Lume (YC W23)

My co-founders and I strolling through Times Square to see the Lume (YC W23) billboard!

?? Our customer just cut 5,000 lines of data mapping code with Lume (YC W23). Now, instead of maintaining all that custom transformation code, they’re using Lume to manage dozens of mappers with AI-powered automation—saving time and reducing manual work. So, what were those 5,000 lines for? Mapping API response payloads to internal schemas—a complex, repetitive process. With Lume, they’ve streamlined this and turned it into a more scalable, manageable workflow. The best part? Teams are getting creative with how they use our transformation tech. We’ve seen a lot of interest in API mapping, and we're already working on new features to make this process even smoother. It's great to see the different ways customers are leveraging Lume—we're excited to ship these new features.

Lume (YC W23) automates data mappings using AI. Lume AI helps teams ingest client data, normalize data from different sources, and build data pipelines, automatically. Today, the mapping process involves a painstakingly manual process of analyzing data, developing mapping logic, and constantly maintaining these mappings. With Lume, you can leverage AI to automatically map data between any schemas, whether for customer data, external sources, or other types. Available via an API or a no-code platform, Lume enables you to generate, review, and edit mappings and manage multiple data pipelines. Lume serves industries ranging from financial products, e-commerce, insurance, and manufacturing. Cofounders Nebyou Zewde, Robert Ross, and Nicolas Machado met in their freshman dorm at Stanford and became best friends. In their time as engineers, they faced this problem head-on and now are on a mission to fully automate data mapping. Learn more: https://lnkd.in/gMcSZ9wC

V2 of the Lume Editing Workflow is live ?? Users now have a more robust interface to review and edit their generated mappers, seamlessly. ?? See your source data and corresponding mappers + mapped data in one page ?? Quickly cycle between records to visualize the corresponding mappings ?? Visualize the lineage for each mapper, along with the output mapped data. ??? Make edits using natural language or manually, run Lume's AI, see the output, and save once finished. Interested in mapping data automatically with AI? Let's talk: calendar link is in the comments.

An important lever to communicate your product with customers is via documentation. I found a service that makes documenting so, so smooth. Thanks Han Wang and Mintlify for making creating beautiful docs so fun and easy. ??

???Lume (YC W23) is now on Product Hunt! We’d appreciate your support and love - check it out. Also, there’s a demo video on there ?? We would love to hear any questions, thoughts, or feedback you may have. Shoutout for Mitch Patin, Rangel Milushev, and Alexander Danilowicz for paving the way with amazing Product Hunt launches and inspiring ours! #ProductHunt #Startups #AI https://lnkd.in/d2gi77Vm

Are you intrigued by the potent mixture of AI, Natural Language Processing, and Data Integration? Well, we've something for you to explore! A fascinating world where artificial intelligence steps up to revolutionize data integration with the power of natural language processing software. Curious? Then dive into our latest article Can AI Transform Data Integration with Natural Language Processing Software?". https://lnkd.in/eUemZHNV A special mention to the progressive startup Lume, founded by the dynamic trio Nebyou Zewde, Nicolas Machado and Robert Ross, who are working diligently to assist engineering teams in creating sustainable data integrations. Their refined approach tackles diverse data integration scenarios and helps in transforming scattered data sources into a unified structure. Stay tuned for more updates on startups like Lume that continue to push the boundaries of technological innovation! #AI #DataIntegration #NaturalLanguageProcessing #Startups #Innovation #ArtificialIntelligence Follow them here ?? https://lnkd.in/eAiqVczF & https://lnkd.in/e7_tegVa.