Your data platform, operated by agents that never sleep.
Atlas AI plans, writes, tests and repairs production pipelines across your warehouse and lakehouse. Every change ships as reviewable code with lineage, contracts and rollback built in.
Deployed inside your VPC · No training on customer data
94%
Pipeline incidents auto-resolved
11x
Faster time-to-first-model
38%
Average warehouse spend reclaimed
Why DataHub
Agents are only as trustworthy as their metadata
Most AI coding tools infer schema and ownership from context and hope it's right. Atlas doesn't guess — every decision it makes is grounded in DataHub, the metadata platform your team already treats as source of truth.
No guessed schemas
Every column, type and constraint Atlas writes against comes from DataHub's live schema registry, not a model's best guess.
No guessed owners
Ownership, on-call routing and approval chains are read directly from DataHub, so PRs land with the right reviewer automatically.
No guessed lineage
Atlas plans against DataHub's real dependency graph, so a fix upstream correctly accounts for every model and dashboard downstream.
Native integrations across the modern data stack
Built for the teams that keep data trustworthy
Atlas AI slots into the stack you already run — Snowflake, BigQuery, Databricks, Postgres, Kafka and dbt — and takes over the repetitive engineering underneath it.
Data platform teams
Hand the on-call pager to agents. Atlas triages broken DAGs, backfills gaps and opens fixes before the morning standup.
- Autonomous incident triage
- Backfill orchestration
- Warehouse cost guardrails
Analytics engineering
Ship models faster with agents that write dbt transformations, tests and documentation against your semantic layer.
- Model scaffolding
- Test & doc generation
- Semantic layer sync
ML & AI platforms
Keep feature pipelines fresh and reproducible, with drift detection wired straight into your training schedule.
- Feature freshness SLAs
- Drift detection
- Reproducible snapshots
Regulated enterprises
Every agent action is policy-checked, logged and reversible — designed for finance, health and public-sector controls.
- Policy-as-code review
- Immutable audit trail
- Private VPC deployment
Six specialists, one accountable system
Each Atlas agent owns a narrow slice of the data lifecycle and hands off through a shared plan. You approve the boundaries; they do the work.
Atlas Ingest
RunningSource connection & landing
Connects new sources, infers schemas and lands raw data with idempotent loads and automatic replay on failure.
Atlas Model
Awaiting reviewTransformation authoring
Writes and refactors SQL and dbt models from intent, complete with tests, documentation and lineage annotations.
Atlas Sentinel
RunningQuality & contracts
Watches freshness, volume and distribution signals, enforcing data contracts before bad rows reach consumers.
Atlas Repair
RunningIncident resolution
Diagnoses failed runs, reproduces them in a sandbox and opens a reviewed pull request with the smallest safe fix.
Atlas Ledger
ScheduledCost & performance
Rewrites expensive queries, tunes clustering and right-sizes warehouses against the budget your finance team set.
Atlas Warden
Awaiting reviewGovernance & access
Classifies sensitive columns, applies masking policies and produces the audit evidence reviewers ask for.
From connection to autonomy in four moves
No rip-and-replace. Atlas starts read-only, earns trust on low-risk work, then takes on more of the pipeline as your guardrails allow.
Connect and map
Atlas reads your warehouse metadata, orchestrator history and repository to build a live map of every table, job and dependency.
Set the guardrails
Define budgets, protected datasets, approval rules and SLAs as policy. Agents can only act inside the boundaries you sign off.
Agents plan and build
Work arrives as an explicit plan: what changes, why, expected cost and blast radius. Approve once and the fleet executes.
Operate and improve
Atlas keeps watching after merge — repairing failures, tightening contracts and reclaiming spend with every run.
Autonomy you can actually put in front of an auditor
Most tools generate code. Atlas takes responsibility for the outcome — proving each change is safe before it ever reaches a consumer.
Median repair time
3m 12s
Changes shipped as PRs
100%
Reviewable by design
Agents never touch production directly. Everything lands as a pull request with a diff, a rationale and a rollback path.
Lineage-aware reasoning
Atlas plans against a live dependency graph, so a fix upstream accounts for every downstream model and dashboard.
Runs in your perimeter
Deploy inside your own cloud account. Credentials stay yours and your data is never used to train models.
Cost is a first-class metric
Every plan estimates compute before it runs, and the Ledger agent works continuously to keep spend under budget.
Minutes, not sprints
Median incident repair is three minutes. New source onboarding that took a quarter now closes in an afternoon.
Your stack, your standards
Atlas writes in the conventions of your repo — naming, folder structure, testing patterns and style guides included.
Trusted where downtime is expensive
Atlas AI runs inside logistics, financial services and life-sciences platforms with strict review and residency requirements.
“We retired an entire rotation of overnight pipeline babysitting. Atlas files the fix, we review it with coffee, and the warehouse is green by 8am.”
Nadia Okonjo
Head of Data Platform, Northlane Logistics
“The plan-before-execute model is what got it past our risk committee. Nothing moves without a diff, an owner and an estimated cost.”
Marcus Feld
VP Engineering, Cindral Financial
“Onboarding a new source used to be a two-sprint project. Our last one took a single afternoon, tests and documentation included.”
Priya Raghavan
Analytics Engineering Lead, Verabio
What teams ask before switching on autonomy
If something here isn't covered, our engineers will answer it live during a demo.
No. Every agent-authored change lands as a pull request with a diff, a rationale and a rollback path. Nothing merges without human approval unless you explicitly configure auto-merge for a narrow, low-risk policy.
Snowflake, BigQuery, Databricks and Postgres for warehouses; dbt, Airflow, Dagster and Kafka for transformation and orchestration — with more connectors added regularly.
Atlas deploys inside your own cloud account. Your credentials and data never leave your perimeter, and nothing is used to train models.
Most teams are connected and mapped within a day. Agents start in a read-only advisory mode before you grant write access to any dataset.
Every change ships as a reviewable PR with an estimated cost and blast radius, so mistakes are caught before merge. Post-merge, the Repair agent can revert or patch automatically.
Pricing is based on managed pipeline volume and agent activity. Talk to us for a plan sized to your warehouse footprint.
Give your pipelines an engineering team that never clocks out
Bring one broken pipeline to a 30-minute session. We'll connect Atlas AI live and show you the fix it proposes before you leave the call.
No credit card · Read-only pilot · Cancel anytime