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Most “AI CRMs” are just old databases with a ChatGPT button. You still type everything in, or the data goes stale in a week. Newly launched Lightfield (Lightfield) flips that: it treats emails, calls, and meetings as the source of truth. The structure assembles itself around the raw conversations....

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Rohan Paul

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22,939 views • 9 months ago •via X (Twitter)

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1/ The end of manual CRMs Most CRMs fail because they force manual typing, so data rots the moment a meeting ends -yet CRM’s core job is the vital, grueling task of logging every customer touchpoint, and in the AI era this human bottleneck turns dynamic relationships into a slow, error-filled slog that no team can sustain. Lightfield fixes that by auto-capturing all interactions—calls, emails, Slack, notes - so it’s the first CRM that truly knows your relationships as well as you do.
0:37

1/ The end of manual CRMs Most CRMs fail because they force manual typing, so data rots the moment a meeting ends -yet CRM’s core job is the vital, grueling task of logging every customer touchpoint, and in the AI era this human bottleneck turns dynamic relationships into a slow, error-filled slog that no team can sustain. Lightfield fixes that by auto-capturing all interactions—calls, emails, Slack, notes - so it’s the first CRM that truly knows your relationships as well as you do.

Chubby♨️

43,765 views • 10 months ago

Today we're announcing Lightfield's $47M Series A led by a16z to reimagine CRM as a world model of a business. For agents to do customer-facing work, they need to understand how your business actually works. Salesforce wasn't designed for this. It was built 20+ years ago for humans to update records. Layer agentic processes on top and you get low quality output, because the underlying data is incomplete and lacks the structure agents need. Lightfield updates itself from every interaction, forming a trustworthy model of your business for people and agents. Since launching last November, 5,000+ companies have signed up - and we're ripping out legacy CRM at mature organizations with hundreds of users. Companies on Lightfield grow faster than their competitors. It finds prospects that look like your best customers and books meetings with them. It captures commitments from every conversation and automates follow-ups. It diagnoses weaknesses in your funnel and learns from your best sellers to codify what works. The companies of the future will run on a business world model, not a Salesforce-era database. Our mission is to put that capability in the hands of every employee, and every agent, at every company.
2:07

Today we're announcing Lightfield's $47M Series A led by a16z to reimagine CRM as a world model of a business. For agents to do customer-facing work, they need to understand how your business actually works. Salesforce wasn't designed for this. It was built 20+ years ago for humans to update records. Layer agentic processes on top and you get low quality output, because the underlying data is incomplete and lacks the structure agents need. Lightfield updates itself from every interaction, forming a trustworthy model of your business for people and agents. Since launching last November, 5,000+ companies have signed up - and we're ripping out legacy CRM at mature organizations with hundreds of users. Companies on Lightfield grow faster than their competitors. It finds prospects that look like your best customers and books meetings with them. It captures commitments from every conversation and automates follow-ups. It diagnoses weaknesses in your funnel and learns from your best sellers to codify what works. The companies of the future will run on a business world model, not a Salesforce-era database. Our mission is to put that capability in the hands of every employee, and every agent, at every company.

Keith Peiris

1,309,274 views • 3 days ago

ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.
0:43

ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

Aaron Levie

57,745 views • 1 year ago

1/ My Co-founder Peter had 6 active negotiations, weeks of calls and emails and genuinely had no idea who actually mattered in any of them. Then, Lightfield just dropped its custom 'Skills & Knowledge' to their CRM. A game changer! We built one right away to map out all these client relationships. What we realized was we had a mess. But now, instead of clicking through a million tabs in a live meeting, Lightfield did the work. The key thing: every email, call, and interaction we had with each client was automatically captured. These skills always run in the freshest context for every client relationship. It doesn't rely on any one team member, and the data is always fresh, never stale. A 🧵:
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1/ My Co-founder Peter had 6 active negotiations, weeks of calls and emails and genuinely had no idea who actually mattered in any of them. Then, Lightfield just dropped its custom 'Skills & Knowledge' to their CRM. A game changer! We built one right away to map out all these client relationships. What we realized was we had a mess. But now, instead of clicking through a million tabs in a live meeting, Lightfield did the work. The key thing: every email, call, and interaction we had with each client was automatically captured. These skills always run in the freshest context for every client relationship. It doesn't rely on any one team member, and the data is always fresh, never stale. A 🧵:

Chubby♨️

18,781 views • 5 months ago

startups buy salesforce or hubspot by default because that's just what you do when you scale. the problem is those systems were built for reps manually typing notes. slapping ai agents on top of subjective, incomplete data is a bottleneck. lightfield actually rebuilt the crm for the agent era. full api and mcp access turns it into infrastructure. 5,000+ companies already moved. i've been working with the lightfield team, and their $47m series a is pure signal. opinions are mine.
0:53

startups buy salesforce or hubspot by default because that's just what you do when you scale. the problem is those systems were built for reps manually typing notes. slapping ai agents on top of subjective, incomplete data is a bottleneck. lightfield actually rebuilt the crm for the agent era. full api and mcp access turns it into infrastructure. 5,000+ companies already moved. i've been working with the lightfield team, and their $47m series a is pure signal. opinions are mine.

kai.

13,404 views • 3 days ago

Everyone’s talking about the future of CRM. Let me tell you what it’s not: ❌ Fields & columns ❌ Manual data entry ❌ Clunky dashboards HubSpot’s CEO Yamini Rangan just admitted it on a recent podcast with The Logan Bartlett Show — traditional CRMs aren’t enough anymore. Most sales data is buried in unstructured data. The majority of that is in conversational data. And only one company has nailed capturing it. Fireflies is what an AI CRM of the future will look like. 🧵👇
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Everyone’s talking about the future of CRM. Let me tell you what it’s not: ❌ Fields & columns ❌ Manual data entry ❌ Clunky dashboards HubSpot’s CEO Yamini Rangan just admitted it on a recent podcast with The Logan Bartlett Show — traditional CRMs aren’t enough anymore. Most sales data is buried in unstructured data. The majority of that is in conversational data. And only one company has nailed capturing it. Fireflies is what an AI CRM of the future will look like. 🧵👇

Shruti

44,047 views • 1 year ago

GROKIPEDIA OPENS THE TRUTH PORTAL Want to search Grokipedia? Just type your question like you would in Grok, it pulls answers straight from the live encyclopedia. No spin, just data. Grokipedia is built to be truth-seeking and self-correcting, updating in real time without the human bias that slows everything else down Source: Tesla Owners Silicon Valley
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GROKIPEDIA OPENS THE TRUTH PORTAL Want to search Grokipedia? Just type your question like you would in Grok, it pulls answers straight from the live encyclopedia. No spin, just data. Grokipedia is built to be truth-seeking and self-correcting, updating in real time without the human bias that slows everything else down Source: Tesla Owners Silicon Valley

Mario Nawfal

173,572 views • 10 months ago

most people stick to just 1 or 2 tables they are familiar with when writing their SQL queries in we built the ability to search for tables, preview tables and easily sample rows from a table so you can find the ones you need + even discover new tables data discovery is a big problem even in small companies. most data doesn't get analyzed because most of company doesn't know it exists
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most people stick to just 1 or 2 tables they are familiar with when writing their SQL queries in we built the ability to search for tables, preview tables and easily sample rows from a table so you can find the ones you need + even discover new tables data discovery is a big problem even in small companies. most data doesn't get analyzed because most of company doesn't know it exists

rahul

11,550 views • 1 year ago

How did the socialists win last night? It wasn’t because of a rigged system or because of Soros. It’s because the DSA in NYC has built an election machine that outworks both the Democrat and Republican parties. This is internal data from their private meetings that prove it.
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How did the socialists win last night? It wasn’t because of a rigged system or because of Soros. It’s because the DSA in NYC has built an election machine that outworks both the Democrat and Republican parties. This is internal data from their private meetings that prove it.

Karlyn Borysenko, anti-communist cult leader

11,068 views • 2 months ago

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David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”

The All-In Podcast

304,450 views • 9 months ago

I got a smart meter recently and saw you can download a HDF file with the data so I had the idea of writing a script that could parse that and show the data in a useful manner. However I discovered that someone has already done this and done a really good job on it. The video explains how it works. In simple terms it uses the ESB smart meter data and shows a breakdown of how much data you are using and when and also recommends plans and estimates what each one would cost based on the data. The tool is available at easier to read on Laptop or Tablet or then your phone to Landscape.
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I got a smart meter recently and saw you can download a HDF file with the data so I had the idea of writing a script that could parse that and show the data in a useful manner. However I discovered that someone has already done this and done a really good job on it. The video explains how it works. In simple terms it uses the ESB smart meter data and shows a breakdown of how much data you are using and when and also recommends plans and estimates what each one would cost based on the data. The tool is available at easier to read on Laptop or Tablet or then your phone to Landscape.

Carlow Weather

298,565 views • 2 years ago

We're launching Lightfield today. It's a CRM designed for founders going zero to one—shaped in the past year by hundreds of founders who took a bet on us and now use it daily, and a waitlist of 20,000 more. When starting out, it takes countless hours of talking to customers to figure out what works. From day zero, Lightfield builds and updates itself from your unstructured conversations with customers. It becomes your customer memory: helping you execute deals, communicate without missing details, and understand patterns across your business. It's free to try. Would love to hear what you think:
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Henri Liriani

317,451 views • 10 months ago

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.
0:22

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

40,562 views • 6 months ago

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:
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Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,420 views • 10 months ago

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership
29:51

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

Matrix AI Network

61,436 views • 3 months ago

Most people browse the internet without thinking about the value they create while doing it. Every click, search, scroll and workflow shows how humans actually use computers and that kind of behavioural data is becoming increasingly valuable as AI learns to take action. Today, most of that value flows one way: users generate the data, platforms capture it, and AI systems become more capable. Action Model is building a different model. Through the browser extension, users can contribute verified actions while browsing normally, helping train a community-owned Large Action Model. We believe the people helping build better AI should have the opportunity to participate in the value they help create. Train it. Earn it. Own it.
0:37

Most people browse the internet without thinking about the value they create while doing it. Every click, search, scroll and workflow shows how humans actually use computers and that kind of behavioural data is becoming increasingly valuable as AI learns to take action. Today, most of that value flows one way: users generate the data, platforms capture it, and AI systems become more capable. Action Model is building a different model. Through the browser extension, users can contribute verified actions while browsing normally, helping train a community-owned Large Action Model. We believe the people helping build better AI should have the opportunity to participate in the value they help create. Train it. Earn it. Own it.

Action Model

11,855 views • 4 days ago

3/ Companies peddle the BS that advertising data is 'anonymous' They want to keep you in the dark. Because, behind the scenes...your weather app is a blinking tracking beacon. And then players like PenLink turn that beacon into a data flow. And sell it to governments.
1:47

3/ Companies peddle the BS that advertising data is 'anonymous' They want to keep you in the dark. Because, behind the scenes...your weather app is a blinking tracking beacon. And then players like PenLink turn that beacon into a data flow. And sell it to governments.

John Scott-Railton

37,960 views • 5 months ago

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓
43:30

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

22,107 views • 2 months ago

Oracle just told every AI company on earth the same thing. Your models are worthless. Not the technology, talent or the billions spent training them. But the data they were trained on. Larry Ellison, the man who built Oracle into the backbone of global enterprise just dropped a bombshell. He said ChatGPT, Gemini, Grok, and Llama, all of them are training on the exact same data.​ The entire public internet, every Wikipedia page, Reddit thread and every news article. That means they're all converging essentially becoming the same product with different logos.​ Ellison's word for it is commodities. But here's where it gets dangerous. He says the real gold isn't public data, It's private data.​ The medical records in hospital systems, the financial data in bank vaults. The supply chain secrets of every Fortune 500 and guess where most of that data already lives. Not Google, Amazon or Microsoft but inside Oracle.​ Oracle databases hold most of the world's high value private enterprise data. So Oracle just launched something called AI Database 26ai.​ It lets the top AI models, ChatGPT, Gemini, Grok, Llama reason directly over a company's private data, without that data ever leaving the vault.​ They're using a technique called RAG, Retrieval Augmented Generation. The AI doesn't train on your data, it searches it in real time.​ Think about what that means. A bank could ask AI to analyze every loan it's ever made without exposing a single customer record. A hospital could have AI diagnose patients using its full medical history without violating HIPAA.​ A defense contractor could let AI reason across classified operations without data leaving a secure environment.​ Ellison is betting this is bigger than the training market. Bigger than the GPU boom. Bigger than the data center buildout.​ He called it the largest and fastest growing market in history.​ The numbers back the ambition. Oracle's remaining performance obligations just hit $523 billion. That's contracted revenue not yet delivered and $300 billion of it comes from OpenAI alone.​ Cloud revenue hit $8 billion in a single quarter, OCI grew 66 percent and GPU revenue surged 177 percent.​ But here's the part nobody's talking about. If private data becomes the real AI moat, then whoever controls the database controls the future of AI.​ And that's a level of power that should make everyone uncomfortable.
0:38

Oracle just told every AI company on earth the same thing. Your models are worthless. Not the technology, talent or the billions spent training them. But the data they were trained on. Larry Ellison, the man who built Oracle into the backbone of global enterprise just dropped a bombshell. He said ChatGPT, Gemini, Grok, and Llama, all of them are training on the exact same data.​ The entire public internet, every Wikipedia page, Reddit thread and every news article. That means they're all converging essentially becoming the same product with different logos.​ Ellison's word for it is commodities. But here's where it gets dangerous. He says the real gold isn't public data, It's private data.​ The medical records in hospital systems, the financial data in bank vaults. The supply chain secrets of every Fortune 500 and guess where most of that data already lives. Not Google, Amazon or Microsoft but inside Oracle.​ Oracle databases hold most of the world's high value private enterprise data. So Oracle just launched something called AI Database 26ai.​ It lets the top AI models, ChatGPT, Gemini, Grok, Llama reason directly over a company's private data, without that data ever leaving the vault.​ They're using a technique called RAG, Retrieval Augmented Generation. The AI doesn't train on your data, it searches it in real time.​ Think about what that means. A bank could ask AI to analyze every loan it's ever made without exposing a single customer record. A hospital could have AI diagnose patients using its full medical history without violating HIPAA.​ A defense contractor could let AI reason across classified operations without data leaving a secure environment.​ Ellison is betting this is bigger than the training market. Bigger than the GPU boom. Bigger than the data center buildout.​ He called it the largest and fastest growing market in history.​ The numbers back the ambition. Oracle's remaining performance obligations just hit $523 billion. That's contracted revenue not yet delivered and $300 billion of it comes from OpenAI alone.​ Cloud revenue hit $8 billion in a single quarter, OCI grew 66 percent and GPU revenue surged 177 percent.​ But here's the part nobody's talking about. If private data becomes the real AI moat, then whoever controls the database controls the future of AI.​ And that's a level of power that should make everyone uncomfortable.

StockMarket.News

1,696,694 views • 6 months ago

Twelve years ago we asked the question: how can we put public data "on wheels"? Datawheel was born from vision that data alone doesn't get far. It needs vehicles to reach hearts and minds. Vehicles that make data easy to find, explore, and understand. Today, Datawheel turns twelve. In these twelve years, we have built dozens of solutions that are helping define the state of the art in public data distribution. Today we celebrate this journey. A journey with a great team and amazing clients. The journey to make public data all it can be. are some of the tools defining all that public data can be. Thank you!!!
1:00

Twelve years ago we asked the question: how can we put public data "on wheels"? Datawheel was born from vision that data alone doesn't get far. It needs vehicles to reach hearts and minds. Vehicles that make data easy to find, explore, and understand. Today, Datawheel turns twelve. In these twelve years, we have built dozens of solutions that are helping define the state of the art in public data distribution. Today we celebrate this journey. A journey with a great team and amazing clients. The journey to make public data all it can be. are some of the tools defining all that public data can be. Thank you!!!

César A. Hidalgo

12,931 views • 1 year ago