Autolake automates your entire data lifecycle and runs governed AI on top — all inside your own cloud.
Your data lake ran smoothly today with 42 of 47 tables refreshed across 8 active sources, maintaining a strong 97.8% pipeline success rate and 94.2% data quality score.
Quality score sits at 94.2% — validation rules are consistently passing across all core oracle-prod and mysql-analytics tables.
Investigate why the email_campaigns pipeline hit rate limit errors twice today and review the API quota.
Built by the team behind lakehouses at
Santa Clara County
Santa Clara CountyGeneric AI fails on enterprise questions because the data underneath is scattered, stale, and ungoverned. Autolake fixes the data first — so the AI on top can be trusted.
AI answers come from curated, governed datasets — not whatever happened to be lying around.
The AI sees exactly what each user is allowed to see. Access controls carry through to every answer.
The platform, the data, and the AI all run inside your own AWS account. Nothing leaves it.
Ingestion to distribution, AI chat to agents, alerts to scheduled reports — the entire data lifecycle runs in one platform, autonomously, inside your AWS account.
Point Autolake at a source and it handles discovery, cataloging, and loading — no pipelines to write.
A custom build-out takes months and millions. Autolake is six steps from zero to a fully managed, production-ready lakehouse — no pipelines to write.
Enter your lake name, budget, and team tags. Autolake handles the rest.
Pick your database, API, or file source — and start ingesting in clicks.
Transform, mask, and prepare analytics-ready datasets with built-in SCD support.
Share curated data via REST APIs, BI tools, and AI/ML platforms instantly.
Track usage, cost, and performance from a single built-in dashboard.
Ask questions in plain English and get instant answers powered by your curated data lake.
And the depth underneath — everything an enterprise lakehouse needs:
Connectors
Pre-built integrations for databases, APIs, SaaS, and more.
Setup time
From zero to running data pipeline, fully configured.
Reliability
Enterprise-grade uptime with automatic error recovery.
Autonomous features
From PII detection to self-healing retries — no babysitting required.
Autoflow is the agent builder inside Autolake. Describe what you want — an agent assembles the workflow on a visual canvas, wires it to your lake and your apps, and runs it under your rules.
passfail
SQL Server connected
1,000 tables
AI maps schemas
Bulk ingest
parallel pipelines
All loads OK?
Load to lakehouse
curated zone
Notify team
Retry failed tables
Open an issue
Chat model
Source schemas
Naming rules
Tell the built-in agent what you want — it assembles the workflow on a visual canvas, ready to run.
Lake events, schedules, and SaaS apps can all start a flow — the same 250+ connectors, now as triggers and actions.
Governed by default: flows pause for sign-off at the steps you choose, and every run is audited.
Autolake deploys into your AWS account and is built entirely on AWS-native services — no third-party tooling, no vendor SaaS in the middle, for the platform or for the AI. Your data, your keys, your audit trail.
Access
Permissions are enforced at the lake level — and the AI inherits them on every query.
Privacy
Sensitive fields stay masked for people and AI alike, by policy rather than convention.
Provenance
Every record is traceable from source to answer, so AI output is explainable, not a black box.
Audit
Every access — human or AI — lands in your audit trail, inside your own account.
The same autonomous lakehouse, tuned to the compliance frameworks and source systems of your world.
See how teams go from raw data to a fully managed lake in under five minutes.
Get started
See it live on your own sources — a 30-minute walkthrough with a founding engineer. No prep needed.
