Ask in plain English. No SQL required.
Conversational analytics for PostgreSQL: verified SQL under every chart
Chion writes the SQL, runs it read-only against your own Postgres (RDS, Supabase, Neon, or self-hosted), and shows you the exact query under every chart (capped at 1,000 rows, your credentials never leave the vault). Type a question the way you'd ask a colleague.
What is conversational analytics?
Query data by chatting in plain English. No SQL, no dashboards.
You ask a question in plain English, the system generates and runs a verified SQL query, and returns a chart plus a written explanation. Each follow-up question keeps the context of the previous turn, so you can refine results by saying "break that down by quarter" or "exclude churned accounts." Unlike Google's data agents (scoped to Cloud SQL and AlloyDB) or spreadsheet tools, every answer shows the verified SQL beneath it.
How conversational analytics works
Three steps: connect, ask, get a verified chart.
- Connect your database. Paste a read-only PostgreSQL connection string. Credentials are stored in an encrypted vault and never leave Chion's backend.
- Ask a question. Type it the way you'd ask a colleague: "which customers churned last quarter and what did they spend?"
- Get a verified chart. Chion returns an interactive D3 chart, the exact SQL it ran, and a written summary. Ask a follow-up to refine.
Every answer shows the SQL it ran, and what the data means
Grounded narratives built from actual query output, not generic summaries.
Most analytics tools hand you a chart and leave you to figure it out. We generate a structured narrative for every result: a grounded analysis built from the actual query output, not a generic summary.
Every narrative follows a mandatory arc: a headline that names all series with their direction and explicit date ranges, a key insight that identifies cross-series patterns like shared peaks or divergences, and per-series statistical facts: non-competing observations that each add new information.
Narrative depth scales with the data.
Simple charts get short answers. Complex data gets deeper analysis.
Not every chart needs the same level of narrative depth. A single-series bar chart with 5 rows doesn't need three paragraphs. We compute a data density tier deterministically in code (not by the LLM) and use it to gate which narrative fields get populated.
THIN
1 series, fewer than 10 rowsSummary only. Headline and key insight are set to null.
MODERATE
2–3 seriesQA (question alignment) + summary. Key insight populated when cross-series patterns exist.
RICH
4+ series or high row countAll three fields: headline, key insight, and per-series facts. Full statistical analysis.
Context carries across every turn.
Follow-up questions refine the prior query. No starting over.
Each conversation tracks entities and filters across turns. When you say "break that down by quarter," the system knows you're refining the prior analysis; it doesn't start from scratch. It uses keyword delta tracking to detect what changed between your current question and the previous one.
Filter changes are classified into four operations: add (new filter that didn't exist before), replace (same dimension, different value), subtract (remove a filter), and preserve (carry forward unchanged). The SQL gets rebuilt surgically: only the parts that changed get regenerated. Everything else carries over from the previous turn's verified query.
This is why follow-up questions are fast. We're not re-profiling your schema or re-resolving entities on every turn. We're applying a delta to a known-good query. See how Chion compiles a verified SQL pipeline step by step.
Take your verified queries anywhere: export to Claude Code, Cursor, and Codex
Chion also offers one-shot text-to-SQL generation to skip the conversational layer and paste verified SQL into your own tools, plus an AI SQL analyst for ad-hoc questions.
Need a portable agent file? Chion exports your verified queries as a SQL Analytics Agent file (CHION.md) interchangeable with CLAUDE.md, AGENTS.md, and SKILL.md. Drop into Claude Code or Codex.
Part of the Chion AI SQL workforce, built on the same verified SQL agent architecture.
Chion vs traditional BI + AI copilots
How Chion differs from BI dashboards and cloud-locked data agents
| Feature | Chion | Tableau AI | Power BI Copilot | Looker |
|---|---|---|---|---|
| Pricing model | From $29/seat (Starter); per-team Enterprise | Per-seat license | Per-seat license | Per-seat license |
| Skill library format (CHION.md / SKILL.md plain Markdown vs vendor language) | SQL skill library, plain Markdown, portable | No equivalent | No equivalent | LookML, vendor-specific, locked |
| Your queries stay portable: CHION.md runs in Claude Code, Cursor, or Codex | ||||
| Connects directly to live PostgreSQL | Extract or live | Extract or live | LookML layer | |
| No ETL / pipeline / modelling required | ||||
| Ask ad-hoc questions in plain English | via Looker AI | |||
| SQL shown under every answer | LookML, not SQL | |||
| Two-layer SQL validator before execution | ||||
| Time to first chart (new schema, no prior modeling) | Under 5 min | Hours to days | Hours to days | Days (LookML setup) |
| Grounded narrative with every chart | Explain Data | Smart Narratives | ||
| Starting price per seat | $29/mo | ~$115/mo (Creator) | $14/mo + Fabric capacity for Copilot | Quote-based |
Competitor pricing at public per-seat rates (Tableau Creator billed annually; Power BI Pro plus Fabric capacity for Copilot). Looker pricing not publicly listed. The "Skill library format" row reflects Chion's architectural differentiation: Chion ships a portable, plain-Markdown skill library; AI copilots ship vendor-locked modeling languages.
See our vault-encryption and read-only SQL security model → · Full Chion vs BI dashboards comparison →
Frequently asked questions
9 answers about conversational analytics.
What is conversational analytics?+
Conversational analytics lets you query data by typing plain-English questions instead of writing SQL or navigating dashboards. The system generates verified SQL, runs it against your database, and returns an interactive chart with a written explanation.
How is conversational analytics different from traditional BI dashboards?+
Dashboards show pre-built views that someone else configured. Conversational analytics lets you ask any question on the fly: no ticket, no analyst, no wait. Each answer includes the SQL so you can verify it.
Do I need to know SQL to use Chion?+
No. You ask questions in plain English. Chion generates the SQL, verifies it against a contract, and runs it. The SQL is visible if you want to inspect it, but you never need to write it.
What databases does Chion support?+
Chion connects to PostgreSQL via a read-only connection string. Your credentials are stored in an AES-256-GCM encrypted vault and never leave Chion's backend.
Is my database data safe? Can Chion write to my tables?+
Chion enforces read-only SELECT queries with a hard row limit. It cannot INSERT, UPDATE, DELETE, or ALTER your data. Every query passes a read-only check before execution.
How does Chion verify the SQL is correct before running it?+
Each generated query passes through a multi-phase verification pipeline: schema alignment, contract enforcement, and a read-only safety check. Invalid queries are rejected before they reach your database.
Can I see the SQL Chion generated?+
Yes. Every chart includes the exact SQL that produced it. Click the query to view, copy, or run it independently in your own database client.
What happens when I ask a follow-up question?+
Chion tracks entities and filters across turns. When you say 'break that down by quarter,' it refines the prior query surgically: only the changed parts are regenerated. Everything else carries over from the verified previous turn.
How is Chion different from Google's conversational analytics for Cloud SQL and AlloyDB?+
Chion works on any PostgreSQL (RDS, Supabase, Neon, or self-hosted), shows the exact SQL under every chart, enforces read-only with a 1,000-row cap, and exports every answer as a portable agent file. Teams fully committed to GCP with data already in AlloyDB get native IAM integration from Google's agents; that remains the deeper choice inside Google's cloud.
Point Chion at your Postgres and ask.
Point it at your database and ask something. See what comes back.