The home page covers why DataGenie exists. This page covers how it works — the lifecycle, the detection pipeline, the story anatomy, and the personas involved. Read this once and everything else in the docs will click into place.Documentation Index
Fetch the complete documentation index at: https://docs.datagenie.ai/llms.txt
Use this file to discover all available pages before exploring further.
The lifecycle
DataGenie is a closed loop, not a report. Data goes in once; stories come out continuously.Onboard with GO
Point GO at a data sample and describe the business problem in plain English. GO scans the schema end-to-end, proposes KPIs and dimensions, picks the right time granularity per KPI, and produces a Blueprint — the complete operational definition of what the dataset monitors. No data engineering. Learn more →
Detect
From just 6 KPIs × 6 dimensions, DataGenie auto-creates and tracks 100,000s of metric combinations. A deterministic ensemble — Prophet, ARIMA, SARIMAX, LightGBM, XGBoost, plus a custom Fourier-based seasonality detector — runs continuously and picks the best forecast per KPI. No threshold tuning.
Connect
Related anomalies — across metrics and across data sources via the Nirvana algorithm — roll up into a single Top Story with root cause and contributors. You get one story, not a flood of alerts. Learn more →
Ask
Wisdom turns plain-English follow-ups into deterministic analytical pipelines. A master coordinator agent picks which of five services (Metric, Insights, Contribution Analysis, Forecasting, Scenario Planning) to run and in what order — and delivers the result as charts, tables, and natural-language summaries. Learn more →
Anatomy of a Top Story
Every autonomous story DataGenie produces follows the same five-part structure. Read one in 15 seconds; know exactly what to do next.| Part | What it answers | Example |
|---|---|---|
| When | Which period | ”Week ending 2026-04-18” |
| Where | Which dimensional context | ”In US, on iOS” |
| What | The headline KPI change | ”Revenue dipped 11%“ |
| What Else | Related KPIs that debunk the obvious cause | ”Visitors +3%, bag rate +2% — so traffic isn’t the issue” |
| Why | The Root KPI + 2–3 Contributors | ”Payment Completion Rate -21%, driven by PayPal on Mobile/iOS” |
Autonomous, by design
No dashboards to build
Stories surface from data, not from the metrics you remembered to configure. The blind spot gap closes automatically.
No thresholds to tune
Anomaly detection is baseline-aware per KPI. Seasonality, zero-handling, and sparse series are handled automatically.
Connected, not isolated
Related anomalies — including across data sources — roll up into one story with root cause, not a flood of disconnected alerts.
Ranked by impact
Impact Score puts the story that actually moves your business at the top. Filter Presets let you scope detection to your team’s focus.
Deterministic core, scoped AI
The autonomous half of DataGenie is entirely deterministic — statistical and ML models running on your data, no generative AI in the loop. The agentic half (Wisdom) uses LLMs only for question interpretation and natural-language summarization, never to invent numbers.Zero raw data to LLMs
Language models only see aggregated KPIs and dimension values. Row-level data never leaves your environment.
Deterministic numbers
Detection, attribution, forecasting, and scenario planning run on proven algorithms. Same inputs, same outputs — always.
Human-in-loop
Wisdom plans, runs deterministic services, and presents results for review. You validate; DataGenie doesn’t guess.
Guardrails you configure
Three surfaces per dataset encode your business rules so Wisdom and Top Stories answer within them — not around them.Domain Knowledge
KPI definitions, business rules, dimension aliases, exclusions. Wisdom respects these on every answer.
Wisdom Skills
Packaged, reusable analytical pipelines in plain language. Invocable with arguments, deterministic output.
Business Events
A documented timeline of real-world context — promotions, outages, seasonality — so anomalies are explained with recorded causes, not invented ones.
Who uses what
DataGenie is built for business users — not developers. Different personas enter the platform from different doors.| Persona | Primary entry point | Secondary |
|---|---|---|
| Business leader (VP, director, exec) | Top Stories + QuickLooks on the home screen | Wisdom for ad-hoc follow-ups |
| Analyst / operator | Wisdom + Explorer + Top Stories | Filter Presets, KPI Attribution |
| Data owner / admin | Datasets + Anomaly Detection | GO for onboarding, RBAC |
| Workspace admin | RBAC + SSO + Users & Mappings | Security & Trust |
The handoff map
DataGenie is a flow, not a collection of tools. Each feature hands off to the next.| Start here | Arc stage | Hand off to |
|---|---|---|
| GO | Onboard — turn raw data into autonomous monitoring | Top Stories, Datasets |
| Top Stories | Discover — autonomous anomaly narratives | Wisdom, Explorer, Alerts |
| Wisdom | Answer — agentic follow-ups, forecasts, scenarios | Dashboards, Wisdom Skills |
| Explorer | Validate — multi-dimensional verification of a Why | Filter Presets, Datasets |
| Dashboards | Share — conversational canvas + QuickLooks | RBAC |
| Datasets | Configure — what gets monitored, how, and by whom | Anomaly Detection, Nirvana |
| RBAC | Govern — three-tier access (Global → Tenant → Dataset) | All features |
Zero rip-and-replace
DataGenie sits on top of the warehouse you already have. Supported sources include Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, and Azure SQL. No data movement, no new pipelines, no months of setup.Connect
Point DataGenie at your warehouse. Read-only credentials; no data leaves your environment.
Configure with GO
GO scans, proposes, asks for domain context, and produces a Blueprint. Hours, not months.
Go live
Stories start surfacing immediately. Autonomous monitoring from day one.
From PoC to production in 8 weeks
The typical path from first connection to production rollout.Go deeper
Autonomous Insights
The detection engine at scale.
HyperConnected
Multi-source insight without ETL.
Responsible AI
Why DataGenie is safe for enterprise use.
Quickstart
Your first insight in minutes.
Core concepts
The vocabulary behind everything above.
Use cases
Concrete playbooks by business problem.