TL;DR
Lume helps teams map data between systems with AI. If your team spends time mapping data for customer data onboarding, data normalization between systems, or mapping for data integrations, we can help! Please go to our website and sign up for a free trial.
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Hi everyone! We are Nicolas, Nebyou, and Robert, the founders of Lume. We are on a mission to automate the painstakingly manual process of data mapping, after experiencing this frustration as engineers ourselves. We have onboarded several customers who are growing and are excited to help more teams use AI in their data mapping workflows.
🧨 Problem
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.
✨ Solution
Lume uses AI to automatically map data between any start and end schema, whether it is customer data, external sources, or anything else. Lume provides this via an API and a no-code platform where you can generate mapping logic, review and edit it, and manage multiple data pipelines. Whether you want to onboard customer data, normalize data from multiple sources, or create auto-mapping UIs over Lume, Lume delivers. With automated transformations and data delivery, error and type checking, auto-maintenance with schema inference, and execution via an API or a no-code App for different use cases, teams can spend more time delivering their core value to customers instead of wrangling and manually mapping data.
🚀 Key Features
⚙️ How it works
✨ Lume’s AI system creates the mappings between any two schemas, ranging from simple 1-1 mappings, time-series aggregations, complex calculations, and ontology classifications.
Use the generated mappings and mapping logic: Use the Lume API or the Lume Platform to upload new incoming data and retrieve the corresponding output mapped data and mapping logic. Your data has just been automatically mapped with AI!
For creating robust data pipelines, review and edit the generated mapping logic before running production data. Once saved, you can confidently use these mappers as deterministic pipelines. This is helpful for your data integrations between systems.
💡 Use Cases
Lume handles three core use cases:
Here are three customer success stories:
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. Clients previously were allocating engineers, customer success teams, or offshore labor to analyze incoming data, map the data, and route it to their new systems. This process used to take up to multiple months for some teams, costing significant time and money.
We serve multiple industries ranging from ecommerce, insurance, manufacturing, and financial products.
👨💻Meet the Team
My co-founders Nebyou Zewde (right), Robert Ross (middle), and I (left) all met during our first year at Stanford. 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, 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.
🙏 Our ask
💥 The Deal
Mention you saw Lume via Launch YC, and we will give you a 50% discount for your first 6 months. We also offer a free pilot.