Lume is joining Harvey
We built Lume because customer integration was broken. Software teams were spending months wrestling with legacy ERPs, custom databases, and messy schemas just to onboard a single new customer.
What is Lume?
Lume was an AI tool built to remove the bottleneck between software teams and their customers' data. It was aimed at engineering and data teams that had to spend days, weeks or months wrestling with legacy ERPs, custom databases and messy schemas just to onboard a single new customer. What set it apart was that its AI handled schema discovery, suggested intelligent data mappings, validated data quality and generated dbt code automatically, turning a manual integration process into a faster one. On March 3, 2026, Lume announced that it is joining Harvey, and the Lume chapter is closed.
Key features
- AI schema discovery
- Lume's AI examined a customer's data environment and worked out the schema instead of leaving that work to engineers.
- Intelligent data mapping suggestions
- It proposed data mappings rather than requiring every field to be matched by hand.
- Data quality validation
- It checked the data as part of the integration process.
- Automatic dbt code generation
- Lume generated dbt code on its own, so teams did not have to write the transformation layer manually.
- Support for difficult sources
- It was built for the legacy ERPs, custom databases and messy schemas that made customer onboarding slow.
- Faster customer onboarding
- The goal was to replace a process that took days, weeks or months with a speedy one.
How to use Lume
- Point Lume at the new customer's data environment, whether that is a legacy ERP, a custom database or another system with a messy schema.
- Let the AI carry out schema discovery to work out how that data is structured.
- Review the data mappings Lume suggests for the customer's fields.
- Have Lume validate the data quality of the source.
- Use the dbt code Lume generates automatically to complete the integration.
Use cases
- Software teams onboarding a new customer whose data sits in a legacy ERP.
- Data engineering teams that need to connect to custom databases with poorly documented schemas.
- Companies whose integration work currently takes days, weeks or months per customer.
- Teams running a dbt-based data stack that want the transformation code generated for them.
- Product and platform teams that want to reduce the manual effort of customer integration.
Pricing
The official Lume website does not list any pricing or plans, and Lume has announced that it is joining Harvey, so the Lume chapter is closed. Check the official site or Harvey for current information.
FAQ
Is Lume still available?
No. Lume announced on March 3, 2026 that it is joining Harvey, and the company states that the Lume chapter is closed. The announcement links to Harvey for more information.
What did Lume do?
Lume was an AI tool for customer integration. It handled schema discovery, suggested data mappings, validated data quality and generated dbt code automatically, so software teams did not have to work through legacy ERPs, custom databases and messy schemas by hand.
Which data sources could Lume connect to?
Lume was built for the sources that made customer integration difficult: legacy ERPs, custom databases and messy schemas. The website does not list specific named systems or connectors.
Did Lume generate dbt code?
Yes. Automatic dbt code generation was one of the things Lume's AI did as part of the integration process, alongside schema discovery, mapping suggestions and data quality validation.
Who backed Lume?
Lume's investors included Y Combinator, General Catalyst, Khosla Ventures, Floodgate and Soma Capital, along with angel investors.
Why did Lume join Harvey?
Lume's team said it is joining Harvey to keep working on a harder problem: automating the complex, high-stakes work that still takes too long in professional workflows. Harvey's own site has the full announcement.
Lume is joining Harvey
We built Lume because customer integration was broken. Software teams were spending months wrestling with legacy ERPs, custom databases, and messy schemas just to onboard a single new customer.
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