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unLAP your OLAP.

Verification-first AI optimization.

Lower data warehouse cost.

Automate query optimization with just a single line of code. unLAP autonomously finds expensive queries, generates optimizations, verifies correctness and performance, and deploys them when approved. All your team has to do is click yes or no.

Feature Shipped unLAP Tuned

You have features to ship.
Not databases to tune.

Writing perfectly optimized analytical queries takes time your team doesn't have. Analysts and engineers just need to get features rolled out.

When you prioritize speed, expensive queries inevitably slip into production. Instead of slowing down development sprints to manually tune OLAP databases, unLAP acts as your automated safety net.

Keep your velocity high. Ship your code. Let our pipeline silently optimize the expensive queries in the background before they spike your cloud bill.

Observation. Generation. Verification. Deployment.

unLAP isn't just another coding agent typing out SQL. It is a fully autonomous optimization pipeline built specifically for production databases.

Instead of blindly guessing at optimizations or performing simple aggregations, our visibility tools observe your actual query patterns and data layout, then tune the database around them.

When generating a fix, unLAP relies on a sandbox of managed data structures, backing up every candidate with formal proofs and built-in regression tests.

By the time a change reaches your desk, it’s already verified. Deployment is just a simple one-click or automated handoff. Your application code doesn't change a single line—it just starts running faster.

Observe

unLAP Monitors BigQuery

Detects Templated Queries & Costs

Provides Unparalleled Visibility

Generate

Tunes Database for Efficiency

Adapts to Data Layout

Manages Sandbox of Data Structures

Verify

Automatic Validation

Ensures Correctness with Formal Proofs

Regression Tests vs Old Query

Deploy

Hot-Swap Optimized Query

Configurable One-Click or Automatic Deployment

Zero Code Changes

* Annualized: ~4000x lower annualized cost observed in an actual million-dollar-query-class production environment.

** Projected: Benchmarked internal performance multipliers applied to standard enterprise query volumes.

Change one import. Keep the rest.

The SDK is drop-in compatible with the code you already have.

Patch diff

- from google.cloud import bigquery
+ from unlap import bigquery
client = bigquery.Client(...)
job = client.query(...)

Built for your specific bottleneck.

Whether you are fighting cross-team friction, runaway cloud bills, or angry customers, select your biggest headache to see how unLAP resolves it.

Stop manual tuning.
Get back to shipping.

Join the engineering teams using unLAP's autonomous pipeline to flatten runaway cloud costs and accelerate long-running queries.

  • Drop-in integration (one import)
  • Mathematically verified candidate generation
  • Zero application code changes required
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