Lume

Automate data mappings using AI

5.0•2 reviews•

350 followers

Automate data mappings using AI

5.0•2 reviews•

350 followers

•

•

Lume automates data mappings using AI. With Lume, you can map any source data to any target schema. This allows teams to ingest client data, normalize data from unique systems, and build and maintain data pipelines, all in seconds.

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Launch tags:Developer Tools•Artificial Intelligence•Data & Analytics

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Lume

Maker

📌

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.

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3yr ago

Lume

Maker

@nebyou_zewde1 @robert_te_ross @shivam_tiwari24 Thank you Shivam! If we can help your team with data mappings, please reach out.

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3yr ago

@nebyou_zewde1 @robert_te_ross @nicolas_machado Best

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3yr ago

Hatable

@nebyou_zewde1 @robert_te_ross @nicolas_machado Super useful in integrating 2 different software that don't talk through APIs. I have come across ERPs and a few legacy software. Integration between these has been a pain point. Well done on the launch!

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3yr ago

NVIDIA

@nicolas_machado congratulations

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3yr ago

Corbado

That's a really cool tool, I love it when developers make a solution for other Developers - these are the best Dev Tools imo. If I may ask, what do you use for the AI-generation part? One of the major LLMs? One you train your own? Excited to hear from you!

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3yr ago

Lume

Maker

@lrueger Thank you so much!! Our default model provider is OpenAI; however, we have architected our system in a modular fashion to allow for the use of other major LLM providers as well as open-source models.

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3yr ago

Corbado

@robert_te_ross Nice! I feel like this is the right path at the moment: Starting with OpenAI because it's just the best atm, but also switching to others if it's the right time. When trying to include GPT in my apps / services I often have problems with quality and every 10th/50th or 100th output is just not good or not in the right format. How do you handle these edge cases, when OpenAI is not returning something good?

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3yr ago

Lume

Maker

@lrueger Great question! We spent a lot of time designing the architecture to address these exact concerns. Our usage of these models is actually very precise and deliberate. We have reduced model hallucinations by never generating the data directly but instead generating a proprietary high-level mapping logic we designed to be executed deterministically. This also allows us to enforce formats and catch hallucinations.

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3yr ago

Corbado

@robert_te_ross Very nice! I can see why Lume is at YC and that you have a Stanford AI background. Do you have a LinkedIn or something to connect? I'd love to stay in contact and follow your journey :)

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3yr ago

Lume

Maker

@robert_te_ross @lrueger Email any time! LinkedIn is https://www.linkedin.com/in/mach...

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3yr ago

🔌 Plugged in

Working with the Lume team has been an absolute pleasure. We have the unique challenge of mapping thousands of different lender data schemas to hundreds of DMV documents. Lume fixes this

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3yr ago

Lume

Maker

@jack_chapman1 It's been a pleasure having you as a customer, Jack! Thank you to you and the Cardinal Gray team.

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3yr ago

Swell

Nice product @nicolas_machado, I could see Lume being super helpful for ERP data migrations (which are typically bespoke in the ecomm world and a huge PITA). For the API responses, what happens when the data is not an exact fit?

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3yr ago

Lume

Maker

Thank you, @mark_regal! Great question. When data does not fit the specified schema, the corresponding fields will be mapped to null, and the pipeline will be marked as "Needs Review" if you are expecting a non-null value. If needed, you can then go to your developer dashboard to review and edit the fields to your desired output. You can check out what this workflow looks like at 1:35 in the demo video: https://youtu.be/dbAuHVO4XsY

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3yr ago

Having involved in many ETL processes I can attest that data mapping can really be painful and this comes *after* the pain of deciding what data to keep/release, especially when dealing with legacy systems. Definitely bookmarking this! Congratulations on the launch!

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3yr ago

Lume

Maker

@clement_mia you’re exactly right. Using Lume can streamline the decision making process in deciding what data to use + do the actual mapping logic. Let me know if you want to try it out !

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3yr ago

Magic Patterns

as someone building an ai product, this makes SO MUCH SENSE. looking forward to seeing lume evolve into supporting unstructured data.

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3yr ago

Lume

Maker

@alexdanilowicz Thank you Alex! Excited to see Magic Patterns grow and evolve!

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3yr ago

AI Copy Feature on Atomic Fusion

I work with Bubble a lot and one of the challenges I've seen people having is managing schemas especially when they've started from a reference, tutorial or a template. Lume seems to completely eliminate that problem. I'll be spending the weekend experimenting with it! Love what you're doing @nebyou_zewde1 , @robert_te_ross, @nicolas_machado :)

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3yr ago

Lume

Maker

@ranjit_bhinge please reach out through our website!

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3yr ago

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