picoask isn't a chart generator with a chat box. Every question runs through a planner-driven pipeline of specialist agents that form hypotheses, test them against your live data, and only then write the answer — with the evidence attached.
Before any analysis runs, picoask checks that your question is answerable: it asks a clarifying question if the ask is ambiguous, and verifies your data is fresh enough to trust — so you never get a confident answer built on a stale table.
A planner decides which specialists the question needs: a hypothesis agent proposes competing explanations, a data-retrieval agent fetches exactly the data required, and statistical agents test each hypothesis against your data.
The reporting stage picks the right visualization, writes a business-readable answer, and attaches everything it used — the queries, the tables, the significance tests — plus the concrete next step the finding suggests.
Every "X changed" claim carries a verdict: significant or not, with the test and p-value shown. A 4% lift that's indistinguishable from seasonality gets called exactly that.
Every check runs through picoask's library of proven statistical algorithms — your data is fed into the algorithm and the result is computed exactly, never estimated by a language model doing arithmetic.
A dedicated classifier labels each relationship it reports: correlated, plausibly causal, or confounded — so a finding never quietly overstates what the data can support.
Metrics are checked against their own history — trend, seasonality, and noise — so "this week looks weird" comes with a baseline, not a hunch.
On connect, picoask introspects your schema and writes a plain-English description of every table and column — which you review, correct in a chat, or reject before it's used.
It maps your business metrics and how they drive each other — revenue ← conversions ← traffic — so root-cause questions follow real relationships instead of assuming every relationship is true.
Schemas change. picoask scans for changes automatically — and you can trigger a scan any time. Anything new or altered shows up with an AI-drafted description — approve it, refine it in chat, or reject it. Your project never silently rots.
Put a watch on the metrics you care about and picoask monitors them continuously, notifying you when one moves in a way its history says it shouldn't.
Any answer can become a recurring report — re-run on your schedule and delivered to your email, with the same statistical scrutiny every time.
Tell it what the data can't know — a pricing change, an outage, a campaign launch — and it uses that context when explaining anomalies. Add facts with /memory.
picoask connects with a read-only database role and verifies it really is read-only before every query. It cannot write to, alter, or delete anything in your database.
Raw rows stay inside the query and computation layer. No LLM agent gets access to your raw data — agents work from your schema, table descriptions, and computed results.
Connections come from a small, stable set of published IPs you can allowlist. Your connected data is never used to train foundation models without your explicit consent.
Connect PostgreSQL, MySQL, Amazon Redshift, Snowflake, or BigQuery directly. Pull Meta Ads, Google Ads, and GA4 into a managed warehouse with first-party connectors. A single project can span multiple sources — and picoask proposes the cross-source joins for you.