60B annually (Grand View Research – BI, Grand View Research – Data Integration).

Bottom-up calculation:

As a proxy, start with organizations running CRM and/or a cloud data warehouse: Salesforce reports ~150k customers and Snowflake ~11k. Targeting mid‑market teams that adopt a spreadsheet UI for live cross‑system tables at ~$10–20k per team per year, even 1–3% near‑term adoption would yield a low‑hundreds‑of‑millions revenue pool (Salesforce facts, Snowflake PR).

Assumptions:

• Buyer uses at least one major CRM/SaaS source plus a cloud data warehouse (or equivalent).
• Near‑term adoption among relevant orgs is 1–3% while the product is early and team‑focused.
• Average annual contract value is ~$10k–$20k per team for live, recurring cross‑system tables.

Who are some of their notable competitors

• Sigma: Spreadsheet‑style BI on top of cloud data warehouses; strong overlap on no‑SQL exploration. Key difference: assumes the warehouse is the single source of truth and emphasizes governed enterprise workbooks.
• Google Connected Sheets: Runs BigQuery queries from Google Sheets for non‑technical users. Tied to Sheets + BigQuery and not built for broad live joins across many third‑party SaaS systems.
• Hex: Collaborative data workspace mixing notebooks, apps, and visual exploration. More analyst/notebook‑centric and less focused on a pure spreadsheet UX or federated SaaS connectors out of the box.
• ThoughtSpot: Search/AI‑driven analytics for live enterprise data. Different primary interaction (search/AI, embedding) versus Snowpilot’s spreadsheet interface for assembling cross‑system tables.
• Metabase: Open‑source self‑service analytics with visual query builder and dashboards. Better for standard dashboards and questions than for building live, spreadsheet‑style joins across many SaaS tools.