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Probably
Local-first data agent that answers questions over CSV, Parquet, and warehouses with citations and deterministic validation so results can be checked. Probably raised a $9 million seed round led by Andreessen Horowitz and Accel in June 2026.
Analysts and operators who want natural-language answers over local or warehouse data without trusting an unchecked LLM summary.
Teams happy with opaque chat-to-SQL and no audit trail, or buyers needing a full BI semantic layer like Looker.
Verdict
Probably, founded by Peter Elias, builds a verifiable data agent: ask questions, get citations and an audit trail, and bounce answers that fail deterministic checks. Stronger harnesses let weaker, local models suffice. The product site is probably.dev.
Score Breakdown
How Probably scores in the categories that matter to its buyers.
Pricing
Probably has a free Data Intern plan, Data Pro at $399 a month, Data Scientist at $1,499 a month, and a Data Team plan with no listed price.
The Field at a Glance
Where Probably ranks among AI spreadsheet analysts we reviewed, by Overall Score and relative typical engagement cost.
Probably scores 7.3, ahead of Julius AI (7.0) and behind Equals (8.0) and Omni (7.8). Relative cost skews lower when local small models replace frontier token spend.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Cited answers over local files | Strong | Answers over local CSV, JSON and Parquet files cite the computation behind them. |
| Deterministic validation harness | Strong | Outputs are checked against computation traces before they are shown. |
| Full BI semantic modeling | Weak | Probably answers questions and does not build a governed semantic model the way Looker does. |
| Warehouse Q&A with audit trail | Strong | Connectors list Snowflake, BigQuery and Postgres by name. |
| Unverified chat-to-SQL | Poor | Probably checks its answers, which a plain chat-to-SQL tool does not. |
Review Excerpts
Excerpts from company materials and customer stories for Probably, plus a representative buyer view drawn from public information. Paid reviews and pay-for-play sites were excluded.
It asks questions over local CSV, JSON, or Parquet files, or warehouses such as Snowflake, BigQuery, Postgres, and ClickHouse, with validation and citations.
“Every number comes with the computation behind it, so I can check it before it goes in a deck.”
Representative buyer view
“The paid plans start at $399 a month, and the team plan has no listed price.”
Representative buyer view
Where customer reviews couldn't be found, our analysts attempted to give a voice to buyer sentiment, drawing on real information from review sites, forums, and product documentation.
Based on: Probably’s pricing page and TechCrunch
Key Leadership
Peter Elias
Founder
Methodology
This page is an independent evaluation of Probably for buyers comparing options in AI spreadsheet analysts. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Probably did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria fall under it in two groups.
- Buyer outcomes (for AI spreadsheet analysts)
- Verifiable answers & citations
- Local / small-model harness
- Warehouse connectors
- Analyst workflow fit
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the use-case matrix above.
Scope
Probably is graded here as AI spreadsheet analysts. Criteria scores can move as more review volume and product checks are added.