As the founder of another product in this space - this is super impressive and well built. Great demo video and congrats on top of HN! Getting this smooth UX and data behind the scenes is not easy.
Wow -- tried it out and looks quite impressive. The granularity of data for these companies is amazing!
My last startup was selling to SMBs. It looks like Sumble is most likely targeted at mid-market and enterprise companies. Any plans to expand coverage into the long tail of smaller companies?
Thanks! Our current coverage is focused on companies with a significant online presence (e.g. they've made job posts, people say they at the company, and/or they have a functional website).
Our goal is to have complete coverage for active companies and organizations in the world, and an understanding for companies that previously existed but are no longer active as well (these appear extensively in CRM's and add noise).
We prioritize expanding data coverage in areas that we hear are most useful from our current users and customers.
Sumble is one of my go-to data tools for GTM – great data quality and lots of interesting data points that are kind of a pain to find elsewhere.
I do find myself wanting to transform the data (especially the stuff in job descriptions) using an LLM, e.g. for scoring companies/contacts or looking for more subtle signals. Sometimes I do this manually but exporting a bunch of JDs from Sumble isn't possible AFAIK. Or doing it in Sumble would be great, too.
This is incredibly useful and I can see myself using it and paying a subscription. That being said:
1. I couldn't find some key persons that I know works in an organization. How accurate is the data?
2. I don't know if this is happening because you are getting lots of traffic now, but each query takes 20-30 seconds which is unusable.
> - Is the web app intuitive?
Yes
> - What queries do you want us to prioritize supporting in an API?
Maybe specific but I want to filter by head count in job function (ie: find organizations that have 50-200 software engineers regardless of their total head count).
> - What additional external data sources would you like us to prioritize? - What workflow improvements/integrations would you find most helpful?
I don't really care as long as the data is as accurate as possible. The process of lead generation/research is a slow one that I don't think workflows matter.
| Maybe specific but I want to filter by head count in job function (ie: find organizations that have 50-200 software engineers regardless of their total head count).
You're not alone! We've heard this from others as well, planning to add it soon
People data has ~85% coverage at the moment for people who put their resume data online. We are going to be adding some others sources (e.g. Github profiles) that will help improve coverage, particularly for technical personas.
Is there no way to add custom searches? As a test, I wanted to look for flight test engineers in aerospace companies, but the only way I could see to approximate it was to look at job postings. I was able to drill down by picking a company (Boeing) until I found one, but that's really tedious compared to just adding a custom job function ("flight test engineer") or selecting "Test Engineer" and adding a custom industry ("Aerospace").
The job functions we currently classify have been mostly focused by our early users/customers (companies building products/tools/infrastructure for data and software engineering teams), and handling the multilingual aspects of those across countries well.
We're aiming to extend this in two ways:
1. Adding job title and job description full-text search, to handle the long tail of usecases (in-flight project)
2. Extend the job function classification to the full universe of jobs that people can have
Job title and job description full text search would really be perfect. At least in my mind, good GTMs are narrow (software engineers in flight test) vs. broad, like selling to all software engineers using python in the manufacturing industry.
Sumble has been my critical tool to research the organization structure and responsibility in a large company, technology adoption like which organization has the LLM adoption.
Congratulations! Looks awesome.
1. I found it very intuitive.
2. If I could have smart filtering using llm classification, that would be very powerful. Any plans on doing that?
As in a search box where you can ask free form queries rather than applying filters? We haven't heard much demand for that yet, so haven't prioritized it. We will if it's a common request.
How does this tool differ from Apollo.io, Clay.com, and Promptloop?
Tools like Clay and Apollo are often misused for spammy cold outreach—which rarely works. The real value lies in enriching leads who’ve already shown interest, helping align marketing efforts with the right prospects. Beyond that, more data doesn’t always improve GTM decisions.
I'd love to hear(and learn) how others would want to use this tool specifically for GTM.
Here's three main ways our users and customers use us:
1. Revenue Operations teams
Integrate Sumble's data programmatically to help with account scoring, territory planning, account qualification/disqualification, and CRM data cleanup. We provide feature matrices that feed ML models for large sales teams.
2. Individual AE's/SDR's
Many sales people have a small universe of named accounts that they go deep on. They use Sumble to understand the buying groups that exist within their target accounts, and which relevant technologies these groups use, and any relevant projects going on (e.g. data infrastructure migrations, cloud migrations, and GenAI projects can be critical signals for many of our customers)
For ongoing awareness of key changes within accounts, we work with our enterprise customers to define all the signals that are relevant to their sales plays, and send email/slack notifications when any of these signals happens in their accounts as well.
For sales reps with a larger universe of accounts (e.g. the SMB/commercial tier), they use us to filter out a lot of the noise in their territory and understand which accounts are real active businesses that are potential users of their product that they should spend time on.
3. Marketing
Marketers use us to figure out which accounts to focus on, and to spin up very targeted LinkedIn/Facebook/etc. campaigns to reach their most likely potential users and buyers
Looks like an amazing product. Been playing around with it for a few mins. The UI is quite buggy and jumps around a lot (Chrome, MacOS), and seems to auto-refresh on the organizations page, which makes curating lists impossible. What's a good way to keep providing feedback?
Super useful and intuitive product, love the granularity of the tech stack keywords, it does find relevant leads/companies that you couldn't find otherwise..
API could be helpful for enrichment of internal sources.
MCP would also definitely make sense as well
Great work, and interesting to see Knowledge Graphs in a production setting. Why did you choose a Knowledge Graph as the backend? How is the graph modeled. Do you use existing Graph Query languages, or did you have to create your own?
Thanks! We describe this as a knowledge graph because that's how we think about the structure in the data & is where we want to go.
Right now, we've focused on normalizing several key entities (e.g. organizations including parent/subsidiary relations, technologies, people, and job functions), and capturing the relations between these as well as additional useful metadata like location and industry.
From a backend implementation standpoint, this is currently implemented as structured relational tables for query performance and simplicity (e.g. count up all teams mentioning pytorch in job posts including rolling up across parent subsidiaries and sort by the biggest organizations descending).
Future direction here is TBD as we expand the sources that we cover and types of queries that can be computed across these sources.
There's been a lot of attempts at building high-quality public knowledge graphs that haven't hit escape velocity.
We're focusing on a structured, commercially relevant subset of the problem as a starting point to generate a critical mass of usage and funding that will enable us to build the bigger vision: a highly structured, up-to-date, and trusted repository of all the facts about the world that is easy to browse, query, and integrate programatically into all the relevant workflows (including for grounding LLM's)
Appreciate you sharing the vision. Having worked in this space for a while, IMO the biggest challenges for a public facing graph are in 1. Entity Linking from NL Query -> Graph queries or in your case relational queries (Multiple similarly named teams in Microsoft). And 2. Relevance of results for more complex queries. I like your approach of having a drop down of filter tags, which eliminates 1, but will be harder to scale like in a Graph of everything.
I'm sure tools like this are useful to salespeople and recruiters, but it also seems like a dream resource for spammers, scammers and esp. phishing attacks.
I'd set up a ticketing system so you can receive bug reports and feature requests. It's more structured than chat rooms, which are information black holes.
I just tried this. HOLY CRAP its good. How did you achieve this? I'm very impressed.
Also: Please don't evolve the UI. Its perfect as it is
As the founder of another product in this space - this is super impressive and well built. Great demo video and congrats on top of HN! Getting this smooth UX and data behind the scenes is not easy.
Wow -- tried it out and looks quite impressive. The granularity of data for these companies is amazing!
My last startup was selling to SMBs. It looks like Sumble is most likely targeted at mid-market and enterprise companies. Any plans to expand coverage into the long tail of smaller companies?
Thanks! Our current coverage is focused on companies with a significant online presence (e.g. they've made job posts, people say they at the company, and/or they have a functional website).
Our goal is to have complete coverage for active companies and organizations in the world, and an understanding for companies that previously existed but are no longer active as well (these appear extensively in CRM's and add noise).
We prioritize expanding data coverage in areas that we hear are most useful from our current users and customers.
Awesome, go crush it!
Sumble is one of my go-to data tools for GTM – great data quality and lots of interesting data points that are kind of a pain to find elsewhere.
I do find myself wanting to transform the data (especially the stuff in job descriptions) using an LLM, e.g. for scoring companies/contacts or looking for more subtle signals. Sometimes I do this manually but exporting a bunch of JDs from Sumble isn't possible AFAIK. Or doing it in Sumble would be great, too.
Awesome to see it on HN. Congrats on the launch!
Thanks! Job descriptions are included in job post CSV exports, which is the current only path for that workflow.
We're planning to make that workflow much better in four ways this year:
1. Adding an API to make it easier to consume the data programmatically (next 2 months)
2. Enabling running LLM's on tabular results on Sumble directly that would enable pulling in job description context into the LLM call
3. Experimenting with an MCP endpoint, to see if that's helpful for these workflows as well
4. Experimenting with adding Sumble scoring models
This is incredibly useful and I can see myself using it and paying a subscription. That being said:
1. I couldn't find some key persons that I know works in an organization. How accurate is the data?
2. I don't know if this is happening because you are getting lots of traffic now, but each query takes 20-30 seconds which is unusable.
> - Is the web app intuitive?
Yes
> - What queries do you want us to prioritize supporting in an API?
Maybe specific but I want to filter by head count in job function (ie: find organizations that have 50-200 software engineers regardless of their total head count).
> - What additional external data sources would you like us to prioritize? - What workflow improvements/integrations would you find most helpful?
I don't really care as long as the data is as accurate as possible. The process of lead generation/research is a slow one that I don't think workflows matter.
| 2. I don't know if this is happening because you are getting lots of traffic now, but each query takes 20-30 seconds which is unusable.
Thanks! What queries are you finding painful? Most should be under a second, there's some that are expensive though
Simple queries. As in typing the name of a person in a company list of 200. Keeps spinning forever.
Thanks for taking it for a spin! I'm working on why this is slow now.
Thanks! We'll take a look at that one
| Maybe specific but I want to filter by head count in job function (ie: find organizations that have 50-200 software engineers regardless of their total head count).
You're not alone! We've heard this from others as well, planning to add it soon
People data has ~85% coverage at the moment for people who put their resume data online. We are going to be adding some others sources (e.g. Github profiles) that will help improve coverage, particularly for technical personas.
Using this product.... big fan. Most important in our GTM stack for building account lists.
Is there no way to add custom searches? As a test, I wanted to look for flight test engineers in aerospace companies, but the only way I could see to approximate it was to look at job postings. I was able to drill down by picking a company (Boeing) until I found one, but that's really tedious compared to just adding a custom job function ("flight test engineer") or selecting "Test Engineer" and adding a custom industry ("Aerospace").
Thanks for the feedback!
The job functions we currently classify have been mostly focused by our early users/customers (companies building products/tools/infrastructure for data and software engineering teams), and handling the multilingual aspects of those across countries well.
We're aiming to extend this in two ways:
1. Adding job title and job description full-text search, to handle the long tail of usecases (in-flight project)
2. Extend the job function classification to the full universe of jobs that people can have
Job title and job description full text search would really be perfect. At least in my mind, good GTMs are narrow (software engineers in flight test) vs. broad, like selling to all software engineers using python in the manufacturing industry.
Sumble has been my critical tool to research the organization structure and responsibility in a large company, technology adoption like which organization has the LLM adoption.
Congrats on the launch!
There is so much signal in job posts - excited to see this launch.
Congratulations! Looks awesome. 1. I found it very intuitive. 2. If I could have smart filtering using llm classification, that would be very powerful. Any plans on doing that?
As in a search box where you can ask free form queries rather than applying filters? We haven't heard much demand for that yet, so haven't prioritized it. We will if it's a common request.
How does this tool differ from Apollo.io, Clay.com, and Promptloop?
Tools like Clay and Apollo are often misused for spammy cold outreach—which rarely works. The real value lies in enriching leads who’ve already shown interest, helping align marketing efforts with the right prospects. Beyond that, more data doesn’t always improve GTM decisions.
I'd love to hear(and learn) how others would want to use this tool specifically for GTM.
Here's three main ways our users and customers use us:
1. Revenue Operations teams
Integrate Sumble's data programmatically to help with account scoring, territory planning, account qualification/disqualification, and CRM data cleanup. We provide feature matrices that feed ML models for large sales teams.
2. Individual AE's/SDR's
Many sales people have a small universe of named accounts that they go deep on. They use Sumble to understand the buying groups that exist within their target accounts, and which relevant technologies these groups use, and any relevant projects going on (e.g. data infrastructure migrations, cloud migrations, and GenAI projects can be critical signals for many of our customers)
For ongoing awareness of key changes within accounts, we work with our enterprise customers to define all the signals that are relevant to their sales plays, and send email/slack notifications when any of these signals happens in their accounts as well.
For sales reps with a larger universe of accounts (e.g. the SMB/commercial tier), they use us to filter out a lot of the noise in their territory and understand which accounts are real active businesses that are potential users of their product that they should spend time on.
3. Marketing
Marketers use us to figure out which accounts to focus on, and to spin up very targeted LinkedIn/Facebook/etc. campaigns to reach their most likely potential users and buyers
Thanks, this is helpful.
Looks like an amazing product. Been playing around with it for a few mins. The UI is quite buggy and jumps around a lot (Chrome, MacOS), and seems to auto-refresh on the organizations page, which makes curating lists impossible. What's a good way to keep providing feedback?
The page is also refreshing constantly for me. Chrome & Safari :(.
Can email me at a@sumble.com. Great if you can record a loom.
Super useful and intuitive product, love the granularity of the tech stack keywords, it does find relevant leads/companies that you couldn't find otherwise..
API could be helpful for enrichment of internal sources. MCP would also definitely make sense as well
Thanks! We're planning to add an API in the next two months, and exploring MCP alongside that
Great work, and interesting to see Knowledge Graphs in a production setting. Why did you choose a Knowledge Graph as the backend? How is the graph modeled. Do you use existing Graph Query languages, or did you have to create your own?
Thanks! We describe this as a knowledge graph because that's how we think about the structure in the data & is where we want to go.
Right now, we've focused on normalizing several key entities (e.g. organizations including parent/subsidiary relations, technologies, people, and job functions), and capturing the relations between these as well as additional useful metadata like location and industry.
From a backend implementation standpoint, this is currently implemented as structured relational tables for query performance and simplicity (e.g. count up all teams mentioning pytorch in job posts including rolling up across parent subsidiaries and sort by the biggest organizations descending).
Future direction here is TBD as we expand the sources that we cover and types of queries that can be computed across these sources.
There's been a lot of attempts at building high-quality public knowledge graphs that haven't hit escape velocity.
We're focusing on a structured, commercially relevant subset of the problem as a starting point to generate a critical mass of usage and funding that will enable us to build the bigger vision: a highly structured, up-to-date, and trusted repository of all the facts about the world that is easy to browse, query, and integrate programatically into all the relevant workflows (including for grounding LLM's)
Appreciate you sharing the vision. Having worked in this space for a while, IMO the biggest challenges for a public facing graph are in 1. Entity Linking from NL Query -> Graph queries or in your case relational queries (Multiple similarly named teams in Microsoft). And 2. Relevance of results for more complex queries. I like your approach of having a drop down of filter tags, which eliminates 1, but will be harder to scale like in a Graph of everything.
How far off is an API? Looks slick but I’d want to be able to query programmatically
Currently aiming for next 2 months.
I'm sure tools like this are useful to salespeople and recruiters, but it also seems like a dream resource for spammers, scammers and esp. phishing attacks.
i think this is true of most powerful sales tools
Love this product and the overall strategy.
Congrats, Anthony and Ben! Super excited for you both! (And still miss you at Kaggle!) :-)
Nicely done. Do you have a roadmap, public ticketing system or communication channel?
Thanks! Haven't prioritized something public facing on this front yet - what would you find most helpful?
I'd set up a ticketing system so you can receive bug reports and feature requests. It's more structured than chat rooms, which are information black holes.
> For example, here's a list of GenAI projects at Capital One that involve RAG/Vector databases: https://sumble.com/l/6sDqKmhyAH
requires you to sign in, which then follows by marketing emails.
We plan to put some data outside the login wall to see what we have without logging in. Haven't put time into this flow yet.
Super interesting!
Please ingest the biotech industry!
What query are you trying to run? Can email me at a@sumble.com and I can see if we can support your query.