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New Salesforce Vector Database for Data Cloud integrates structured data with unstructured data—case files, call transcripts, Slack, emails, PDFs, audio, and video. This synergy powers the most accurate AI results, advanced automations, and profound customer insights, enabling seamless collaboration between AI and human workers to supercharge your sales, service,...

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Marc Benioff

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57,904 views • 2 years ago •via X (Twitter)

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Andrew Russo2 years ago

@salesforce How can we get emails from our sales teams inboxes into data cloud?

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Simon Severino | The Sales Booster2 years ago

@salesforce Proud shareholder. Keep rolling!

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Stephen Mahoney2 years ago

@salesforce

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40,810 views • 9 months ago

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110,606 views • 2 years ago

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36,504 views • 10 months ago

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Box

10,565 views • 1 year ago

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: ​ 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. ​ 2. The connector ecosystem to load data from unstructured data sources is very immature. ​ 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. ​ The goal of a RAG Pipeline is to solve these problems. ​ The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. ​ At a high level, there are four different stages in the architecture of a RAG pipeline: ​ 1. Ingestion: Here is where the pipeline loads the information from the data source. ​ 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. ​ 3. Transform: Where the pipeline chunks the data and generates document embeddings. ​ 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. ​ There are different rabbit holes at each one of these stages. Here are three of them: ​ 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. ​ 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. ​ 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. ​ In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. ​ I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. ​ ​ If you have a few documents lying around, set up a free account and give it a try.
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Traditional data pipelines don't work for RAG applications. There are 3 issues with them: ​ 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. ​ 2. The connector ecosystem to load data from unstructured data sources is very immature. ​ 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. ​ The goal of a RAG Pipeline is to solve these problems. ​ The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. ​ At a high level, there are four different stages in the architecture of a RAG pipeline: ​ 1. Ingestion: Here is where the pipeline loads the information from the data source. ​ 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. ​ 3. Transform: Where the pipeline chunks the data and generates document embeddings. ​ 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. ​ There are different rabbit holes at each one of these stages. Here are three of them: ​ 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. ​ 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. ​ 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. ​ In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. ​ I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. ​ ​ If you have a few documents lying around, set up a free account and give it a try.

Santiago

40,441 views • 1 year ago

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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 🚀

39,331 views • 5 months ago

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Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

84,686 views • 1 year ago

Introducing Einstein Sales Agent on Salesforce's new Agentforce Platform! Scale your sales force easily with autonomous sales agents! Sales agents can turn deals over to humans directly in Salesforce Sales Cloud! Humans with AI drive customer success, together. 🔹 24/7 automated pipeline nurturing 🔹 Personalized engagement with inbound leads using trusted CRM data 🔹 Seamless transitions and human handoffs across all channels 🔹 No need for complex conversational scripts 🔹 Operates across sales, service, marketing, and commerce 🔹 Reaches leads on their preferred channel, in their preferred language 🔹 Configurable with sales strategies and knowledge, just like training a seller 🔹 Transparent governance dashboards for full oversight 🔹 Extensible low-code tools 🔹 MuleSoft for cross-system actions 🔹 Real-time, harmonized data with Data Cloud See Einstein Sales Agents in action at Dreamforce 2024! Now everyone is an Einstein. ❤️
3:11

Introducing Einstein Sales Agent on Salesforce's new Agentforce Platform! Scale your sales force easily with autonomous sales agents! Sales agents can turn deals over to humans directly in Salesforce Sales Cloud! Humans with AI drive customer success, together. 🔹 24/7 automated pipeline nurturing 🔹 Personalized engagement with inbound leads using trusted CRM data 🔹 Seamless transitions and human handoffs across all channels 🔹 No need for complex conversational scripts 🔹 Operates across sales, service, marketing, and commerce 🔹 Reaches leads on their preferred channel, in their preferred language 🔹 Configurable with sales strategies and knowledge, just like training a seller 🔹 Transparent governance dashboards for full oversight 🔹 Extensible low-code tools 🔹 MuleSoft for cross-system actions 🔹 Real-time, harmonized data with Data Cloud See Einstein Sales Agents in action at Dreamforce 2024! Now everyone is an Einstein. ❤️

Marc Benioff

56,695 views • 1 year ago

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”
1:16

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”

David Fields

13,639 views • 1 year ago

Build your own Gen AI Agents with Salesforce’s new Agentforce platform—anyone can create, test, and scale custom Gen AI Agents using our unique Einstein1 low-code platform—deeply integrated with our Einstein1 platform. The future of AI Agent and App Dev is in your hands! With Agentforce, your custom AI agents: - Achieve greater accuracy by securely grounding in all your enterprise data with Data Cloud - Take action seamlessly by leveraging Flow automations - Connect effortlessly to your enterprise APIs through MuleSoft - Test with confidence in Sandboxes before deployment - Use in your apps, web, social, and Slack. Humans with AI drive customer success, together. Now everyone is an Einstein. More to come at Dreamforce 2024! 💙
3:52

Build your own Gen AI Agents with Salesforce’s new Agentforce platform—anyone can create, test, and scale custom Gen AI Agents using our unique Einstein1 low-code platform—deeply integrated with our Einstein1 platform. The future of AI Agent and App Dev is in your hands! With Agentforce, your custom AI agents: - Achieve greater accuracy by securely grounding in all your enterprise data with Data Cloud - Take action seamlessly by leveraging Flow automations - Connect effortlessly to your enterprise APIs through MuleSoft - Test with confidence in Sandboxes before deployment - Use in your apps, web, social, and Slack. Humans with AI drive customer success, together. Now everyone is an Einstein. More to come at Dreamforce 2024! 💙

Marc Benioff

70,971 views • 1 year ago

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:
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Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

167,963 views • 10 months ago

Your AI Agents can only analyze 1GB of data Enterprises have 1,000,000x as much data We fixed that... It took 3 years and building an entirely new execution environment to pull this off. TextQL lets AI agents analyze your ENTIRE enterprise data - not just the 0.0001% they can handle today. Here's what makes it so different: 1. ALL your data, not samples. AI today: 1GB demos. Your reality: 350TB across 47 systems. TextQL: Everything. At once. 2. Real enterprise questions.ChatGPT writes poems. We answer why renewal rates dropped 3% after ERP upgrades. 3. No more sandbox limitations.Everyone else: Jupyter notebook with 10GB RAM. Us: A data center. ------------------------------ In 2021, I watched Fortune 500s spend millions on AI that could only analyze data that fits on a USB stick. Today: - We process 10+ petabytes daily - Our biggest customer analyzes 100TB in real-time - Average query spans 50+ data sources And we just proved what everyone said was impossible - AI that can actually build a digital twin of your enterprise ------------------------------ To celebrate, we're giving away free data architecture assessments. We'll show you exactly how much of your data is invisible to AI right now (spoiler: it's probably 99.9%). Comment "TextQL" and we'll send you the assessment link.
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Your AI Agents can only analyze 1GB of data Enterprises have 1,000,000x as much data We fixed that... It took 3 years and building an entirely new execution environment to pull this off. TextQL lets AI agents analyze your ENTIRE enterprise data - not just the 0.0001% they can handle today. Here's what makes it so different: 1. ALL your data, not samples. AI today: 1GB demos. Your reality: 350TB across 47 systems. TextQL: Everything. At once. 2. Real enterprise questions.ChatGPT writes poems. We answer why renewal rates dropped 3% after ERP upgrades. 3. No more sandbox limitations.Everyone else: Jupyter notebook with 10GB RAM. Us: A data center. ------------------------------ In 2021, I watched Fortune 500s spend millions on AI that could only analyze data that fits on a USB stick. Today: - We process 10+ petabytes daily - Our biggest customer analyzes 100TB in real-time - Average query spans 50+ data sources And we just proved what everyone said was impossible - AI that can actually build a digital twin of your enterprise ------------------------------ To celebrate, we're giving away free data architecture assessments. We'll show you exactly how much of your data is invisible to AI right now (spoiler: it's probably 99.9%). Comment "TextQL" and we'll send you the assessment link.

ethan ding 📊

161,864 views • 11 months ago

The brain of #Agentforce is our #Atlas learning and reasoning engine, developed by Salesforce AI Research 🧠 Atlas reasons, generates a plan, evaluates, and refines until it feels confident it knows how to accomplish your goals. It pulls in the data it needs from Data Cloud using advanced #RAG and custom CRM #embeddings. Then Atlas takes action across the Customer 360, whether that’s automating a campaign in Agentforce Marketing, resolving a case in Agentforce Service or, in OpenTable’s case, confirming that perfect dinner reservation for your friend's birthday. The more you use Atlas, the smarter it gets. We've pioneered reinforcement learning based on customer outcomes (#RLCO)— Agentforce is continuously tuning to align with your business outcomes, such as higher conversion and increased CSAT. Your data is never our product, which means your outcome data is proprietary to you. The results we're seeing are incredibly promising. Agentforce powered by Atlas delivers 33% more accuracy and 2x more relevance than DIY AI. It's the difference between a DIY science project and a trusted enterprise-grade agent you can confidently deploy into production. #DF24 #agents #enterprise #reasoning #dreamforcekeynote
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The brain of #Agentforce is our #Atlas learning and reasoning engine, developed by Salesforce AI Research 🧠 Atlas reasons, generates a plan, evaluates, and refines until it feels confident it knows how to accomplish your goals. It pulls in the data it needs from Data Cloud using advanced #RAG and custom CRM #embeddings. Then Atlas takes action across the Customer 360, whether that’s automating a campaign in Agentforce Marketing, resolving a case in Agentforce Service or, in OpenTable’s case, confirming that perfect dinner reservation for your friend's birthday. The more you use Atlas, the smarter it gets. We've pioneered reinforcement learning based on customer outcomes (#RLCO)— Agentforce is continuously tuning to align with your business outcomes, such as higher conversion and increased CSAT. Your data is never our product, which means your outcome data is proprietary to you. The results we're seeing are incredibly promising. Agentforce powered by Atlas delivers 33% more accuracy and 2x more relevance than DIY AI. It's the difference between a DIY science project and a trusted enterprise-grade agent you can confidently deploy into production. #DF24 #agents #enterprise #reasoning #dreamforcekeynote

Clara Shih

11,136 views • 1 year ago

Announcing Superblocks on Snowflake: Secure Enterprise Vibe Coding on the AI Data Cloud This couldn't be more timely. Vibe coding is exploding inside the enterprise. It’s also becoming the fastest path to a data breach. Every CEO is trying to accelerate adoption of vibe coding for business users automate, without blocking on engineering. Every VP of Data and CIO is trying to figure out how to lock down their data, harden their permissions, and gain centralized visibility. That’s exactly what this partnership unlocks. Superblocks becomes the enterprise platform for governed vibe coding across business teams, deeply embedded within Snowflake: > Snowflake Postgres becomes your secure vibe coding database. Superblocks spins it up in every app for fast writes and net new operational workflow data >Snowflake Cortex becomes the inference engine for Superblocks. Your prompts and vibe coded AI features run within your trusted security perimeter >Snowflake Warehouse governance is enforced. AI apps use federated token passthrough so your existing row level access policies, dynamic data masking, tag-based-access controls are respected “With Superblocks on Snowflake, any business team can safely build AI applications governed on their Snowflake data. It’s a dream come true.” -Unmesh Jagtap, Director of Product, Snowflake Now enterprises can stop playing whack-a-mole, and move your organization past the AI prototype graveyard. If you’re a Snowflake customer, book a demo (link in comments) Thanks to the Snowflake team for the partnership: Christian Kleinerman, Vivek Raghunathan, Bala Kasiviswanathan, Unmesh Jagtap, and Myles Borins for helping shape how the industry democratizes governed AI app development while centralizing control on the AI Data Cloud.
2:39

Announcing Superblocks on Snowflake: Secure Enterprise Vibe Coding on the AI Data Cloud This couldn't be more timely. Vibe coding is exploding inside the enterprise. It’s also becoming the fastest path to a data breach. Every CEO is trying to accelerate adoption of vibe coding for business users automate, without blocking on engineering. Every VP of Data and CIO is trying to figure out how to lock down their data, harden their permissions, and gain centralized visibility. That’s exactly what this partnership unlocks. Superblocks becomes the enterprise platform for governed vibe coding across business teams, deeply embedded within Snowflake: > Snowflake Postgres becomes your secure vibe coding database. Superblocks spins it up in every app for fast writes and net new operational workflow data >Snowflake Cortex becomes the inference engine for Superblocks. Your prompts and vibe coded AI features run within your trusted security perimeter >Snowflake Warehouse governance is enforced. AI apps use federated token passthrough so your existing row level access policies, dynamic data masking, tag-based-access controls are respected “With Superblocks on Snowflake, any business team can safely build AI applications governed on their Snowflake data. It’s a dream come true.” -Unmesh Jagtap, Director of Product, Snowflake Now enterprises can stop playing whack-a-mole, and move your organization past the AI prototype graveyard. If you’re a Snowflake customer, book a demo (link in comments) Thanks to the Snowflake team for the partnership: Christian Kleinerman, Vivek Raghunathan, Bala Kasiviswanathan, Unmesh Jagtap, and Myles Borins for helping shape how the industry democratizes governed AI app development while centralizing control on the AI Data Cloud.

Brad Menezes

12,869 views • 2 months ago

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 views • 8 months ago