Use AI to automatically map data between any two schemas in seconds. Lume AI helps teams ingest client data, normalize data from different sources, and build data pipelines, automatically.
Hi Product Hunt 👋 @nebyou_zewde1 , @robert_te_ross , and I are the founders of Lume.
🧨 Problem: Data Mapping is manual and slow
We are on a mission to automate the painstakingly manual process of data mapping, after experiencing this frustration as engineers ourselves.
The usual mapping process involves a labor-intensive cycle: analyzing data to determine what's relevant, selecting the appropriate properties, developing the mapping logic, and constantly updating mappers to accommodate schema changes in source or target systems. This process, we learned, takes days, weeks, or even months for most teams, and automating it has traditionally been borderline impossible due to unique differences in data.
🚀 Key Features
- ✨ Generate mapping logic in seconds: via our API, just pass in a sample of your source data and a target schema, and Lume will generate a mapper.
- 🛠️ Edit mapping logic: review and update the mapper as needed to ensure you have the desired outcome. Leverage natural language to edit the mappers quickly and easily.
- ✅ Deploy mappers: once you’ve reviewed the mapping logic, save the mappers to use them deterministically in your code via our API, allowing you to reliably move data between schemas as you scale your data pipelines.
- 🤖 Auto-maintain mappers: Lume detects changes in your schemas, notifying you and allowing you to use AI to update the mapping logic, effectively automating your maintenance.
- 📊 Manage and organize mappers: use Lume’s dashboard to have visibility over all of your mappers and live data pipelines.
- 🔮 Upcoming: viewing the generated mapping logic, and adding custom logic.
💡 Use Cases
Lume handles three core use cases:
- Normalizing Data from Various Providers
- Client Data Ingestion
- Rapid Setup and Maintenance of Data Pipelines
All of these have the common theme of having to map data between unique schemas, where even discrepancies as minor as column name variations make this process time-consuming and near-impossible to automate. This gets even worse at scale.
🎯 Who’s this for?
We are actively talking to small to midsize startups that are bottlenecked by data mapping on any of the above 3 fronts, but are also excited to serve larger companies and use cases beyond.
👀 Try Lume
Request a demo via https://www.lume.ai. We offer a free pilot. I’ll reach out to you promptly and get you onboarded quickly.
📖 Our story
As engineers ourselves, we’ve spent plenty of time grudgingly going over the manual task of mapping data. We quickly learned that we were not the only ones who faced this problem - most companies spend too much time on this. As AI grads from Stanford, and with a fire for this problem, we built Lume AI. We are part of the Y Combinator W23 batch, and we’re excited to be launching here.
💰 Special Pricing
Email me mentioning you saw Lume via Product Hunt, and we will give you a 50% discount for your first 6 months! We also offer a free pilot.
