Product analytics without the SQL bottleneck
PMs ship features and need to know if they worked — without waiting two weeks for the data team. AnalityQa AI reads your product database directly so you ask the question and get the chart back.
Where the day goes wrong
Every retention or adoption query goes through the data team
You ship a feature on Tuesday. By Friday you want to know who used it and how it affected retention. The data team needs three days to write the query, validate the dashboard, and get back to you. By then the next sprint has started.
A/B test results are reported in tools that hide the underlying data
Your experimentation tool shows the headline lift but doesn't let you slice by segment, by usage frequency, or by integration with revenue. Half the time the test "won" but you suspect it only won for one segment — proving it requires pulling the raw data.
Activation and engagement definitions drift across teams
Engineering measures "active" by login. CS measures it by feature use. Product measures it by depth of session. Each team's dashboard tells a different story. Aligning on definitions is a months-long project that nobody wants to lead.
Funnel drops are noticed late and diagnosed slowly
A signup funnel drops 8% on Wednesday. By the time the dashboard refresh runs Friday morning, two days of cohort data are already affected. Diagnosing whether it's a real change or sampling noise takes another week.
What you actually ask AnalityQa
Plain English in. Charts, tables, and live dashboards out.
What's the activation rate for users who signed up in the last 30 days, defined as: connected a data source AND ran 3+ queries within 7 days?
→ Activation funnel with the definition shown and the cohort size confirmed.
Compare retention at month 3 between users who completed onboarding and users who skipped it.
→ Two retention curves with the gap quantified and statistical significance flagged.
Did the new feature we shipped last week affect retention for early adopters?
→ Cohort comparison: feature-users vs non-users, retention curves overlaid.
Why did the signup-to-activation conversion drop 12% this week?
→ Investigation report with segment splits and ranked drivers of the drop.
Is our latest A/B test winning across all segments, or only for the high-engagement cohort?
→ Per-segment lift table with confidence intervals shown.
Build me a feature-adoption dashboard for the past 90 days with usage frequency, retention impact, and revenue correlation.
→ Live dashboard URL refreshed daily, shareable with the cross-functional team.
How AnalityQa fits your workflow
Five capabilities — every persona uses all of them, in their own way.
Chat with your data
Ask product questions in plain English: feature adoption, retention impact, segment differences. AnalityQa AI runs the SQL against your product database directly — no waiting on the data team to translate your question into a query.
Auto data prep
Product event data is messy: duplicate events, missing user IDs, inconsistent property names. AnalityQa AI's data prep step flags these before they pollute your activation or retention numbers.
Live shareable dashboards
Pin per-feature adoption dashboards, the activation funnel, the retention heatmap. Each refreshes against the live product DB. Share with engineering, design, and CS — they all see the same source of truth.
Investigation mode
When a metric moves, ask why immediately — not next week. AnalityQa AI runs a structured investigation across product events, segment mix, and recent releases. Returns a ranked list of contributors, so you know whether the drop is real, where it's concentrated, and what to do about it.
Data-aware analyst agents
Product-aware analyst agents monitor your KPI thresholds (activation, day-7 retention, NPS) and proactively flag anomalies — including segment-level regressions that aggregate views miss. PMs walk into the weekly review with the issues already identified.
What changes
Use cases relevant to your role
Product
Know If Your Feature Launch Is Actually Working
Product
A/B Test Results That Tell You What to Ship
SaaS / Product
Find the activation moment that converts trials
SaaS / Customer Success
Find Where Onboarding Breaks and Fix It With Data
Product / Growth
Why your funnel is leaking, with the receipts
SaaS / Customer Success
Cohort retention without the spreadsheet that nobody updates
Frequently asked questions
Do we need PostHog or Mixpanel for this to work?+
No. AnalityQa AI reads from any source where your product events live: Postgres, MySQL, BigQuery, Snowflake, or CSV exports of PostHog/Mixpanel/Amplitude. The tool you use to capture events is irrelevant — only the underlying data needs to be queryable.
How does it handle event schemas that change as the product evolves?+
AnalityQa AI re-scans the schema on each refresh and flags changes (new event types, deprecated properties, renamed fields). The chat shows you which event names exist and lets you map old to new explicitly so historical analyses don't break.
Can we define activation per persona — e.g. activation means different things for marketers vs analysts?+
Yes. Per-persona definitions in one prompt: "For marketers, activation = ran a campaign report. For analysts, activation = connected a data source and ran a SQL query." AnalityQa AI applies the right rule based on the user's persona attribute.
Does it integrate with our experimentation tool?+
It reads from the underlying data — assignment events and outcome events — so it works with any experimentation tool that writes to your warehouse or product DB. Optimizely, GrowthBook, in-house homegrown — all the same to AnalityQa AI.
How does it identify segment-level regressions that aggregate views miss?+
When you ask "what drove the drop in conversion?", AnalityQa AI doesn't just look at the aggregate — it scans your typical segment cuts (channel, plan, device, geography) for anomalies and ranks by contribution. The structural search means a 30pt drop on a small segment doesn't get hidden by a stable aggregate.
Can engineering and design see the same dashboards?+
Yes — share the public dashboard URL. Anyone with the URL sees live data without an AnalityQa AI account. Many product teams have a 'product health' dashboard pinned in the team Slack channel that everyone refers to in standup.