Probably is a secure, local application that lets users ask natural‑language questions of any data source and receive deterministic, verifiable answers backed by the actual data. It connects to files such as CSV, JSON, Parquet and to warehouses like Snowflake, BigQuery, and Postgres, while keeping all sensitive data on‑premise and using cloud AI only for reasoning. The tool also detects data quality issues and learns a user’s business context to improve reporting for roles ranging from CEOs to data scientists.
Funding
$9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations struggle to extract accurate, verifiable insights from large, heterogeneous data sets because existing tools either require extensive manual coding, rely on cloud services that expose sensitive data, or produce results that can include hallucinated figures.
Solution
Probably provides a local‑first data agent that lets users query any data source—CSV, JSON, Parquet, Snowflake, BigQuery, Postgres, etc.—using natural language. The agent runs deterministic analyses on the user’s machine, ensuring that all reported numbers are directly supported by the underlying data and that no data leaves the local environment. It performs real mathematical operations, detects data quality issues, and incrementally learns the user’s business context to improve future interactions. Results are delivered as concise reports or interactive answers, suitable for executives, data scientists, and engineers alike, while maintaining privacy and security.
Target Audience
Primary users are data‑driven teams in enterprises—analysts, data scientists, and engineering leads—who need fast, accurate insights from internal data without exposing it to external services.
Features
- Local‑only processing: all data stays on the user’s device or private network; cloud AI is used only for reasoning
- Natural‑language querying across billions of rows with deterministic, verifiable outputs (no hallucinations)
- Supports a wide range of data formats and warehouses (CSV, JSON, Parquet, Snowflake, BigQuery, Postgres, etc.)
- Built‑in data‑quality detection that flags outliers, missing values, and inconsistent formats before analysis
- Incremental learning of business context (“learning neurons”) to provide more relevant answers over time
- Role‑specific reporting: executive‑level summaries, exploratory data analysis for data scientists, pipeline debugging for engineers
- Integrated math engine that performs exact calculations, statistical functions, and optional ML/anomaly detection in higher tiers