Meta Archives - gettectonic.com

30Aug

Detecting the Hot Chatbot

All the tech giants are eager to prove their chatbot is the hottest in the market. Like wild stallions fighting over the mares, Google, Meta, Microsoft, and OpenAI are competing to show that their AI models have the most momentum. Companies with built-in AI like Salesforce occupy a broader sector. Detecting the Hot Chatbot is the challenge for the consumer. Why Detecting the Hot Chatbot Matters These companies have poured immense resources—both talent and money—into developing their models and adding new features. Now, they’re keen to showcase that these investments are yielding results. What’s Happening In the past few dayss, several major players have released new usage statistics: The Big Picture Generative AI is still in its early stages, and the entire industry faces the challenge of proving that these products deliver real value—whether by capturing market share from the lucrative search industry or by helping companies save money through increased productivity. How are you Detecting the Hot Chatbot. In the short term, however, everyone is eager to show they’re leading the pack. TV commercials for generative AI are now common, with Meta, Google, and Microsoft all airing spots, although the effectiveness of these ads varies. Some companies even boast that their commercials were created using AI—not necessarily the most convincing selling point. Between the Lines The competition isn’t just about consumer popularity; it’s also spilling over into the battle to secure business customers. On Wednesday’s earnings call, Salesforce CEO Marc Benioff made a point of distinguishing Salesforce’s new Agentforce AI sales assistant from Microsoft’s Copilot offerings. “This is not Copilot,” Benioff said. “So many customers are disappointed with what they bought from Microsoft Copilot because they’re not getting the accuracy and response they want. Microsoft has let down many customers with AI.” Microsoft quickly responded in a comment to CNBC. “We are hearing something quite different from our Copilot for Microsoft 365 customers,” said corporate VP Jared Spataro. “When I talk to CIOs directly, and if you look at recent third-party data, organizations are betting on Microsoft for their AI transformation.” The Bottom Line The competition is heating up as tech giants vie to prove they have the upper hand in the AI race and the Hot Chatbot. Customers will ultimately decide. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

30Aug

Tectonic Guide to Rag Part 2

The first notable change in the field of language models is the significant expansion of context window sizes and a reduction in token costs. For instance, Anthropic’s largest model, Claude, has a context window exceeding 200,000 tokens, while recent reports indicate that Gemini’s context window can reach up to 10 million tokens. Under such circumstances, Retrieval-Augmented Generation (RAG) may no longer be necessary for many tasks, as all required data can be accommodated within the expanded context window. Several financial and analytical projects have already demonstrated that tasks can be solved without needing a vector database as intermediate storage. This trend of reducing token costs and increasing context window sizes is likely to continue, potentially decreasing the need for external mechanisms in LLMs, although they are still relevant for the time being. If the context window remains insufficient, methods for summarization and context compression have been introduced. LangChain, for example, offers a class called ConversationSummaryMemory to address this challenge. pythonCopy codellm = OpenAI(temperature=0) conversation_with_summary = ConversationChain( llm=llm, memory=ConversationSummaryMemory(llm=OpenAI()), verbose=True ) conversation_with_summary.predict(input=”Hi, what’s up?”) Knowledge Graphs As the volume of data continues to grow, navigating through it efficiently becomes increasingly critical. In certain cases, understanding the structure and attributes of data is essential for effective use. For example, if the data source is a company’s wiki, an LLM might not recognize a phone number unless the structure or metadata indicates that it’s the company’s contact information. Humans can infer meaning from conventions, such as the subdirectory “Company Information,” but standard RAG may miss such connections. This challenge can be addressed by Knowledge Graphs, also known as Knowledge Maps, which provide both raw data and metadata that illustrates how different entities are interconnected. This method is referred to as Graph Retrieval-Augmented Generation (GraphRAG). Graphs are excellent for representing and managing structured, interconnected information. Unlike vector databases, they excel at capturing complex relationships and attributes among diverse data types. Creating a Knowledge Graph The process of creating a knowledge graph typically involves collecting and structuring data, which requires expertise in both the subject matter and graph modeling. However, LLMs can automate a significant portion of this process by analyzing textual data, identifying entities, and recognizing their relationships, which can then be represented in a graph structure. In many cases, an ensemble of vector databases and knowledge graphs can improve accuracy, as discussed previously. For example, search functionality might combine keyword search through a regular database (e.g., Elasticsearch) and graph-based queries. LangChain can also assist in extracting structured data from entities, as demonstrated in this code example: pythonCopy codedocuments = parse_and_load_data_from_wiki_including_metadata() graph_store = NebulaGraphStore( space_name=”Company Wiki”, tags=[“entity”] ) storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex.from_documents( documents, max_triplets_per_chunk=2, space_name=space_name, tags=[“entity”] ) query_engine = index.as_query_engine() response = query_engine.query(“Tell me more about our Company”) Here, searching is conducted based on attributes and related entities, instead of similar vectors. If set up correctly, metadata from the company’s wiki, such as its phone number, would be accessible through the graph. Access Control One challenge with this system is that data access may not be uniform. For instance, in a wiki, access could depend on roles and permissions. Similar issues exist in vector databases, leading to the need for access management mechanisms such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Relationship-Based Access Control (ReBAC). These access control methods function by evaluating paths between users and resources within graphs, such as in systems like Active Directory. To ensure the integrity of data during the ingestion phase, metadata related to permissions must be preserved in both the knowledge graph and vector database. Some commercial vector databases already have this functionality built in. Ingestion and Parsing Data needs to be ingested into both graphs and vector databases, but for graphs, formatting is especially critical since it reflects the data’s structure and serves as metadata. One particular challenge is handling complex formats like PDFs, which can contain diverse elements like tables, images, and text. Extracting structured data from such formats can be difficult, and while frameworks like LLama Parse exist, they are not always foolproof. In some cases, Optical Character Recognition (OCR) may be more effective than parsing. Enhancing Answer Quality Several new approaches are emerging to improve the quality of LLM-generated answers: While these advancements in knowledge graphs, access control, and retrieval mechanisms are promising, challenges remain, particularly around data formatting and parsing. However, these methods continue to evolve, enhancing LLM capabilities and efficiency. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

August 30, 2024in Data

25Aug

Winter 25 Release Developer Enhancements

Developer Enhancements Salesforce Flow Enhancements Flow Orchestrator Enhancements How to Access and Use Key Salesforce Features View the Automation Lightning App: Manage Steps in an Orchestration Stage: Customize the Flow Orchestration Work Guide Component: Customize SMS One-Time Password Delivery for Experience Cloud Sites: Control Authenticated Callouts Permissions: Improve Data Transmission Speed and Security with TLS 1.3: Enable LWC Stacked Modals: Additional Enhancements: Salesforce Mobile App Enhancements Resolve Sync Issues with Offline Drafts: Access Hyperlinks in Enhanced Reports: Messaging in the Salesforce Mobile App: Seller-Focused Sales Mobile Experience: Stay Informed About Upcoming Releases: Check Out Resources: Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

August 25, 2024in Salesforce Shield

20Aug

Apex Sharing Reasons

Creating Apex Sharing Reasons Apex sharing reasons are essential for implementing Apex-managed sharing on custom objects. They define the purpose behind the sharing of records and streamline the process of managing sharing entries programmatically. By using Apex sharing reasons, you can: Why Use Apex Sharing Reasons? When Apex sharing reasons are configured, they appear in the Reason column of the sharing settings for a custom object record. This visibility helps users and administrators understand the intent behind each sharing entry. Key Considerations Steps to Create an Apex Sharing Reason Additional Information For more details about Apex-managed sharing, consult the Apex Developer Guide. This resource provides comprehensive insights into managing record sharing programmatically in Salesforce. By implementing Apex sharing reasons thoughtfully, you can enhance both the functionality and clarity of your record-sharing processes. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

August 20, 2024in Data, Salesforce

03Aug

State of AI

With the Dreamforce conference just a few weeks away, AI is set to be a central theme once again. This week, Salesforce offered a preview of what to expect in September with the release of its “Trends in AI for CRM” report. This report consolidates findings from several Salesforce research studies conducted from February last year to April this year. The report’s executive summary highlights four key insights: The Fear of Missing Out (FOMO) An intriguing statistic from Salesforce’s “State of Data and Analytics” report reveals that 77% of business leaders feel a fear of missing out on generative AI. This concern is particularly pronounced among marketers (88%), followed by sales executives (78%) and customer service professionals (73%). Given the continued hype around generative AI, these numbers are likely still relevant or even higher as of July 2024. As Salesforce AI CEO Clara Shih puts it: “The majority of business executives fear they’re missing out on AI’s benefits, and it’s a well-founded concern. Today’s technology world is reminiscent of 1998 for the Internet—full of opportunities but also hype.” Shih adds: “How do we separate the signal from the noise and identify high-impact enterprise use cases?” The Quest for ROI and Value The surge of hype around generative AI over the past 18 months has led to high expectations. While Salesforce has been more responsible in managing user expectations, many executives view generative AI as a cure-all. However, this perspective can be problematic, as “silver bullets” often miss their mark. Recent tech sector developments reflect a shift toward a longer-term view of AI’s impact. Meta’s share price fell when Mark Zuckerberg emphasized AI as a multi-year project, and Alphabet’s Sundar Pichai faced tough questions from Wall Street about the need for continued investment. State of AI Shih notes a growing impatience with the time required to realize AI’s value: “It’s been over 18 months since ChatGPT sparked excitement about AI in business. Many companies are still grappling with building or buying solutions that are not overly siloed and can be customized. The challenge is finding a balance between quick implementation and configurability.” She adds: “The initial belief was that companies could just integrate ChatGPT and see instant transformation. However, there are security risks and practical challenges. For LLMs to be effective, they need contextual data about users and customers.” Conclusion: A Return to the Future Shih likens the current AI landscape to the late 90s Internet boom, noting: “It’s similar to the late 90s when people questioned if the Internet was overhyped. While some investments will not pan out, the transformative potential of successful use cases is enormous. Just as with the Internet, discovering the truly valuable applications of AI may require experimentation and time. We are very much in the 1998 moment for AI now.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

August 3, 2024in Generative AI

23Jul

What is OpenAI Strawberry?

OpenAI’s Secret Project: “Strawberry” Background and Goals OpenAI, the company behind ChatGPT, is working on a new AI project codenamed “Strawberry,” according to an insider and internal documents reviewed by Reuters. This project, whose details have not been previously reported, aims to showcase advanced reasoning capabilities in OpenAI’s models. The project seeks to enable AI to not only generate answers to queries but also plan and navigate the internet autonomously to perform “deep research.” What is OpenAI Strawberry? Project Overview The “Strawberry” initiative represents an evolution of the previously known Q* project, which demonstrated potential in solving complex problems like advanced math and science questions. While the precise date of the internal document is unclear, it outlines plans for using Strawberry to enhance AI’s reasoning and problem-solving abilities. The source describes the project as a work in progress, with no confirmed timeline for its public release. Technological Approach Strawberry is described as a method of post-training AI models, refining their performance after initial training on large datasets. This post-training phase involves techniques such as fine-tuning, where models are adjusted based on feedback and examples of correct and incorrect responses. The project is reportedly similar to Stanford’s 2022 “Self-Taught Reasoner” (STaR) method, which uses iterative self-improvement to enhance AI’s intelligence levels. Potential and Challenges If successful, Strawberry could revolutionize AI by improving its reasoning capabilities, allowing it to tackle complex tasks that require multi-step problem-solving and planning. This could lead to significant advancements in scientific research, software development, and various other fields. However, the project also raises concerns about ethical implications, control, accountability, and bias, necessitating careful consideration as AI becomes more autonomous. Industry Context OpenAI is not alone in this pursuit. Other major tech companies like Google, Meta, and Microsoft are also experimenting with improving AI reasoning. The broader goal across the industry is to develop AI that can achieve human or super-human levels of intelligence, capable of making major scientific discoveries and planning complex tasks. Conclusion OpenAI’s project Strawberry represents a significant step forward in AI research, pushing the boundaries of what AI can achieve. While the project is still in its early stages, its potential to enhance AI reasoning capabilities is significant. As OpenAI continues to develop and refine Strawberry, its impact on the future of artificial intelligence will be closely watched by researchers and industry leaders alike. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

July 23, 2024in Salesforce

13Jul

Salesforce Notifications

Standard Email Notifications By default, email notifications for Lead, Contact, Account, Case, and Opportunity are automatically enabled for each new Assignee, with no additional setup required. If you have disabled these notifications and want to re-enable them, or if you wish to set up other types of notifications, follow the steps outlined below. Notifications Setup Overview Notifications are configured per Assignee and per MatchGroup. If an Assignee belongs to multiple MatchGroups, they may have different notification settings for each group. Notifications must be enabled for each MatchGroup where the Assignee is assigned. Start by setting up the notification messages for the desired notification type, then enable the notifications in Super Round Robin (SRR). Prerequisites Setting Up Notification Messages For Mobile and Desktop To configure Mobile or Desktop in-app and push notifications, you first need to create a Custom Notification. Mobile and Desktop notifications can be enabled independently, so it’s recommended to create separate notifications for each. For Mobile only: Read more about Mobile Notifications in Salesforce For Email Super Round Robin includes pre-configured email templates for Lead, Contact, Account, Case, and Opportunity notifications, which require no further setup. For other objects, you need to add a new email template with the prefix SRR_Notification_ and store it in the SuperRoundRobin folder. Learn more in Customizing the Email Alerts > Adding Notification Templates for Other Objects. You can also: After making changes to the SRR email templates, purge the Platform Cache in the “Setup And Status” tab. All SRR email templates should be stored in the SuperRoundRobin folder. For SMS To send SMS notifications, ensure you have an Email-to-SMS provider. No further configuration for notification messages is needed. Enabling Notifications in SRR Notification settings are configured in the Assignee’s profile. You can set these while creating a new Assignee or editing an existing one. Access existing Assignees via their MatchGroup or from the Assignee Summary tab. Steps: Troubleshooting Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

July 13, 2024in Data

08Jul

Salesforce Sandboxes Explained

What is a Salesforce Sandbox? A Salesforce Sandbox is a duplicate of your live production environment, offering a safe and isolated space for developers and administrators to test new features, configurations, and code changes. This environment ensures that you can experiment without affecting your live data, allowing for thorough validation before changes are deployed to production. By mimicking the production setup, a sandbox helps identify and fix issues early, leading to more stable applications and a more reliable development process. Salesforce Sandboxes play a crucial role in ensuring the quality and functionality of applications. They provide a secure environment to conduct rigorous testing, allowing teams to catch errors and make improvements before affecting end users. This controlled environment fosters innovation while minimizing the risk of disruptions. Types of Salesforce Sandboxes Salesforce offers different sandbox types, each designed to meet various development and testing needs. Choosing the right sandbox depends on the scope and complexity of the project. 1. Developer Sandbox A Developer Sandbox is ideal for individual developers working on isolated tasks. It includes a copy of the production environment’s metadata but no actual data. This sandbox allows developers to build and test new features or configurations without impacting each other’s work. With daily refreshes, it provides an agile development environment for quick iterations. 2. Developer Pro Sandbox Similar to the Developer Sandbox, the Developer Pro Sandbox has more storage capacity and includes some data. This makes it suitable for larger development projects, where more comprehensive testing is required. It’s perfect for data-intensive tasks and collaborative development, allowing multiple developers to work on shared projects. 3. Partial Copy Sandbox A Partial Copy Sandbox includes all metadata and a sample of production data, making it useful for functional testing and user acceptance testing (UAT). It allows teams to validate application performance with real data, helping uncover data-specific issues. It can be refreshed every five days, providing an accurate testing environment without needing a full data set. 4. Full Copy Sandbox The most comprehensive option, a Full Copy Sandbox replicates the entire production environment, including all data and metadata. This sandbox is ideal for final testing, load testing, and staging before deployment. By mirroring production, it ensures that any changes or updates function seamlessly when deployed to live systems. How to Create a Sandbox in Salesforce How to Refresh a Sandbox in Salesforce Refreshing a sandbox updates it with the latest data and configurations from the production environment. Here’s how to do it: Salesforce Sandbox Templates Sandbox Templates allow you to define specific data and metadata to include in your sandbox. These templates ensure a more tailored environment for testing and development activities. 1. Developer Sandbox Template This template includes metadata from the production environment and is perfect for developers who don’t need production data, allowing them to focus on configurations and code. 2. Developer Pro Sandbox Template Similar to the Developer Sandbox Template but with more storage and data, this is suited for more complex projects requiring integration and quality assurance testing. 3. Partial Copy Sandbox Template Ideal for testing with actual data, this template allows you to include a subset of production data for validation and scenario-based testing. 4. Full Copy Sandbox Template A complete replica of your production environment, this template supports full-scale performance testing and final staging before deployment. Frequently Asked Questions By utilizing Salesforce Sandboxes, your team can ensure thorough development, testing, and training processes, leading to better performance and greater confidence in your Salesforce projects. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

July 8, 2024in Salesforce

02Jul

Best Practices for RAG LLM

Best Practices for Building a RAG LLM Chatbot In today’s competitive landscape, a robust Retrieval-Augmented Generation (RAG) Large Language Model (LLM) chatbot can provide a significant edge. However, implementing such a system involves multiple steps and challenges. Below is a guide to help you navigate this journey effectively. 1. Define Clear Objectives Start by identifying your goals: A clear purpose will drive your design and implementation decisions. 2. Prepare Your Data Your chatbot’s success relies heavily on the quality and structure of your data. Assess Your Data Format: Enrich Your Data: 3. Select the Right Platform Choose a platform based on your goals and data type. Common architectures include: Standard RAG with Vector Databases: Relational Databases: Hybrid Text Search: Graph Databases: 4. Fine-Tune the Model While RAG often enriches data without altering the base model, fine-tuning may be essential in specific scenarios, such as: You can fine-tune open-source models directly or use OpenAI’s APIs for custom training. OpenAI also offers domain-specific GPTs, which can save development time. 5. Master Prompt Engineering Crafting precise prompts is critical to guide LLM responses. Prompts generally include: Example for Elasticsearch: javascriptCopy codeYour task is to create a valid Elasticsearch DSL query. Given the mapping: “`{mapping}“`, translate the query: “`{query}“` into JSON. – Use only fields from the mapping. – Ensure case-insensitivity and support fuzzy matches. – Compress the JSON output, removing spaces. For advanced prompt strategies, see Modern Advances in Prompt Engineering. 6. Iterative Testing Testing machine learning models is inherently complex. Key strategies: 7. Optimize the Front-End Collaborate with UI/UX teams to integrate LLM capabilities seamlessly. Consider: 8. Avoid Common Pitfalls Looking Ahead The rise of LLMs has transformed how we think about search and chatbot applications. While we’re in the early stages, the possibilities are immense. Businesses that adapt and strategically integrate these technologies will unlock innovative opportunities and thrive in the evolving landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

July 2, 2024in Data

30Jun

Gearset’s Features Empower Salesforce Teams

Gearset, a leading Salesforce DevOps platform, is introducing a new suite of features aimed at accelerating and improving reliable release management for Salesforce teams. Gearset’s Features Empower Salesforce Teams. These enhancements, including Archiving, expanded Compare and Deploy capabilities, and Flow Navigator, address critical user feedback and deliver tools for optimizing data and enhancing visibility throughout the DevOps lifecycle. According to Matt Dickens, CPO and co-founder of Gearset, effective DevOps strategies rely on accurate and consistent data to deploy applications at the pace of innovation. The latest features empower teams to manage and apply relevant data in real-time, supporting organizations in navigating continuous changes to drive business growth on Salesforce confidently. Gearset Archiving provides organizations with control over Salesforce data storage by consolidating backup, sandbox seeding, and monitoring solutions into a unified platform. By eliminating outdated data from Salesforce orgs, enterprises can reduce storage costs and prevent performance degradation. Key benefits of Archiving include: Dickens explains that Gearset Archiving allows teams to safely store obsolete or legacy data off-platform while maintaining accessibility for future use, without impacting org performance. Maintaining clean and consistent metadata across the organization is crucial for Gearset. Recognizing limitations in Salesforce deployment tools, Gearset’s enhanced Compare and Deploy capability now enables DevOps teams to quickly analyze and compare metadata, facilitating efficient issue identification and resolution during deployments. This update streamlines operations, ensuring faster delivery of business-critical functionality to maximize ROI. Gearset’s Flow Navigator tool introduces visual comparison capabilities for Salesforce deployments, addressing challenges with Flow versioning and enhancing transparency in deployment processes. By presenting flows in their native Salesforce format, Flow Navigator enables teams to better manage deployments and mitigate potential conflicts. Dickens emphasizes that Gearset remains committed to user-driven innovation, aiming to continuously address Salesforce challenges and deliver solutions that save time, reduce stress, and improve efficiency for all customers. In summary, Gearset’s latest release underscores its dedication to empowering Salesforce teams with robust tools tailored to enhance efficiency and reliability across the DevOps lifecycle. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

June 30, 2024in Data, Enterprise, Salesforce

04Jun

How to Sync Salesforce Lead Data with Meta & Google Ads for Better Conversions

Imagine running an online coaching business with hundreds of potential leads in Salesforce—people who showed interest but never signed up. Instead of letting these leads go cold, you can sync them with Meta and Google Ads to retarget them with personalized offers. Picture a life coach who boosted sign-ups by X% using this exact strategy. By integrating Salesforce with these ad platforms, brands can:✅ Improve targeting – Reach the right people with precision.✅ Personalize ads – Deliver tailored messaging based on lead behavior.✅ Increase conversions – Turn warm leads into paying customers. In this insight, we’ll explore why syncing Salesforce with Meta and Google Ads is a game-changer for marketers. You’ll learn how to leverage your existing lead data for smarter ad campaigns that drive real results. Why Integrate Salesforce with Meta & Google Ads? Most businesses waste ad spend targeting cold audiences while their warm leads sit untouched in Salesforce. By syncing your CRM data with ad platforms, you can: ✔ Retarget high-intent leads – Bring back lost opportunities.✔ Build lookalike audiences – Find new customers similar to your best leads.✔ Track offline conversions – Measure real-world sales driven by ads.✔ Automate lead nurturing – Keep prospects engaged until they convert. Let’s dive into the key benefits and how to set it up. Key Benefits of Syncing Salesforce Lead Data 1. Enhanced Audience Targeting Instead of broad targeting, use your first-party Salesforce data to reach the most relevant prospects. 🔹 Meta Lookalike Audiences 🔹 Google Ads Customer Match 🔹 Retargeting for Higher Conversions 2. Improved Ad Personalization Generic ads get ignored. Personalized ads convert. 🔹 Dynamic Ads Based on CRM Data 🔹 Tailor Messaging to the Lead’s Stage 3. Higher Conversion Rates 🔹 Focus on Warm Leads, Not Cold Traffic 🔹 Leverage First-Party Data 4. Automated Lead Nurturing & Sales Alignment 🔹 Sync High-Intent Leads for Immediate Action 🔹 Close the Loop Between Marketing & Sales How to Sync Salesforce with Meta & Google Ads Option 1: Native Integrations (Best for Automation) 🔹 Salesforce to Meta (Facebook & Instagram Ads) 1. Meta Conversions API (CAPI) 2. Offline Conversions 🔹 Salesforce to Google Ads 1. Customer Match 2. Offline Conversion Tracking Option 2: Manual CSV Uploads (For Small Businesses) Option 3: Third-Party Tools For automated, real-time syncing, tools like EasyInsights make it easy:✔ Auto-sync Salesforce leads to Meta & Google Ads.✔ No manual exports—always up-to-date audiences.✔ Track conversions across the funnel. Conclusion: Turn Salesforce Leads into Paying Customers Syncing Salesforce with Meta and Google Ads ensures:🎯 Better targeting – Reach the right people.💡 Smarter ads – Personalize messaging for higher engagement.📈 More conversions – Retarget warm leads for lower acquisition costs. Next Steps: By leveraging your existing Salesforce data, you can maximize ad ROI and turn more leads into customers—without wasted spend. Contact Tectonic for assistance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

June 4, 2024in Data, Google, Salesforce

AI Agents

Lessons Learned in the First Year of Developing AI Agents In the first year of working on AI agents, valuable insights emerged from direct collaboration with engineers and UX designers, as they iterated on the overall product experience. The objective was to create a platform for customers to use standard data analysis agents and build custom agents tailored to specific tasks and data structures relevant to their business. This platform integrates connectors to databases like Snowflake and BigQuery with built-in security, supports RAG over a metadata layer describing database contents, and facilitates data analysis through SQL, Python, and data visualization tools. Feedback on the effectiveness of these developments came from both internal evaluations and customer insights. Users from Fortune 500 companies utilize these agents daily to analyze their internal data. Key Insights on AI Agents Additional Insights Further insights on code and infrastructure include: These lessons underscore the importance of focusing on reasoning, iterative improvements to the agent-computer interface, understanding model limitations, and building robust supporting infrastructure to enhance AI agent performance and user satisfaction. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

June 4, 2024in Salesforce Einstein

14May

Zero Copy from Salesforce

When was the last time you relocated? Remember the hassle of packing up all your stuff, loading it onto a truck, and unpacking everything at your new digs, hoping it all survived the journey? Now, imagine if your furniture and possessions could simply teleport to your new location in perfect condition. While this may not be possible (yet) in the physical world, the concept of zero copy integration offers a similar seamless experience for handling customer data. Zero Copy from Salesforce. Zero copy integration, also known as zero ETL (extract-transform-load), allows data to be shared among multiple data stores without the need for actual movement or duplication. This is particularly advantageous for companies utilizing cloud data warehouses like Snowflake or Google BigQuery. Some companies hesitate to adopt a customer data platform (CDP) due to concerns about duplicating data. However, with zero copy integration, users can leverage the benefits of a CDP—such as data harmonization, identity management, built-in analytics, and activation—without the drawbacks of physical data movement. Some functions related to data have advanced with astonishing speed: generation, dissemination, storage, even security (and yes, that one still has many challenges). But there are others still mired in the stone age—call it the 80s—of the digital era. For example, think copies. How many business professionals still make multiple copies of sensitive data, just as it’s been done for the last four decades? What does this endless duplication do for regulatory risk, security threats and privacy liabilities? This is a symptom of a larger problem: Far from the data-centric universe we all envisioned, we’re beholden instead to the apps used to create, store, and distribute data. There’s an app for everything, but also a database for every app, leading to dozens of data silos undergoing hundreds of integrations, increasing fragmentation, complexity, and costs. The basic idea, according to Dan DeMers, Cinchy’s CEO, is that the framework aims to remove application data silos by using access-based data collaboration versus standard API-base data integration that involves copying data and branding it with complex app-specific coding. This would be done by access controls set in the data layer. It would also involve: What is Zero Copy Integration? Zero copy integration enables access to data stored in multiple databases simultaneously without the need to move, copy, or reformat the data. This approach not only facilitates faster and easier access to data but also reduces costs and minimizes the risk of errors associated with data movement or transformation. Comparing Traditional Methods with Zero Copy Integration: Traditional Methods: Zero Copy Integration: How It Works: From CDP to Data Warehouse In this scenario, data is accessed from the CDP and shared with the data warehouse, a process known as data sharing. The steps typically involve: How It Works: From Data Warehouse to CDP Conversely, in this scenario, data is accessed from the data warehouse and integrated into the CDP, a process referred to as data federation. The steps typically involve: Real-World Application: How Buyers Edge Utilizes Zero Copy Technology Buyers Edge, a leading procurement optimization company in the food service industry, leverages zero copy technology to access purchase data stored in a data warehouse while building a unified customer profile in a CDP. This allows them to provide better customer insights to their sales and marketing teams. By eliminating the need for data movement, duplication, or reformatting, zero copy technology streamlines data access, harmonizes data for insights and analytics, and provides a real-time holistic view of customers. As data volumes continue to grow exponentially, technologies like zero copy integration will play a crucial role in simplifying data management and facilitating seamless access to valuable insights. Whether it’s optimizing business processes or enhancing customer experiences, zero copy integration empowers organizations to navigate the data landscape with agility and efficiency. Be a zero copy hero and unlock the full potential of your data management strategy. “In today’s digital landscape, companies struggle with islands of data spread across various systems. With this global ecosystem of partners, companies can access all of their data, no matter where it resides, and unlock the power of all of that data within Salesforce — creating more personalized customer interactions and establishing a foundation for trusted AI, in less time and at lower cost.” Brian Millham, President and Chief Operating Officer at Salesforce The Zero Copy Partner Network features initial partners Amazon Web Services (AWS), Databricks, Google Cloud, and Snowflake, and adds Microsoft, all committed to zero copy integrations with Salesforce that give customers a secure and cost-effective way to connect and take action on all of their data New zero copy support for data warehouses and data lakehouses built on open table formats, like Apache Iceberg, with Salesforce Data Cloud, removes the need to copy or move data, so customers can unlock their data, powering Customer 360 experiences with AI, automation, and analytics The network includes Salesforce ISV partners, building new Zero Copy Data Kits to bring valuable new datasets to Salesforce Data Cloud, and Salesforce SI partners including Accenture, Cognizant, Deloitte Digital, and PwC, helping customers with zero copy Salesforce Data Cloud implementations. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 14, 2024in Salesforce, Salesforce Data Cloud, Snowflake

29Apr

Enhanced WhatsApp Channel In Salesforce Service Cloud

Setting up an enhanced WhatsApp channel in Salesforce Service Cloud allows your support team to assist customers over WhatsApp with advanced features like messaging session transfers, collaboration with supervisors, and interactive content sharing. This insight provides a guide on how to create an enhanced WhatsApp channel, including upgrading from a standard channel or migrating a number from an external business service provider. Required Salesforce Editions and User Permissions To configure WhatsApp channels, you need the following user permissions: Note: Standard WhatsApp channels will be retired in mid-2025. Upgrade to enhanced channels before July 30, 2025, to avoid interruptions. Step 1: Prepare to Create Your Channel Step 2: Create Your Channel in Setup Step 3: Activate Your Channel If activation is not completed within 14 days, reverify your WhatsApp number through Meta Business Manager or repeat the setup flow in Messaging Settings. Enhanced WhatsApp Channel In Salesforce Service Cloud Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

April 29, 2024in Salesforce

24Apr

What is the SORA Model – In OpenAI’s Own Words

Introducing Sora: The Future of AI-Generated Video A New Frontier in AI Simulation We’re pioneering AI that understands and simulates physical reality—training models to help solve real-world interaction challenges. Meet Sora, our breakthrough text-to-video model that creates high-fidelity, minute-long videos from simple prompts while maintaining stunning visual quality. Sora in Action: Sample Creations 🏙️ “Neon Tokyo Stroll” A stylish woman in a black leather jacket walks confidently down a reflective Tokyo street, bathed in glowing neon signs. 🦣 “Prehistoric Giants” Woolly mammoths trek through snowy meadows, their fur rippling in the wind against mountain backdrops—captured in cinematic detail. 🚀 “Space Adventure Trailer” *A 30-year-old astronaut in a red wool helmet explores salt deserts under blue skies—shot on “35mm film” with vivid colors.* 🌊 “Big Sur’s Raw Beauty” A drone captures waves crashing against rugged cliffs at sunset, with a distant lighthouse completing this Pacific Coast masterpiece. 🕯️ “Curious Creature” *A fluffy monster kneels beside a melting candle, wide-eyed with wonder in a cozy 3D-rendered scene.* Why Sora Stands Out ✅ Complex scene generation with multiple characters✅ Physics-aware motion (though still improving)✅ Emotionally expressive characters✅ Multi-shot continuity within single videos Current Limitations ⚠️ Physics inaccuracies (e.g., objects not reacting realistically)⚠️ Spatial confusion (left/right or camera movement errors)⚠️ Spontaneous entity generation in crowded scenes Example: A bitten cookie might not show teeth marks; a basketball might “morph” unnaturally after swishing through a hoop. Safety First We’re implementing robust safeguards: Early Access & Future Vision Sora is now available to: This controlled rollout lets us refine Sora responsibly before broader release. Technical breakthrough: Sora uses diffusion transformers and DALL·E 3’s recaptioning tech to achieve unprecedented video coherence across variable durations/resolutions. This Is Just the Beginning Sora represents a critical step toward AI that understands physical reality—a key milestone on the path to AGI. Explore more examples and our technical report here. The future of video creation is being rewritten. Stay tuned. 🎥✨ More Sample Prompts & Outputs Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

April 24, 2024in Data

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