Design Archives - gettectonic.com

14Aug

GitHub Copilot Autofix

On Wednesday, GitHub announced the general availability of Copilot Autofix, an AI-driven tool designed to identify and remediate software vulnerabilities. Originally unveiled in March and tested in public beta, Copilot Autofix integrates GitHub’s CodeQL scanning engine with GPT-4, heuristics, and Copilot APIs to generate code suggestions for developers. The tool provides prompts based on CodeQL analysis and code snippets, allowing users to accept, edit, or reject the suggestions. In a blog post, Mike Hanley, GitHub’s Chief Security Officer and Senior Vice President of Engineering, highlighted the challenges developers and security teams face in addressing existing vulnerabilities. “Code scanning tools can find vulnerabilities, but the real issue is remediation, which requires security expertise and time—both of which are in short supply,” Hanley noted. “The problem isn’t finding vulnerabilities; it’s fixing them.” According to GitHub, the private beta of Copilot Autofix showed that users could respond to a CodeQL alert and automatically remediate a vulnerability in a pull request in just 28 minutes on average, compared to 90 minutes for manual remediation. The tool was even faster for common vulnerabilities like cross-site scripting, with remediation times averaging 22 minutes compared to three hours manually, and SQL injection flaws, which were fixed in 18 minutes on average versus almost four hours manually. Hanley likened the efficiency of Copilot Autofix in fixing vulnerabilities to the speed at which GitHub Copilot, their generative AI coding assistant released in 2022, produces code for developers. However, there have been concerns that GitHub Copilot and similar AI coding assistants could replicate existing vulnerabilities in the codebases they help generate. Industry analyst Katie Norton from IDC noted that while the replication of vulnerabilities is concerning, the rapid pace at which AI coding assistants generate new software could pose a more significant security issue. Chris Wysopal, CTO and co-founder of Veracode, echoed this concern, pointing out that faster coding speeds have led to more software being produced and a larger backlog of vulnerabilities for developers to manage. Norton also emphasized that AI-powered tools like Copilot Autofix could help alleviate the burden on developers by reducing these backlogs and enabling them to fix vulnerabilities without needing to be security experts. Other vendors, including Mobb and Snyk, have also developed AI-powered autoremediation tools. Initially supporting JavaScript, TypeScript, Java, and Python during its public beta, Copilot Autofix now also supports C#, C/C++, Go, Kotlin, Swift, and Ruby. Hanley also highlighted that Copilot Autofix would benefit the open-source software community. GitHub has previously provided open-source maintainers with free access to enterprise security tools for code scanning, secret scanning, and dependency management. Starting in September, Copilot Autofix will also be made available for free to these maintainers. “As the global home of the open-source community, GitHub is uniquely positioned to help maintainers detect and remediate vulnerabilities, making open-source software safer and more reliable for everyone,” Hanley said. Copilot Autofix is now available to all GitHub customers globally. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 14, 2024in Data, Enterprise, Generative AI, Laws

13Aug

Qwary Salesforce Integration

Qwary Enhances Customer Insights with New Salesforce Integration HERNDON, Va., Aug. 13, 2024 /PRNewswire/ — While surveys have long been a staple for gathering customer feedback, data entry often poses a challenge in obtaining comprehensive insights. Qwary’s new Salesforce integration aims to resolve this issue by enabling seamless data transfer and synchronization between the two platforms. This integration allows teams to consolidate customer information into a single hub, providing real-time visibility and enhancing strategic planning and collaboration. Key features include creating email campaigns, importing contacts, mapping survey results, and automating event-based workflows. What Is Qwary’s Salesforce Integration? Qwary’s Salesforce integration is designed to streamline the analysis of Salesforce survey data, offering a more efficient way to understand customer interactions with your brand. By integrating survey feedback with CRM data, this tool helps you quickly adapt your products and services to meet evolving customer needs. It tracks customer journeys, collects feedback, and reveals pain points, enabling you to deliver tailored solutions. Benefits of Using Qwary’s Salesforce Integration Qwary’s integration offers several notable benefits: Automate Feedback Collection The integration automates the feedback collection process by triggering surveys at strategic points in the customer lifecycle. This allows your team to act swiftly to foster engagement and generate leads. Gain Actionable Insights Seamlessly integrating with Salesforce CRM, Qwary scores, analyzes, and enriches customer data, helping your team identify emerging trends and seize opportunities for personalization and customer development. Synchronize Data Automatically With Qwary’s integration, your contact data is consolidated into a single, reliable source of truth. Whether you’re using Salesforce or Qwary, automated data synchronization ensures consistency and provides real-time updates. Collaborate Effectively The integration promotes effective teamwork by sharing data between Salesforce and Qwary, enabling your team to solve problems collaboratively and refine strategies to boost customer retention. Key Capabilities Qwary’s Salesforce integration excels in managing customer feedback, automating workflows, and consolidating contact data: Salesforce Workflow Automation The integration simplifies scheduling and automating survey triggers, eliminating manual processes. Surveys can be initiated via email or following significant events, with responses seamlessly mapped into Salesforce. This creates a comprehensive view of customer behavior, helping your team act on insights, strengthen connections, and enhance satisfaction. Contact Data Importation Qwary facilitates quick access to Salesforce contacts, providing a holistic view of your customer base. The integration streamlines contact data importation and updates, eliminating manual data entry and speeding up data management. Potential Business Impacts By combining automation, synchronization, and data consolidation with a user-friendly interface, Qwary’s Salesforce integration enhances your sales team’s ability to collect and leverage customer feedback. Immediate access to comprehensive consumer insights allows your business to respond promptly to customer needs, improving satisfaction and loyalty. Real-time data aggregation helps your company adapt quickly and refine offerings to exceed customer expectations. Stay Ahead with Qwary’s Salesforce Integration Qwary continuously updates its solutions to meet the evolving needs of businesses focused on customer engagement. Leveraging automation, synchronization, and advanced analytics through an accessible platform, Qwary’s Salesforce integration empowers your team to enhance offerings and connect with customers efficiently. By optimizing the use of survey data and Salesforce feedback, Qwary keeps your business at the forefront of market trends, enabling you to consistently delight your customers. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 13, 2024in Generative AI, Marketing Automation, Salesforce Data Cloud, Salesforce Einstein, Salesforce Implementation Services, Salesforce Managed Services, Salesforce Marketing Automation, Salesforce Optimization, Salesforce Personalization

11Aug

AI Strategy and Tectonic

AI Strategy and Tectonic Recent advancements in artificial intelligence (AI) have showcased the immense potential of this technology to transform both business and society. However, as organizations scale AI systems, they must ensure these systems are structured and governed responsibly to prevent bias and errors. The widespread use of AI can have significant implications, and without proper safeguards, businesses risk costly outcomes. As your organization leverages diverse datasets to apply machine learning and automate workflows, it’s critical to implement strong guardrails to maintain data quality, ensure compliance, and promote transparency within AI systems. Tectonic is here to help you implement AI responsibly, focusing on areas where it can quickly and ethically deliver real business benefits. Our comprehensive portfolio of enterprise-grade AI products and analytics solutions is designed to minimize the challenges of AI adoption, establish a solid data foundation, and optimize for positive outcomes while ensuring responsible AI use. Global enterprises turn to Tectonic as a trusted partner in their AI transformation journeys. As a leading AI consulting firm, we enhance the value of AI and cloud technologies in driving business transformation. By working with our own advanced AI technologies and an open ecosystem of partners, we deliver AI models on any cloud, all guided by the principles of ethics and trust. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 11, 2024in Artificial Intelligence, Data, Enterprise, Technology

11Aug

Licensing and Permitting with Salesforce Public Sector Solutions

Licensing, Permitting, and Inspections Inspections are a crucial part of the licensing and permitting process, whether they involve a new home, a business seeking to open, or a follow-up based on a public complaint. Licensing and Permitting with Salesforce Public Sector Solutions aids in the critical steps in the process. Inspections can also be used independently for other assessments related to regulatory requirements, safety, and auditing. Assignments Inspections can be assigned with just a few clicks. The application reviewer or inspection dispatcher can quickly designate an inspector and schedule the visit. Mobile Inspections Public Sector Mobile Inspection automatically notifies inspectors of their daily visit plans on their mobile devices. Inspectors can use filters to view other days or prioritize tasks based on urgency and status. Inspector Checklists Configurable inspection checklists help ensure that inspectors don’t miss any steps during their onsite visits, enhancing community safety and reducing the need for follow-up inspections. Assessment Indicators Inspectors document compliance or violations against regulatory codes using configurable fields. They can also upload files, videos, or pictures from their mobile devices to support their assessments. Regulatory Codes Inspectors can easily reference relevant regulatory codes to verify their assessments, ensuring accuracy and compliance. Digital Signatures Digital signatures are captured on-site, eliminating the need for additional paperwork and streamlining the inspection process. No more emails, stamps, or standing in line. Enforcement Compliance officers can follow up on violations and create enforcement actions to ensure that stakeholders address any oustanding issues. Unified View Government agencies can access a unified 360 degree view of all relevant information in one place, enabling them to track resolution progress and assess final compliance. Experience Portal Throughout the process, stakeholders can stay informed about the status of their inspection and communicate with agency employees to ask questions or provide updates. Salesforce Experience Cloud provides an easy to apply solution to a constituent portal. Licensing and Permitting with Salesforce Public Sector Solutions With Salesforce Licensing and Permitting you can download and install process libraries that contain components for automating licensing and permitting workflows saving more time. Public Sector Solutions provides OmniScript flows and components that automate these licensing and permitting workflows. Some components are available directly in Public Sector Solutions; others are not built-in and require that you download them from GitHub. Like1 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 11, 2024in Public Sector Solutions, Salesforce Public Sector Solutions

10Aug

Generative AI Tools

One of the most significant use cases for generative AI in business is customer service and support. Most of us have likely experienced the frustration of dealing with traditional automated systems. However, today’s advanced AI, powered by large language models and natural language chatbots, is rapidly improving these interactions. While many still prefer human agents for complex or sensitive issues, AI is proving highly capable of handling routine inquiries efficiently. Here’s an overview of some of the top AI-powered tools for automating customer service. Although the human element will always be essential in customer experience, these tools free up human agents from repetitive tasks, allowing them to focus on more complex challenges requiring empathy and creativity. Cognigy Cognigy is an AI platform designed to automate customer service voice and chat channels. It goes beyond simply reading FAQ responses by delivering personalized, context-sensitive answers in multiple languages. Cognigy’s AI Copilot feature enhances human contact center workers by offering real-time AI assistance during interactions, making both fully automated and human-augmented support possible. IBM WatsonX Assistant IBM’s WatsonX Assistant helps businesses create AI-powered personal assistants to streamline tasks, including customer support. With its drag-and-drop configuration, companies can set up seamless self-service experiences. The platform uses retrieval-augmented generation (RAG) to ensure that responses are accurate and up-to-date, continuously improving as it learns from customer interactions. Salesforce Einstein Service Cloud Einstein Service Cloud, part of the Salesforce platform, automates routine and complex customer service tasks. Its AI-powered Agentforce bots manage “low-touch” interactions, while “high-touch” cases are overseen by human agents supported by AI. Fully customizable, Einstein ensures that responses align with your brand’s tone and voice, all while leveraging enterprise data securely. Zendesk AI Zendesk, a leader in customer support, integrates generative AI to boost its service offerings. By using machine learning and natural language processing, Zendesk understands customer sentiment and intent, generates personalized responses, and automatically routes inquiries to the most suitable agent—be it human or machine. It also provides human agents with real-time guidance on resolving issues efficiently. Ada Ada is a conversational AI platform built for large-scale customer service automation. Its no-code interface allows businesses to create custom bots, reducing the cost of handling inquiries by up to 78% per ticket. By integrating domain-specific data, Ada helps improve both support efficiency and customer experience across omnichannel support environments. More AI Tools for Customer Service There are numerous other AI tools designed to enhance automated customer support: While AI tools are transforming customer service, the key lies in using them to complement human agents, allowing for a balance of efficiency and personalized care. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 10, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein

10Aug

Small Language Models

Large language models (LLMs) like OpenAI’s GPT-4 have gained acclaim for their versatility across various tasks, but they come with significant resource demands. In response, the AI industry is shifting focus towards smaller, task-specific models designed to be more efficient. Microsoft, alongside other tech giants, is investing in these smaller models. Science often involves breaking complex systems down into their simplest forms to understand their behavior. This reductionist approach is now being applied to AI, with the goal of creating smaller models tailored for specific functions. Sébastien Bubeck, Microsoft’s VP of generative AI, highlights this trend: “You have this miraculous object, but what exactly was needed for this miracle to happen; what are the basic ingredients that are necessary?” In recent years, the proliferation of LLMs like ChatGPT, Gemini, and Claude has been remarkable. However, smaller language models (SLMs) are gaining traction as a more resource-efficient alternative. Despite their smaller size, SLMs promise substantial benefits to businesses. Microsoft introduced Phi-1 in June last year, a smaller model aimed at aiding Python coding. This was followed by Phi-2 and Phi-3, which, though larger than Phi-1, are still much smaller than leading LLMs. For comparison, Phi-3-medium has 14 billion parameters, while GPT-4 is estimated to have 1.76 trillion parameters—about 125 times more. Microsoft touts the Phi-3 models as “the most capable and cost-effective small language models available.” Microsoft’s shift towards SLMs reflects a belief that the dominance of a few large models will give way to a more diverse ecosystem of smaller, specialized models. For instance, an SLM designed specifically for analyzing consumer behavior might be more effective for targeted advertising than a broad, general-purpose model trained on the entire internet. SLMs excel in their focused training on specific domains. “The whole fine-tuning process … is highly specialized for specific use-cases,” explains Silvio Savarese, Chief Scientist at Salesforce, another company advancing SLMs. To illustrate, using a specialized screwdriver for a home repair project is more practical than a multifunction tool that’s more expensive and less focused. This trend towards SLMs reflects a broader shift in the AI industry from hype to practical application. As Brian Yamada of VLM notes, “As we move into the operationalization phase of this AI era, small will be the new big.” Smaller, specialized models or combinations of models will address specific needs, saving time and resources. Some voices express concern over the dominance of a few large models, with figures like Jack Dorsey advocating for a diverse marketplace of algorithms. Philippe Krakowski of IPG also worries that relying on the same models might stifle creativity. SLMs offer the advantage of lower costs, both in development and operation. Microsoft’s Bubeck emphasizes that SLMs are “several orders of magnitude cheaper” than larger models. Typically, SLMs operate with around three to four billion parameters, making them feasible for deployment on devices like smartphones. However, smaller models come with trade-offs. Fewer parameters mean reduced capabilities. “You have to find the right balance between the intelligence that you need versus the cost,” Bubeck acknowledges. Salesforce’s Savarese views SLMs as a step towards a new form of AI, characterized by “agents” capable of performing specific tasks and executing plans autonomously. This vision of AI agents goes beyond today’s chatbots, which can generate travel itineraries but not take action on your behalf. Salesforce recently introduced a 1 billion-parameter SLM that reportedly outperforms some LLMs on targeted tasks. Salesforce CEO Mark Benioff celebrated this advancement, proclaiming, “On-device agentic AI is here!” 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 10, 2024in Generative AI

09Aug

Einstein Chatbot

Businesses have increasingly adopted “chatbots” to provide quick answers to customer queries outside regular business hours or to route customers to the appropriate department after answering preliminary questions. While these chatbots can be useful, they often fall short in delivering the same level of value as human interaction, sometimes leading to frustration. Today, chatbots are advancing significantly, with Salesforce’s Einstein Service Agent leading this evolution. This technology offers notable benefits but also presents challenges that businesses must address for effective implementation. Advantages of Einstein Service Agent Seamless Integration with Salesforce: Unlike standalone AI tools, Einstein Service Agent leverages comprehensive customer profiles, purchase histories, and previous interactions to offer personalized responses. Its integration within established Salesforce workflows allows for rapid deployment, reducing both time and cost associated with implementation. Experience has shown that selecting technologies with built-in CRM or ERP integration is a significant advantage over those requiring separate integration efforts. Built on Salesforce’s Trust Layer: Einstein Service Agent ensures secure handling of customer data, adhering to relevant regulations. This enhances trust among businesses and their customers, facilitating smoother adoption. GenAI Capabilities: The AI can manage complex, multi-step tasks like processing returns or refunds, and deliver tailored responses based on specific customer needs, enhancing the overall customer experience. Scalability Across Salesforce Clouds: Einstein Service Agent is adaptable to various business needs and can evolve as those needs change. Whether a company expands, introduces new services, or shifts its customer service strategy, the agent can be scaled and customized to maintain long-term value and utility. Challenges in Implementing AI Agents Data Quality and Integration: The effectiveness of AI tools relies heavily on the quality of the data they access. Incomplete, outdated, or poorly maintained data can lead to inaccurate or ineffective responses. To address this, businesses should prioritize data quality through regular audits and ensure comprehensive and up-to-date customer information. Change Management and Employee Training: The introduction of AI can lead to resistance from employees concerned about job displacement or unfamiliarity with new technology. Businesses should invest in change management strategies, including clear communication about AI as a complement to, not a replacement for, human agents. Training programs should focus on helping employees work alongside AI tools, enhancing skills where human judgment and empathy are crucial. Balancing Customer Service: Over-reliance on AI may diminish the personal touch essential in customer service. AI should handle straightforward and repetitive inquiries, while more complex or sensitive issues should be escalated to human agents who can provide personalized responses. Considerations for a Successful Deployment Customization and Flexibility: Tailoring the AI to fit unique processes and customer service requirements may require additional configuration or custom development to align with the company’s goals and service expectations. Ethical and Bias Concerns: AI systems can unintentionally perpetuate biases present in their training data, leading to unfair interactions. Businesses must actively identify and mitigate biases, ensuring that their AI operates fairly and equitably. This includes regularly reviewing training data for biases, implementing safeguards, and maintaining a commitment to ethical AI practices. Customer Acceptance and User Experience: Some customers may be hesitant to interact with AI or have negative perceptions of automated service. To improve acceptance, businesses should design user-friendly AI interactions, ensure transparency, and provide clear options for escalating issues to human agents. Einstein Chatbot Implementing AI agents like Salesforce’s Einstein Service Agent can significantly enhance customer service efficiency, personalization, and scalability. However, businesses must carefully navigate challenges related to data quality, change management, and maintaining trust. A thoughtful approach to AI deployment can transform customer service operations and drive business growth. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 9, 2024in Salesforce Einstein, Salesforce Marketing Automation

09Aug

Gearset Flow Navigator

Salesforce Flow enables teams to create complex business automations with clicks, not code. This approach allows admins to manage more of the business logic, reducing the dependency on developers and increasing project velocity, thus delivering value to the business more quickly. However, deploying Salesforce Flows often poses significant challenges. In this article, we’ll explore why deploying Flows can be tricky and how Gearset Flow Navigator simplifies the process with visual comparison tools for easy, declarative deployments. Join us to learn about tackling Flow deployments. Challenges in Deploying Salesforce Flows Deploying Salesforce Flows can be complex, particularly when using change sets, due to several issues: While tools using the Metadata API offer an alternative, they typically represent Flows as XML, which has its drawbacks: A New Solution for Salesforce Flow Deployments Despite Flow Builder’s user-friendly design for no-code admins, deployment can still be a challenging and frustrating experience. This affects the entire team and the broader business. Gearset’s new Flow Navigator addresses these pain points with industry-first functionality designed to simplify Flow deployments. Key Features of Flow Navigator Visualize Differences in Flows Between Environments:Gearset’s Flow Navigator offers a visual interface to compare source and target environments, such as Salesforce orgs or Git branches. The tool highlights new or changed elements in the Flow, making it easy to identify changes. A sidebar provides a detailed breakdown of changes, allowing users to quickly spot any unintended modifications. Detailed Breakdown of Changes:Users can drill down into specific Flow elements to review changes in a simple, tabular format. This ensures that only the intended changes are deployed. Switch Versions with a Click:The tool allows users to compare different versions of a Flow, which is particularly useful if the latest version includes in-progress work that shouldn’t be deployed. Automatic Flow Activation:Gearset can automatically activate Flows post-deployment, eliminating the need for manual activation steps and reducing the chance of errors. Flow Deployments that Resemble Flow Builder:Flow Navigator provides a more familiar and accessible deployment experience for no-code developers, avoiding the complexity of XML. Gearset offers a 30-day trial, allowing users to explore the platform and its Flow Navigator feature at no cost. By addressing these common challenges, Gearset’s Flow Navigator ensures smoother and more reliable Flow deployments, enhancing the overall efficiency and productivity of teams working with Salesforce. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 9, 2024in Salesforce

09Aug

Demandbase One for Sales iFrame

Understanding the Demandbase One for Sales iFrame in Salesforce The Demandbase One for Sales iFrame (formerly known as Sales Intelligence) allows sales teams to access deep, actionable insights directly within Salesforce. This feature provides account-level and people-level details, including engagement data, technographics, intent signals, and even relevant news, social media posts, and email communications. By offering this level of visibility, sales professionals can make informed decisions and take the most effective next steps on accounts. Key Points: Overview of the Demandbase One for Sales iFrame The iFrame is divided into several key sections: Account, People, Engagement, and Insights tabs. Each of these provides critical information to help you better understand and engage with the companies and people you’re researching. Account Tab People Tab Engagement Tab Final Notes: The Demandbase One for Sales iFrame is a powerful tool that provides a complete view of account activity, helping sales teams make informed decisions and drive results. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 9, 2024in Data

07Aug

SearchGPT and Knowledge Cutoff

Tackling the Knowledge Cutoff Challenge in Generative AI In the realm of generative AI, a significant hurdle has been the issue of knowledge cutoff—where a large language model (LLM) only has information up until a specific date. This was an early concern with OpenAI’s ChatGPT. For example, the GPT-4o model that currently powers ChatGPT has a knowledge cutoff in October 2023. The older GPT-4 model, on the other hand, had a cutoff in September 2021. Traditional search engines like Google, however, don’t face this limitation. Google continuously crawls the internet to keep its index up to date with the latest information. To address the knowledge cutoff issue in LLMs, multiple vendors, including OpenAI, are exploring search capabilities powered by generative AI (GenAI). Introducing SearchGPT: OpenAI’s GenAI Search Engine SearchGPT is OpenAI’s GenAI search engine, first announced on July 26, 2024. It aims to combine the strengths of a traditional search engine with the capabilities of GPT LLMs, eliminating the knowledge cutoff by drawing real-time data from the web. SearchGPT is currently a prototype, available to a limited group of test users, including individuals and publishers. OpenAI has invited publishers to ensure their content is accurately represented in search results. The service is positioned as a temporary offering to test and evaluate its performance. Once this evaluation phase is complete, OpenAI plans to integrate SearchGPT’s functionality directly into the ChatGPT interface. As of August 2024, OpenAI has not announced when SearchGPT will be generally available or integrated into the main ChatGPT experience. Key Features of SearchGPT SearchGPT offers several features designed to enhance the capabilities of ChatGPT: OpenAI’s Challenge to Google Search Google has long dominated the search engine landscape, a position that OpenAI aims to challenge with SearchGPT. Answers, Not Links Traditional search engines like Google act primarily as indexes, pointing users to other sources of information rather than directly providing answers. Google has introduced AI Overviews (formerly Search Generative Experience or SGE) to offer AI-generated summaries, but it still relies heavily on linking to third-party websites. SearchGPT aims to change this by providing direct answers to user queries, summarizing the source material instead of merely pointing to it. Contextual Continuity In contrast to Google’s point-in-time search queries, where each query is independent, SearchGPT strives to maintain context across multiple queries, offering a more seamless and coherent search experience. Search Accuracy Google Search often depends on keyword matching, which can require users to sift through several pages to find relevant information. SearchGPT aims to combine real-time data with an LLM to deliver more contextually accurate and relevant information. Ad-Free Experience SearchGPT offers an ad-free interface, providing a cleaner and more user-friendly experience compared to Google, which includes ads in its search results. AI-Powered Search Engine Comparison Here’s a comparison of the AI-powered search engines available today: Search Engine Platform Integration Publisher Collaboration Ads Cost SearchGPT (OpenAI) Standalone prototype Strong emphasis Ad-free Free (prototype stage) Google SGE Built on Google’s infrastructure SEO practices, content partnerships Includes ads Free Microsoft Bing AI/Copilot Built on Microsoft’s infrastructure SEO practices, content partnerships Includes ads Free Perplexity AI Standalone Basic source attribution Ad-free Free; $20/month for premium You.com AI assistant with various modes Basic source attribution Ad-free Free; premium tiers available Brave Search Independent search index Basic source attribution Ad-free Free 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 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

August 7, 2024in Generative AI

07Aug

Salesforce Query Builder

Salesforce Query Builder Effortlessly Build SOQL Queries for Salesforce Objects with Salesforce Query Builder. The Salesforce Query Builder is a powerful Chrome extension that simplifies the creation of SOQL (Salesforce Object Query Language) queries for administrators, developers, and power users. This tool addresses the common challenge of building complex queries directly within your Salesforce environment, eliminating the need for external tools. Key Features and Benefits Seamless Integration: The Query Builder works directly within your Salesforce tabs, streamlining your workflow by removing the need to switch between apps or browser windows. This integration ensures better productivity without disruption. User-Friendly Interface: Its intuitive design makes query building easy for users at any skill level. A step-by-step process walks you through selecting objects, fields, and applying filters, reducing the complexities of SOQL syntax. Dynamic Object and Field Selection: The extension automatically fetches and displays available Salesforce objects and fields, saving time and minimizing errors by using up-to-date schema information. Real-Time Query Generation: As you choose objects, fields, and filters, the extension generates the SOQL query in real-time. This live feedback helps you understand the structure of the query, allowing for quick adjustments. Secure Authentication: Using your existing Salesforce session, the Query Builder ensures your credentials remain secure. It doesn’t store or transmit sensitive information, maintaining the integrity of your data. Flexible Filtering: Easily add WHERE clauses to filter data based on specific criteria, making it simple to focus on the data subsets you need. Copy to Clipboard: With one click, copy the generated SOQL query to your clipboard for easy use in other tools, development environments, or for sharing with teammates. Field Search: For objects with many fields, the search function helps you quickly locate the fields you need, reducing time spent scrolling. Lightweight and Fast: As a browser extension, the Query Builder is lightweight, requiring no installation on your Salesforce instance, ensuring fast performance without impacting your org. Cross-Domain Support: The tool supports multiple Salesforce domains (salesforce.com, force.com, cloudforce.com), providing a consistent experience across different environments. Why You Should Install It Time-Saving: The Query Builder dramatically reduces the time spent constructing SOQL queries, especially for complex objects or unfamiliar schemas. Error Reduction: By providing a visual interface, the tool minimizes syntax errors that can occur when manually writing SOQL queries. Learning Tool: Ideal for those new to SOQL, the Query Builder helps users understand query structure and best practices through its interactive design. Increased Productivity: With seamless Salesforce integration, you can generate queries quickly without disrupting your workflow. Accessibility: The tool empowers users who may not be comfortable writing SOQL manually, making advanced querying capabilities accessible to a wider range of Salesforce users. Consistency: It encourages consistent query-building practices across teams, making collaboration and sharing of queries easier. No Setup Required: As a browser extension, it requires no changes to your Salesforce org, making it perfect for admins or developers working across multiple orgs or with limited customization permissions. By installing the Salesforce Query Builder, you gain a valuable tool for your daily Salesforce tasks. Whether you’re a developer needing to prototype queries, an admin exploring data relationships, or a business analyst needing custom views, this tool simplifies interacting with your Salesforce data. With its combination of ease of use, security, and powerful features, it’s an essential addition to any Salesforce professional’s toolkit. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 7, 2024in Data, Salesforce

06Aug

Understanding AI Agents

Understanding AI Agents: A Comprehensive Guide Artificial Intelligence (AI) has come a long way, offering systems that automate tasks and provide intelligent, responsive solutions. One key concept within AI is the AI agent—an autonomous system capable of perceiving its environment and taking actions to achieve specific goals. This guide explores AI agents, their types, working mechanisms, and how to build them using platforms like Microsoft Autogen and Google Vertex AI Agent Builder. It also highlights how companies like LeewayHertz and Markovate can assist in the development of AI agents. What is an AI Agent? AI agents are systems designed to interact with their environment autonomously. They process inputs, make decisions, and execute actions based on predefined rules or learned experiences. These agents range from simple rule-based systems to complex machine learning models that adapt over time. Types of AI Agents AI agents can be classified based on complexity and functionality: How AI Agents Work The working mechanism of an AI agent involves four key components: Architectural Blocks of an Autonomous AI Agent An autonomous AI agent typically includes: Building an AI Agent: The Basics Building an AI agent involves several essential steps: Microsoft Autogen: A Platform Overview Microsoft Autogen is a powerful tool for building AI agents, offering a range of features that simplify the development, training, and deployment process. Its user-friendly interface allows developers to create custom agents quickly. Key Steps to Building AI Agents with Autogen: Benefits of Autogen: Vertex AI Agent Builder: Enabling No-Code AI Development Google’s Vertex AI Agent Builder simplifies AI agent development through a no-code platform, making it accessible to users without extensive programming experience. Its drag-and-drop functionality allows for quick and efficient AI agent creation. Key Features of Vertex AI Agent Builder: Conclusion AI agents play a critical role in automating decision-making and performing tasks independently. Platforms like Microsoft Autogen and Google Vertex AI Agent Builder make the development of these agents more accessible, providing powerful tools for both novice and experienced developers. By leveraging these technologies and partnering with companies like LeewayHertz and Markovate, businesses can build custom AI agents that enhance automation, decision-making, and operational efficiency. Whether you’re starting from scratch or looking to integrate AI capabilities into your existing systems, the right tools can make the process seamless and effective. How do you think these tools stack up next to Salesforce AI Agents? Comment below. Content updated October 2024. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 6, 2024in AI Tools, Artificial Intelligence, Data, Google

02Aug

Role of Small Language Models

The Role of Small Language Models (SLMs) in AI While much attention is often given to the capabilities of Large Language Models (LLMs), Small Language Models (SLMs) play a vital role in the AI landscape. Role of Small Language Models. Large vs. Small Language Models LLMs, like GPT-4, excel at managing complex tasks and providing sophisticated responses. However, their substantial computational and energy requirements can make them impractical for smaller organizations and devices with limited processing power. In contrast, SLMs offer a more feasible solution. Designed to be lightweight and resource-efficient, SLMs are ideal for applications operating in constrained computational environments. Their reduced resource demands make them easier and quicker to deploy, while also simplifying maintenance. What are Small Language Models? Small Language Models (SLMs) are neural networks engineered to generate natural language text. The term “small” refers not only to the model’s physical size but also to its parameter count, neural architecture, and the volume of data used during training. Parameters are numeric values that guide a model’s interpretation of inputs and output generation. Models with fewer parameters are inherently simpler, requiring less training data and computational power. Generally, models with fewer than 100 million parameters are classified as small, though some experts consider models with as few as 1 million to 10 million parameters to be small in comparison to today’s large models, which can have hundreds of billions of parameters. How Small Language Models Work SLMs achieve efficiency and effectiveness with a reduced parameter count, typically ranging from tens to hundreds of millions, as opposed to the billions seen in larger models. This design choice enhances computational efficiency and task-specific performance while maintaining strong language comprehension and generation capabilities. Techniques such as model compression, knowledge distillation, and transfer learning are critical for optimizing SLMs. These methods enable SLMs to encapsulate the broad understanding capabilities of larger models into a more concentrated, domain-specific toolset, facilitating precise and effective applications while preserving high performance. Advantages of Small Language Models Applications of Small Language Models Role of Small Language Models is lengthy. SLMs have seen increased adoption due to their ability to produce contextually coherent responses across various applications: Small Language Models vs. Large Language Models Feature LLMs SLMs Training Dataset Broad, diverse internet data Focused, domain-specific data Parameter Count Billions Tens to hundreds of millions Computational Demand High Low Cost Expensive Cost-effective Customization Limited, general-purpose High, tailored to specific needs Latency Higher Lower Security Risk of data exposure through APIs Lower risk, often not open source Maintenance Complex Easier Deployment Requires substantial infrastructure Suitable for limited hardware environments Application Broad, including complex tasks Specific, domain-focused tasks Accuracy in Specific Domains Potentially less accurate due to general training High accuracy with domain-specific training Real-time Application Less ideal due to latency Ideal due to low latency Bias and Errors Higher risk of biases and factual errors Reduced risk due to focused training Development Cycles Slower Faster Conclusion The role of Small Language Models (SLMs) is increasingly significant as they offer a practical and efficient alternative to larger models. By focusing on specific needs and operating within constrained environments, SLMs provide targeted precision, cost savings, improved security, and quick responsiveness. As industries continue to integrate AI solutions, the tailored capabilities of SLMs are set to drive innovation and efficiency across various domains. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 2, 2024in Salesforce

01Aug

Close Date Predictions

Important Update: Close Date Predictions Feature Retiring in Spring ’25 The Close Date Predictions feature, currently available in Pipeline Inspection, is designed to forecast when an opportunity with a close date in the current month may not close on time. However, Salesforce will be retiring this feature with the Spring ’25 release. What’s Changing? With the retirement of Close Date Predictions, users will no longer receive alerts about opportunities that are unlikely to close within the current month. Previously, these predictions appeared as an icon on the Close Date field or within the Insights tab of the side panel. What Should You Do? To continue receiving valuable predictions about opportunity closures, we recommend transitioning to Einstein Opportunity Scoring. This feature, available with your Sales Cloud license, provides more advanced predictions on an opportunity’s likelihood of closing and highlights the top reasons behind the prediction. Here’s how you can make the switch: For further assistance, you can open a support case via Salesforce Help. To learn more about Salesforce’s approach to product and feature retirements, please review our Product & Feature Retirement Philosophy. Like1 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 1, 2024in Salesforce

30Jul

New Service Cloud Tools

Salesforce has unveiled new out-of-the-box service components, an automation tool, and a new app for Service Cloud customers, designed to help agents resolve customer cases faster and enable companies to scale their support operations efficiently. New Service Cloud Tools are here. Why It Matters: With 69% of agents reporting that balancing speed and quality is a challenge, and as the volume and complexity of cases increase, there is a growing need for tools that enhance efficiency without compromising service quality. Salesforce Service Cloud: Deliver Value Across Every Customer Touchpoint with Service Cloud Built on the Einstein 1 Platform. New Tools and Features: Service Cloud customers now have access to a suite of efficiency tools aimed at automating processes and identifying the best product capabilities to enhance service delivery. These new features allow customers to maximize their Service Cloud investment and improve their return on investment. Salesforce Perspective: Kishan Chetan, EVP & GM of Service Cloud, emphasized that the new efficiency tools help companies of all sizes increase service team productivity and better serve their customers. Industry Reaction: Rebecca Wettemann, CEO & Principal Analyst at Valoir, noted that these innovations offer service teams quick wins, enhancing operational efficiency and maximizing technology investments. Fast Facts: 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

July 30, 2024in Salesforce Service Cloud

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