Product analytics with an AI analyst.
Ask a question in plain English, get a chart, cohort or funnel with the SQL to back it up — no dashboard building required. Turn every metric and event into a decision — with warehouse-native analytics, anomaly detection and narrative reporting that answer business questions in seconds, not sprints.
Overview
A single home for ai analytics.
AI Analytics closes the loop between your data warehouse and the people who need to act on it. Instead of dashboards no one reads, teams get answers to real business questions in seconds — with the SQL, lineage and permissions to back them up.
Queries run in place on Snowflake, BigQuery, Databricks or Postgres, honor row-level security, and are governed by a semantic layer your data team owns. Insights don't stop at a chart — they route into workflows, alerts and executive narratives that get read.
50%
fewer tools
4×
faster time to value
99.99%
uptime
SOC
2 · HIPAA
Problems it solves
What breaks without AI Analytics.
The pain points data & analytics teams tell us they hit before adopting AI Analytics — and how the platform removes each one.
Dashboards no one opens
Static BI dashboards answer last quarter's questions, not this week's.
Ad-hoc analysis takes days
Every stakeholder request queues behind the data team, and the answer arrives too late to matter.
Metrics that don't agree
Different teams calculate revenue, activation and retention differently, so decisions are arguments.
Missed anomalies
Silent regressions and cost blowouts go unnoticed until finance or a customer surfaces them.
Insights that don't drive action
Even good insights sit in a chart because there's no path from finding to workflow.
Governance vs. speed tradeoff
Locking down data slows the business; opening it up creates PII and compliance risk.
Capabilities
Everything you need to ship.
Natural-language queries
Auto-joined events, sessions and revenue.
Anomaly detection
Weekly digests that rank changes by business impact.
Warehouse-native
Query in place on Snowflake, BigQuery, Databricks or Postgres.
Technology
Built on production-grade primitives.
AI Analytics is engineered for teams that need to run AI in production — with the reliability, observability and portability that regulated, high-volume workloads demand.
- Warehouse-native execution on Snowflake, BigQuery, Databricks and Postgres
- Semantic layer with dbt-friendly metric definitions
- LLM-to-SQL with schema-aware validation and lineage
- Anomaly detection ranked by revenue and user impact
- Row-level and column-level security honored end to end
- Narrative summarization with model-agnostic routing
- SOC 2, ISO 27001, HIPAA-eligible; regional data residency
Why teams love AI Analytics
Outcomes, not outputs.
The measurable benefits data & analytics leaders report after standardizing on AI Analytics.
Every PM becomes an analyst
AI Analytics lets non-technical users ask questions in plain English and get answers backed by trustworthy SQL.
Warehouse-native and secure
Queries run in place on Snowflake, BigQuery, Databricks or Postgres — data never leaves your perimeter.
Governed metrics
A semantic layer your data team owns keeps every stakeholder on the same definition of revenue, retention and activation.
Proactive anomaly detection
Weekly digests rank changes by revenue and user impact so teams focus on what actually moved the business.
Narrative reporting
AI-generated commentary on what changed and why — delivered to Slack, email or a PDF board pack.
Insight-to-action loops
Approve a recommendation into a workflow with one click — insights don't die in a dashboard.
Row-level security respected
Every AI answer respects the same permissions as your BI tool — no data leaks to the wrong team.
How we deliver
From setup to signal in weeks.
Every AI Analytics rollout follows the same repeatable process — designed with your engineering, product and compliance teams in the loop from day one.
1
Connect
Point Flyps at your warehouse and semantic layer — permissions, lineage and freshness are honored automatically.
2
Model
Data team defines governed metrics; stakeholders query them in plain English without breaking definitions.
3
Ask & explain
Business users ask questions; AI Analytics returns charts, cohorts and narratives with the SQL to back them up.
4
Act
Turn insights into workflows, alerts or executive narratives with one click.
Expected results
Business impact you can measure.
Typical outcomes teams report within the first two quarters on AI Analytics — instrumented against the metrics your leadership already tracks.
6×
faster reporting cycles
90%
of stakeholder questions answered without a data ticket
3×
sooner detection of revenue anomalies
Hours
saved every week per business team
Use cases
Where teams use AI Analytics.
Feature adoption
Retention
Attribution
Exec reporting
Integrations
Plays well with your stack.
First-party connectors — plus 300+ more across the Flyps platform.
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ExploreFAQ
Answers about AI Analytics.
The questions engineering, product and compliance leaders ask most often before rolling AI Analytics out — with detailed answers on implementation, technology and security.