How is this different from Monte Carlo?

Monte Carlo tells you the pipeline broke. We tell you why, what to fix, and we remember it for next time. They are a smoke detector. We are a firefighter with institutional memory.

How does contract auto-generation work?

Connect your repo (GitHub, GitLab, Azure DevOps). LakeLogic scans your SQL, dbt models, and Spark jobs, infers schemas and dependencies, then generates production-ready contracts automatically.

How are contracts enforced?

Contracts validate at runtime or in CI/CD. If a change violates a contract (schema drift, quality rule failure), the pipeline fails early — bad rows route to quarantine with exact failure reasons.

Can business users define context without YAML?

Yes. The visual Contract Studio lets non-technical users add business context, ownership, and SLAs. Changes sync back to your version-controlled source of truth automatically.

We only parse metadata (table names, column names, lineage) — never your actual row-level data. Telemetry from your OSS pipelines carries rule names and row counts only. SOC 2 Type II certification is on our roadmap.

Data contract platform · Agentic incident resolution

Define data trust once. Enforce it everywhere.

Bad data breaks dashboards and decisions — usually too late to catch. LakeLogic turns your quality, ownership, and compliance rules into contracts that block bad data before it reaches production.

Git-native — issues caught in review, before they merge.

Start on GitHub Talk to us

Zeus AIAgentic resolutiondiagnoses, recalls the fix, drafts the PR

3 enginesOne contractPolars · DuckDB · Spark

Zero egressMetadata onlyyour warehouse stays your warehouse

Apache 2.0Open core foreveraudit, fork, self-host on GitHub

Where LakeLogic sits

Between observability and governance. Contract-first.

Observability

Monte Carlo · Bigeye · Anomalo

Watches pipelines after they run. Detects anomalies once they've already hit production.

·Post-hoc detection
·You learn after the dashboard breaks
·No PR-time enforcement

AI Incident Resolution

LakeLogic

Defines trust before the run, enforces it during the run, proves it after. Across every engine.

Block unsafe changes in PRs
Quarantine bad rows at runtime
Zeus diagnoses incidents and reduces MTTR
Polars · DuckDB · Spark · Delta · Iceberg

Governance & Catalog

Collibra · Atlan · Informatica

Documents pipelines after they exist. Heavyweight rollouts, separate from engineering workflow.

·Documentation-led
·Lives outside the PR workflow
·6-month rollouts, 6-figure prices

Most teams buy separate tools for detection, governance, and remediation. LakeLogic compresses the three jobs into one operating layer — built around the contract, not the dashboard.

How Zeus Learns What's Correct

Contracts teach Zeus your standards.

Every contract — quality rules, owners, SLAs, PII flags — is one more thing Zeus knows about your data. Define it once in a visual editor or plain YAML; Zeus uses it to detect drift, route quarantines, and explain incidents in your team's vocabulary, not generic ML.

Bad data never reaches your dashboards.— Quarantine routing on every pipeline run
Business users and engineers edit the same contract.— Visual editor for ownership, SLAs, and PII tags; plain-text for code reviews
Generate a contract in minutes from code you already have.— Zeus reads your pipelines (AI-assisted)
Change reviews built into your workflow.— Git + pull requests, no new tools to learn
Open standard — works with your existing tooling.— Plain YAML, no proprietary format

Runs on

One contract. Three execution paths.

The same YAML runs as native code or on any Spark runtime, and reads from / writes to your warehouse — without rewriting transformations.

Native engines

In-process. Zero JVM. Today.

Spark

DuckDB

Polars

Spark runtimes

Anywhere PySpark runs. Today.

Databricks

Fabric

Also runs on: AWS EMR, AWS Glue, Azure Synapse, GCP Dataproc — anywhere you can pip install lakelogic.

Warehouses

Read / write today

Native SQL pushdown on the roadmap.

Snowflake

BigQuery

Redshift

How: pull data in via the integrated load layer, transform on Polars/DuckDB/Spark, write results back.

Company Brain

Every incident makes Zeus smarter.

Over 6 months, teams go from 45-minute incident resolution to 5 minutes. The Knowledge Base becomes institutional memory that never leaves with people.

↻ Next incident auto-suggested

Zeus root-cause

Stripe webhook schema drift —
field customer_email changed shape upstream.

Contract:bronze_stripe_payments@v2.1

Run ID:5574_1113-d76b-4bd9

Lineage:Bronze → Silver → 3 downstream Gold tables

Git-native

Enforced in your PR workflow

Memory

Never solve the same incident twice

Knowledge lost to turnover

Zeus ROI

What that’s worth to your team.

Move the sliders to match your team. The number on the right is the engineer-hours Zeus reclaims every year.

Your setup

Active pipelines25

1500

Pipeline incidents per month30

0150

A healthy 25-pipeline team typically sees 10–40 incidents/month. The pipelines slider above just sets context.

Hours wasted per incident (today)6 hrs

1 hrs24 hrs

Loaded data-engineer cost / hour$100

$50$300

Assumption: Zeus diagnoses ~80% of incidents to a resolution in under an hour. Untouched incidents fall back to your current time-to-resolve. Adjust your hourly cost to match fully-loaded salary + benefits.

Annual impact

Reclaimed eng cost / year

$144,000

≈ 1,440 engineer-hours reclaimed — 0.7 FTE of capacity returned to feature work.

Incidents / yr

360

Cost without Zeus

$216,000

Cost with Zeus

$72,000

Hours saved / yr

1,440

Estimates only. Real savings depend on incident mix, on-call structure, and how quickly your team adopts Zeus suggestions.

Have questions? Most teams do.

The short answers below cover what we get asked most often. If you don't see yours, the founders read every inbound — reach out directly.

Talk to the founders

Built for the security team too

Your data never leaves your lakehouse. Period.

Metadata only

We process schemas, lineage, rule names, row counts. Never row-level data. Your warehouse stays your warehouse.

Open source core

The runtime engine is Apache 2.0 on GitHub. Audit the code. Self-host the OSS forever. No vendor lock-in by design.

GDPR-ready primitives

PII flagging, masking strategies, and right-to-be-forgotten erasure are first-class — built into the contract, not bolted on.

SOC 2 on the roadmap

Pre-launch and pursuing SOC 2 Type II. Until then: minimal data surface, regional deployment, signed DPA on request.

Need a security questionnaire, DPA, or architecture deep-dive? Contact us — the founders read every inbound and reply within a business day.

Runnable proof

8 working notebooks. No install required.

TrustComplianceScaleDXAIIntegrations

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Two Products · One Vision

Build your company brain.

Join the data teams building institutional memory for their data platforms — powered by contracts, lineage, and AI agents.

LakeLogic Open Source

The declarative, executable contract engine. Apache 2.0 — free forever, runs on Polars, Spark, or DuckDB.

View on GitHub

LakeLogic Cloud

Observability, Zeus AI, and enterprise governance — fully managed. Zero infrastructure to run.

Talk to us about Cloud

Migration Path

On OSS already? Drop your docs/contracts/*.yaml straight into Cloud.

Talk to us