State Archives - gettectonic.com
deepseek deep dive

Deep Dive into DeepSeek

DeepSeek: The AI Lab Turned Controversial Global Player You know we have to write about anything AI related that is making waves. And DeepSeek is definitely doing that. On April 14, 2023, High-Flyer announced the launch of a dedicated artificial general intelligence (AGI) lab, focused on AI research independent of its financial business. This initiative led to the incorporation of DeepSeek on July 17, 2023, with High-Flyer as its primary investor and backer. DeepSeek’s Breakthrough and the Debate on AI Development DeepSeek quickly gained attention in the AI world, with former India IT Minister Rajeev Chandrasekhar highlighting its impact. He stated that DeepSeek’s success reinforced the idea that better datasets and algorithms—rather than increased compute capacity—are the key to advancing AI capabilities. National Security Concerns: Hidden Risks in DeepSeek’s Code Despite its technological achievements, DeepSeek is now at the center of global controversy. Cybersecurity experts have raised serious concerns about the tool’s potential data-sharing links to the Chinese government. According to a report by ABC News, DeepSeek contains hidden code capable of transmitting user data directly to China. Ivan Tsarynny, CEO of the Ontario-based cybersecurity firm Feroot Security, conducted an analysis of DeepSeek’s code and discovered an embedded function that connects user data to CMPassport.com—the online registry for China Mobile, a state-owned telecommunications company. Key Concerns Raised by Cybersecurity Experts: Global Backlash and Regulatory Actions DeepSeek’s security concerns have sparked international scrutiny. Several governments and organizations have moved swiftly to restrict or ban its use: John Cohen, a former acting Undersecretary for Intelligence and Analysis at the U.S. Department of Homeland Security, described DeepSeek as one of the most blatant cases of suspected Chinese surveillance. He emphasized that it joins a growing list of Chinese tech firms identified as potential national security threats. The Future of DeepSeek DeepSeek’s rapid rise and subsequent scrutiny reflect the broader tensions between AI innovation and national security. As regulators worldwide assess its risks, the company’s future remains uncertain—caught between technological breakthroughs and growing geopolitical concerns. 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 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

Read More
salesforce agentforce ai powered agentic agents

Agentforce 2.0

Salesforce, the leading CRM provider, is set to launch Agentforce 2.0 in February 2025—an AI-powered toolset designed as a “digital labor platform for building a limitless workforce for the enterprise.” Agentforce 2.0 is a comprehensive AI system that enhances teams with autonomous AI agents embedded in everyday workflows. Among its key offerings are AI-driven agents for Sales Development and Sales Coaching, with pricing starting at $2 per conversation. With this release, Salesforce introduces a library of pre-built skills and workflow integrations, enabling rapid customization and seamless deployment within Slack. Marc Benioff, Chair and CEO of Salesforce, stated, “We’re seamlessly bringing together AI, data, apps, and automation with humans to reshape how work gets done. Agentforce 2.0 cements our position as the leader in digital labor solutions, allowing any company to build a limitless workforce that can truly transform their business.” Agentforce 2.0 includes pre-built AI skills across CRM, Slack, Tableau, and partner-developed integrations via the AppExchange. Customers can further extend Agentforce’s capabilities using MuleSoft, enabling low-code workflows that integrate with any system. The release also introduces an enhanced Agent Builder, which interprets natural language instructions—such as “Onboard New Product Managers”—to automatically generate new AI agents. These agents combine pre-made skills with custom logic built directly in Salesforce, offering unmatched flexibility and efficiency. Additionally, Agentforce 2.0 features Tableau Skills for advanced analytics and insights, further empowering businesses to harness AI-driven decision-making. 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

Read More
AI Market Heat

AI Market Heat

Alibaba Feels the Heat as DeepSeek Shakes Up AI Market Chinese tech giant Alibaba is under pressure following the release of an AI model by Chinese startup DeepSeek that has sparked a major reaction in the West. DeepSeek claims to have trained its model—comparable to advanced Western AI—at a fraction of the cost and with significantly fewer AI chips. In response, Alibaba launched Qwen 2.5-Max, its latest AI language model, on Tuesday—just one day before the Lunar New Year, when much of China’s economy typically slows down for a 15-day holiday. A Closer Look at Qwen 2.5-Max Qwen 2.5-Max is a Mixture of Experts (MoE) model trained on 20 trillion tokens. It has undergone supervised fine-tuning and reinforcement learning from human feedback to enhance its capabilities. MoE models function by using multiple specialized “minds,” each focused on a particular domain. When a query is received, the model dynamically routes it to the most relevant expert, improving efficiency. For instance, a coding-related question would be processed by the model’s coding expert. This MoE approach reduces computational requirements, making training more cost-effective and faster. Other AI vendors, such as France-based Mistral AI, have also embraced this technique. DeepSeek’s Disruptive Impact While Qwen 2.5-Max is not a direct competitor to DeepSeek’s R1 model—the release of which triggered a global selloff in AI stocks—it is similar to DeepSeek-V3, another MoE-based model launched earlier this month. Alibaba’s swift release underscores the competitive threat posed by DeepSeek. As the world’s fourth-largest public cloud vendor, Alibaba, along with other Chinese tech giants, has been forced to respond aggressively. In the wake of DeepSeek R1’s debut, ByteDance—the owner of TikTok—also rushed to update its AI offerings. DeepSeek has already disrupted the AI market by significantly undercutting costs. In 2023, the startup introduced V2 at just 1 yuan ($0.14) per million tokens, prompting a price war. By comparison, OpenAI’s GPT-4 starts at $10 per million tokens—a staggering difference. The timing of Alibaba and ByteDance’s latest releases suggests that DeepSeek has accelerated product development cycles across the industry, forcing competitors to move faster than planned. “Alibaba’s cloud unit has been rapidly advancing its AI technology, but the pressure from DeepSeek’s rise is immense,” said Lisa Martin, an analyst at Futurum Group. A Shifting AI Landscape DeepSeek’s rapid growth reflects a broader shift in the AI market—one driven by leaner, more powerful models that challenge conventional approaches. “The drive to build more efficient models continues,” said Gartner analyst Arun Chandrasekaran. “We’re seeing significant innovation in algorithm design and software optimization, allowing AI to run on constrained infrastructure while being more cost-competitive.” This evolution is not happening in isolation. “AI companies are learning from one another, continuously reverse-engineering techniques to create better, cheaper, and more efficient models,” Chandrasekaran added. The AI industry’s perception of cost and scalability has fundamentally changed. Sam Altman, CEO of OpenAI, previously estimated that training GPT-4 cost over $100 million—but DeepSeek claims it built R1 for just $6 million. “We’ve spent years refining how transformers function, and the efficiency gains we’re seeing now are the result,” said Omdia analyst Bradley Shimmin. “These advances challenge the idea that massive computing power is required to develop state-of-the-art AI.” Competition and Data Controversies DeepSeek’s success showcases the increasing speed at which AI innovation is happening. Its distillation technique, which trains smaller models using insights from larger ones, has allowed it to create powerful AI while keeping costs low. However, OpenAI and Microsoft are now investigating whether DeepSeek improperly used their models’ data to train its own AI—a claim that, if true, could escalate into a major dispute. Ironically, OpenAI itself has faced similar accusations, leading some enterprises to prefer using its models through Microsoft Azure, which offers additional compliance safeguards. “The future of AI development will require stronger security layers,” Shimmin noted. “Enterprises need assurances that using models like Qwen 2.5 or DeepSeek R1 won’t expose their data.” For businesses evaluating AI models, licensing terms matter. Alibaba’s Qwen 2.5 series operates under an Apache 2.0 license, while DeepSeek uses an MIT license—both highly permissive, allowing companies to scrutinize the underlying code and ensure compliance. “These licenses give businesses transparency,” Shimmin explained. “You can vet the code itself, not just the weights, to mitigate privacy and security risks.” The Road Ahead The AI arms race between DeepSeek, Alibaba, OpenAI, and other players is just beginning. As vendors push the limits of efficiency and affordability, competition will likely drive further breakthroughs—and potentially reshape the AI landscape faster than anyone anticipated. 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

Read More
operational customer profiles

Operational Customer Profiles

What is an operational customer profile? An operational customer profile is a detailed representation of a customer’s interactions with a company. Operational Customer Profile: The Key to Your Marketing Puzzle An illustration of a man on a pink background with puzzle pieces symbolizing how an operational customer profile brings fragmented customer data together. Marketers are constantly challenged to unify fragmented customer data and make it actionable. Enter the operational customer profile—the solution to connecting the dots and revealing the bigger picture. Seeing the Bigger Picture Without Overhauling Your Strategy Picture this: You’re trying to understand your customer’s preferences. You have some demographic details, a few website interactions, and maybe a record of past purchases. But when you attempt to piece it all together, the view remains blurry. You’re close to understanding your customer, yet the fragmented data leaves you guessing. This challenge isn’t unique. According to Salesforce’s State of Marketing report, only 31% of marketers are fully satisfied with their ability to unify customer data sources. Even fewer—just 32%—are satisfied with how effectively they use that data to create personalized customer experiences. The culprit? Data fragmentation. Customer information lives across disconnected systems, making it nearly impossible to gain a complete view of each customer. What Is an Operational Customer Profile? An operational customer profile serves as a comprehensive snapshot of a customer’s interactions with your company. It includes critical data like: This consolidated profile empowers marketers to create personalized, relevant experiences. For example: The Pain Points of Fragmented Customer Data Fragmented customer data lives in silos: Each platform captures different touchpoints, but they rarely communicate seamlessly. Manually combining this data is not only tedious but also prone to errors, leading to incomplete profiles. This inefficiency robs marketers of the insights they need to: Without a unified view, marketing efforts lose their impact, becoming less targeted and less effective. The Solution to Fragmentation An operational customer profile is the key to overcoming these challenges. By unifying data and making it actionable, marketers can unlock valuable insights and build stronger connections with their customers—no major marketing overhaul required. It’s time to let the operational customer profile complete your marketing plan. 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

Read More
Python-Based Reasoning

Python-Based Reasoning

Introducing a Python-Based Reasoning Engine for Deterministic AI As the demand for deterministic systems grows reviving foundational ideas for the age of large language models (LLMs) is here. The Challenge One of the critical issues with modern AI systems is establishing constraints around how they validate and reason about incoming data. As we increasingly rely on stochastic LLMs to process unstructured data, enforcing rules and guardrails becomes vital for ensuring reliability and consistency. The Solution Thus a company has developed a Python-based reasoning and validation framework inspired by Pydantic, designed to empower developers and non-technical domain experts to create sophisticated rule engines. The system is: By transforming Standard Operating Procedures (SOPs) and business guardrails into enforceable code, this symbolic reasoning framework addresses the need for structured, interpretable, and reliable AI systems. Key Features System Architecture The framework includes five core components: Types of Engines Case Studies 1. Validation Engine: Mining Company Compliance A mining company needed to validate employee qualifications against region-specific requirements. The system was configured to check rules such as minimum age and required certifications for specific roles. Input Example:Employee data and validation rules were modeled as JSON: jsonCopy code{ “employees”: [ { “name”: “Sarah”, “age”: 25, “documents”: [{ “type”: “safe_handling_at_work” }] }, { “name”: “John”, “age”: 17, “documents”: [{ “type”: “heavy_lifting” }] } ], “rules”: [ { “type”: “min_age”, “parameters”: { “min_age”: 18 } } ] } Output:Violations, such as “Minimum age must be 18,” were flagged immediately, enabling quick remediation. 2. Reasoning Engine: Solving the River Crossing Puzzle To showcase its capabilities, we modeled the classic river crossing puzzle, where a farmer must transport a wolf, a goat, and a cabbage across a river without leaving incompatible items together. Steps Taken: Enhanced Scenario:Adding a new rule—“Wolf cannot be left with a chicken”—created an unsolvable scenario. By introducing a compensatory rule, “Farmer can carry two items at once,” the system adapted and solved the puzzle with fewer moves. Developer Insights The system supports rapid iteration and debugging. For example, adding rules is as simple as defining Python classes: pythonCopy codeclass GoatCabbageRule(Rule): def evaluate(self, state): return not (state.goat == state.cabbage and state.farmer != state.goat) def get_description(self): return “Goat cannot be left alone with cabbage” Real-World Impact This framework accelerates development by enabling non-technical stakeholders to contribute to rule creation through natural language, with developers approving and implementing these rules. This process reduces development time by up to 5x and adapts seamlessly to varied use cases, from logistics to compliance. 🔔🔔 Follow us on LinkedIn 🔔🔔 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

Read More
salesforce agentforce rapid deployment

Agentforce: Your Partner in Seamless Customer Experiences

Building Deeper Customer Relationships with Agentforce In today’s competitive landscape, building meaningful relationships with customers is no longer optional—it’s essential. Customers expect brands to know them, anticipate their needs, and deliver seamless, personalized experiences across every interaction. While data and AI have the potential to help marketers achieve these goals, many organizations struggle to realize their full potential. In fact, only 32% of marketers report being completely satisfied with how they use customer data to create relevant experiences, according to our State of Marketing report. So, how can marketers close this gap? Meet Agentforce—a proactive, autonomous application designed to provide specialized, always-on support for employees and customers alike. With Agentforce, marketers can strengthen relationships through personalized conversations, proactive engagement, and seamless customer experiences. 1. Automate Personalized, Two-Way Conversations on WhatsApp Interacting one-on-one with every customer responding to a promotional campaign on WhatsApp has historically been a challenge. Limited chatbot capabilities or a complete lack of response options often resulted in missed opportunities. Agentforce changes the game by introducing a customer-facing AI agent that acts as a personal concierge directly within WhatsApp. Imagine a customer receiving an exclusive offer for a product they’ve shown interest in. They reply with a question, and the agent instantly provides tailored product recommendations, current promotions, or details about complementary products. If the customer decides to make a purchase, the agent guides them through the entire checkout process—from completing the transaction to sending real-time order updates. For more complex needs, the agent seamlessly transfers the conversation to a human service representative, ensuring continuity. Why it matters: This approach not only increases conversions but also builds customer satisfaction and loyalty through timely, relevant responses. By reducing the workload on support teams, Agentforce delivers a consistent brand experience that feels personal and effortless. 2. Create Personalized Agendas for Event Attendees Events are powerful tools for fostering customer connections and delivering value. However, ensuring attendees find the most relevant sessions and resources can be daunting. Self-guided experiences often result in missed opportunities or abandoned registrations. Agentforce for Marketing solves this by providing personalized, 1:1 assistance to every visitor on your event website. The agent recommends sessions based on visitor interests and helps attendees create personalized agendas. Leveraging past attendance and engagement data, Agentforce curates agendas tailored to each attendee’s priorities, from keynote presentations to breakout sessions. For repeat attendees, it suggests new content based on their history, completing the registration process with their customized agenda. Why it matters: Personalized agendas enhance the event experience, leading to higher satisfaction and loyalty. By making the registration journey seamless, Agentforce reduces bounce rates and builds long-term engagement with your events. 3. Capture and Qualify Leads Effortlessly on Your Website Visitors often abandon self-guided website experiences before converting into leads. With Agentforce, you can proactively engage them by providing tailored product recommendations, exclusive content offers, and opportunities to share contact information. Depending on visitor behavior, the agent might suggest gated assets like case studies or demo videos—or even register them for an event or webinar. For highly engaged prospects, the agent can schedule follow-up meetings with sales reps, ensuring visitors receive immediate value without friction. Why it matters: Automated lead capture accelerates qualification and increases conversions. By guiding visitors to the right solutions and reducing friction, Agentforce nurtures leads naturally, creating higher-quality opportunities and loyal customers. 4. Improve Customer Journeys with Intelligent Reprioritization Balancing customer engagement with respect for their communication preferences is critical. Before customers hit their communication limit, Agentforce can dynamically reprioritize their journey based on both their interests and your business goals. For example, if a customer is close to their communication cap, the agent can prioritize sending a VIP event invitation or product announcement over less relevant messages. This ensures high-value content is delivered at the right time, without overwhelming the customer. Why it matters: Intelligent reprioritization improves conversion rates, reduces unsubscribe rates, and strengthens customer relationships. By ensuring every interaction is timely and relevant, Agentforce helps keep customers engaged without feeling inundated. 5. Reduce Churn with Proactive, Personalized Promotions Retaining customers is just as important as acquiring new ones. Agentforce can identify at-risk customers using churn indicators—such as low engagement or declining purchase frequency—and automatically send tailored promotions. For instance, if a customer’s churn score nears a threshold, the agent can proactively offer a loyalty discount or renewal incentive. It can guide the customer through the redemption process, rekindling their interest before they decide to leave. Why it matters: Proactive retention strategies powered by AI increase customer lifetime value, reduce churn, and foster loyalty. With minimal effort, Agentforce ensures that marketers stay connected to customers who might otherwise disengage. Agentforce: Your Partner in Seamless Customer Experiences More than just an automation tool, Agentforce is an essential partner for delivering the personalized experiences your customers expect. By implementing these use cases, marketers can: Ready to take your marketing strategy to the next level? With Agentforce, meaningful customer relationships are within reach. 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

Read More
Statement Accuracy Prediction based on Language Model Activations

Statement Accuracy Prediction based on Language Model Activations

When users first began interacting with ChatGPT, they noticed an intriguing behavior: the model would often reverse its stance when told it was wrong. This raised concerns about the reliability of its outputs. How can users trust a system that appears to contradict itself? Recent research has revealed that large language models (LLMs) not only generate inaccurate information (often referred to as “hallucinations”) but are also aware of their inaccuracies. Despite this awareness, these models proceed to present their responses confidently. Unveiling LLM Awareness of Hallucinations Researchers discovered this phenomenon by analyzing the internal mechanisms of LLMs. Whenever an LLM generates a response, it transforms the input query into a numerical representation and performs a series of computations before producing the output. At intermediate stages, these numerical representations are called “activations.” These activations contain significantly more information than what is reflected in the final output. By scrutinizing these activations, researchers can identify whether the LLM “knows” its response is inaccurate. A technique called SAPLMA (Statement Accuracy Prediction based on Language Model Activations) has been developed to explore this capability. SAPLMA examines the internal activations of LLMs to predict whether their outputs are truthful or not. Why Do Hallucinations Occur? LLMs function as next-word prediction models. Each word is selected based on its likelihood given the preceding words. For example, starting with “I ate,” the model might predict the next words as follows: The issue arises when earlier predictions constrain subsequent outputs. Once the model commits to a word, it cannot go back to revise its earlier choice. For instance: In another case: This mechanism reveals how the constraints of next-word prediction can lead to hallucinations, even when the model “knows” it is generating an incorrect response. Detecting Inaccuracies with SAPLMA To investigate whether an LLM recognizes its own inaccuracies, researchers developed the SAPLMA method. Here’s how it works: The classifier itself is a simple neural network with three dense layers, culminating in a binary output that predicts the truthfulness of the statement. Results and Insights The SAPLMA method achieved an accuracy of 60–80%, depending on the topic. While this is a promising result, it is not perfect and has notable limitations. For example: However, if LLMs can learn to detect inaccuracies during the generation process, they could potentially refine their outputs in real time, reducing hallucinations and improving reliability. The Future of Error Mitigation in LLMs The SAPLMA method represents a step forward in understanding and mitigating LLM errors. Accurate classification of inaccuracies could pave the way for models that can self-correct and produce more reliable outputs. While the current limitations are significant, ongoing research into these methods could lead to substantial improvements in LLM performance. By combining techniques like SAPLMA with advancements in LLM architecture, researchers aim to build models that are not only aware of their errors but capable of addressing them dynamically, enhancing both the accuracy and trustworthiness of AI systems. 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

Read More
Payload and REDA One Launch REDA Pay Embedded Payment Solution Payload has unveiled REDA Pay, an embedded payment solution tailored for Salesforce users. This new offering stems from a strategic partnership between Payload and REDA One, aiming to streamline and enhance payment processes for businesses. “REDA One needed a payment solution that could be deployed quickly, efficiently, and effectively to expand their platform’s capabilities,” said Zach Jacob, Vice President of Partnerships at Payload. “Payload’s robust APIs proved to be the perfect match, seamlessly integrating with REDA One’s existing infrastructure." Jacob emphasized the ease and speed of implementation, noting, “In no time, REDA One was able to activate REDA Pay, demonstrating the simplicity and adaptability of our payment tools. This collaboration underscores Payload’s commitment to delivering powerful, straightforward solutions that enable SaaS platforms like REDA One to provide cutting-edge payment experiences without unnecessary complexity.” The Growing Role of Embedded Payments Highlighting the importance of embedded payments, Jacob stated, “In today’s digital era, embedded payments are not just a feature—they are an essential element of an exceptional user experience.” With REDA Pay, Salesforce users can now process payments seamlessly within their workflows, reducing friction and enhancing efficiency. Jacob outlined three core advantages of REDA Pay: Intuitive Experience: “Users can send and receive payments directly within the platform, boosting productivity and convenience.” Security: “Built to the highest industry standards, our solutions protect every transaction with advanced security measures.” Scalability and Flexibility: “As REDA One’s customers grow, REDA Pay will evolve to meet their changing requirements.” Elevating SaaS Platforms Jacob also highlighted the broader impact of this launch for SaaS providers: “REDA Pay exemplifies how effortlessly SaaS platforms can elevate their offerings by integrating embedded payment solutions. Payload’s APIs are designed for seamless integration, enabling platforms to quickly add enhanced payment capabilities for their users. This not only improves the user experience but also unlocks new opportunities for growth and revenue.” With REDA Pay, Payload and REDA One have set a new standard for embedded payment solutions in Salesforce, driving innovation and efficiency for businesses in the digital age.

Payload and REDA

Payload and REDA One Launch REDA Pay Embedded Payment Solution Payload has unveiled REDA Pay, an embedded payment solution tailored for Salesforce users. This new offering stems from a strategic partnership between Payload and REDA One, aiming to streamline and enhance payment processes for businesses. “REDA One needed a payment solution that could be deployed quickly, efficiently, and effectively to expand their platform’s capabilities,” said Zach Jacob, Vice President of Partnerships at Payload. “Payload’s robust APIs proved to be the perfect match, seamlessly integrating with REDA One’s existing infrastructure.” Jacob emphasized the ease and speed of implementation, noting, “In no time, REDA One was able to activate REDA Pay, demonstrating the simplicity and adaptability of our payment tools. This collaboration underscores Payload’s commitment to delivering powerful, straightforward solutions that enable SaaS platforms like REDA One to provide cutting-edge payment experiences without unnecessary complexity.” The Growing Role of Embedded Payments Highlighting the importance of embedded payments, Jacob stated, “In today’s digital era, embedded payments are not just a feature—they are an essential element of an exceptional user experience.” With REDA Pay, Salesforce users can now process payments seamlessly within their workflows, reducing friction and enhancing efficiency. Jacob outlined three core advantages of REDA Pay: Elevating SaaS Platforms Jacob also highlighted the broader impact of this launch for SaaS providers:“REDA Pay exemplifies how effortlessly SaaS platforms can elevate their offerings by integrating embedded payment solutions. Payload’s APIs are designed for seamless integration, enabling platforms to quickly add enhanced payment capabilities for their users. This not only improves the user experience but also unlocks new opportunities for growth and revenue.” With REDA Pay, Payload and REDA One have set a new standard for embedded payment solutions in Salesforce, driving innovation and efficiency for businesses in the digital age. 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

Read More

Autonomy, Architecture, and Action

Redefining AI Agents: Autonomy, Architecture, and Action AI agents are reshaping how technology interacts with us and executes tasks. Their mission? To reason, plan, and act independently—following instructions, making autonomous decisions, and completing actions, often without user involvement. These agents adapt to new information, adjust in real time, and pursue their objectives autonomously. This evolution in agentic AI is revolutionizing how goals are accomplished, ushering in a future of semi-autonomous technology. At their foundation, AI agents rely on one or more large language models (LLMs). However, designing agents is far more intricate than building chatbots or generative assistants. While traditional AI applications often depend on user-driven inputs—such as prompt engineering or active supervision—agents operate autonomously. Core Principles of Agentic AI Architectures To enable autonomous functionality, agentic AI systems must incorporate: Essential Infrastructure for AI Agents Building and deploying agentic AI systems requires robust software infrastructure that supports: Agent Development Made Easier with Langflow and Astra DB Langflow simplifies the development of agentic applications with its visual IDE. It integrates with Astra DB, which combines vector and graph capabilities for ultra-low latency data access. This synergy accelerates development by enabling: Transforming Autonomy into Action Agentic AI is fundamentally changing how tasks are executed by empowering systems to act autonomously. By leveraging platforms like Astra DB and Langflow, organizations can simplify agent design and deploy scalable, effective AI applications. Start building the next generation of AI-powered autonomy today. 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

Read More
Apple's Privacy Changes: A Call for Email Marketing Innovation

Liar Liar Apple on Fire

Apple Developing Update After AI System Generates Inaccurate News Summaries Apple is working on a software update to address inaccuracies generated by its Apple Intelligence system after multiple instances of false news summaries were reported. The BBC first alerted Apple in mid-December to significant errors in the system, including a fabricated summary that falsely attributed a statement to BBC News. The summary suggested Luigi Mangione, accused of killing United Healthcare CEO Brian Thompson, had shot himself, a claim entirely unsubstantiated. Other publishers, such as ProPublica, also raised concerns about Apple Intelligence producing misleading summaries. While Apple did not respond immediately to the BBC’s December report, it issued a statement after pressure mounted from groups like the National Union of Journalists and Reporters Without Borders, both of which called for the removal of Apple Intelligence. Apple assured stakeholders it is working to refine the technology. A Widespread AI Issue: Hallucinations Apple joins the ranks of other AI vendors struggling with generative AI hallucinations—instances where AI produces false or misleading information. In October 2024, Perplexity AI faced a lawsuit from Dow Jones & Co. and the New York Post over fabricated news content attributed to their publications. Similarly, Google had to improve its AI summaries after providing users with inaccurate information. On January 16, Apple temporarily disabled AI-generated summaries for news apps on iPhone, iPad, and Mac devices. The Core Problem: AI Hallucination Chirag Shah, a professor of Information Science at the University of Washington, emphasized that hallucination is inherent to the way large language models (LLMs) function. “The nature of AI models is to generate, synthesize, and summarize, which makes them prone to mistakes,” Shah explained. “This isn’t something you can debug easily—it’s intrinsic to how LLMs operate.” While Apple plans to introduce an update that clearly labels summaries as AI-generated, Shah believes this measure falls short. “Most people don’t understand how these headlines or summaries are created. The responsible approach is to pause the technology until it’s better understood and mitigation strategies are in place,” he said. Legal and Brand Implications for Apple The hallucinated summaries pose significant reputational and legal risks for Apple, according to Michael Bennett, an AI adviser at Northeastern University. Before launching Apple Intelligence, the company was perceived as lagging in the AI race. The release of this system was intended to position Apple as a leader. Instead, the inaccuracies have damaged its credibility. “This type of hallucinated summarization is both an embarrassment and a serious legal liability,” Bennett said. “These errors could form the basis for defamation claims, as Apple Intelligence misattributes false information to reputable news sources.” Bennett criticized Apple’s seemingly minimal response. “It’s surprising how casual Apple’s reaction has been. This is a major issue for their brand and could expose them to significant legal consequences,” he added. Opportunity for Publishers The incident highlights the need for publishers to protect their interests when partnering with AI vendors like Apple and Google. Publishers should demand stronger safeguards to prevent false attributions and negotiate new contractual clauses to minimize brand risk. “This is an opportunity for publishers to lead the charge, pushing AI companies to refine their models or stop attributing false summaries to news sources,” Bennett said. He suggested legal action as a potential recourse if vendors fail to address these issues. Potential Regulatory Action The Federal Trade Commission (FTC) may also scrutinize the issue, as consumers paying for products like iPhones with AI capabilities could argue they are not receiving the promised service. However, Bennett believes Apple will likely act to resolve the problem before regulatory involvement becomes necessary. 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

Read More
What is Heroku

What is Heroku

What is Heroku? Heroku is a modern, container-based Platform as a Service (PaaS) that enables developers to deploy, manage, and scale applications with ease. Designed for simplicity, flexibility, and elegance, it provides the fastest path for developers to take their apps to market. Key Features of Heroku: The Evolution of Heroku Heroku has recently undergone a transformation, becoming fully cloud-native with advanced integrations like Kubernetes, OpenTelemetry, and Agentforce, an AI-powered enhancement to its platform. These upgrades retain the platform’s hallmark simplicity while delivering more performance and tools, such as Graviton processors, EKS, ECR, and AWS Global Accelerator. AI-Powered Innovation: Agentforce Agentforce, Heroku’s latest feature, brings AI-powered automation to app development. It empowers both technical and non-technical users by offering natural language workflows for building applications, making it accessible to a wider range of business users. According to Betty Junod, Heroku’s Chief Marketing Officer at Salesforce, the platform now seamlessly combines user-friendly experiences with cutting-edge AI capabilities: “We’ve replatformed while keeping the experience as simple as ever, but now with added horsepower, Graviton performance, and managed AI tools like Bedrock.” Agentforce is particularly impactful for non-developers, guiding them through building apps and automating processes with no-code or low-code tools. This innovation aligns with Heroku’s mission to make app creation easier and more interactive: “It’s not just apps serving information anymore; users are engaging with them in entirely new ways.” Deliver Apps, Your Way Heroku is designed to serve a variety of needs, from quick prototypes to mission-critical enterprise applications. Its fully managed ecosystem allows you to build and scale apps efficiently, leveraging the tools and languages you already know and love. 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

Read More
Salesforce AI Research Introduces BLIP-3-Video

Salesforce AI Research Introduces BLIP-3-Video

Salesforce AI Research Introduces BLIP-3-Video: A Groundbreaking Multimodal Model for Efficient Video Understanding Vision-language models (VLMs) are transforming artificial intelligence by merging visual and textual data, enabling advancements in video analysis, human-computer interaction, and multimedia applications. These tools empower systems to generate captions, answer questions, and support decision-making, driving innovation in industries like entertainment, healthcare, and autonomous systems. However, the exponential growth in video-based tasks has created a demand for more efficient processing solutions that can manage the vast amounts of visual and temporal data inherent in videos. The Challenge of Scaling Video Understanding Existing video-processing models face significant inefficiencies. Many rely on processing each frame individually, creating thousands of visual tokens that demand extensive computational resources. This approach struggles with long or complex videos, where balancing computational efficiency and accurate temporal understanding becomes crucial. Attempts to address this issue, such as pooling techniques used by models like Video-ChatGPT and LLaVA-OneVision, have only partially succeeded, as they still produce thousands of tokens. Introducing BLIP-3-Video: A Breakthrough in Token Efficiency To tackle these challenges, Salesforce AI Research has developed BLIP-3-Video, a cutting-edge vision-language model optimized for video processing. The key innovation lies in its temporal encoder, which reduces visual tokens to just 16–32 tokens per video, significantly lowering computational requirements while maintaining strong performance. The temporal encoder employs a spatio-temporal attentional pooling mechanism, selectively extracting the most informative data from video frames. By consolidating spatial and temporal information into compact video-level tokens, BLIP-3-Video streamlines video processing without sacrificing accuracy. Efficient Architecture for Scalable Video Tasks BLIP-3-Video’s architecture integrates: This design ensures that the model efficiently captures essential temporal information while minimizing redundant data. Performance Highlights BLIP-3-Video demonstrates remarkable efficiency, achieving accuracy comparable to state-of-the-art models like Tarsier-34B while using a fraction of the tokens: For context, Tarsier-34B requires 4608 tokens for eight video frames, whereas BLIP-3-Video achieves similar results with only 32 tokens. On multiple-choice tasks, the model excelled: These results highlight BLIP-3-Video as one of the most token-efficient models in video understanding, offering top-tier performance while dramatically reducing computational costs. Advancing AI for Real-World Video Applications BLIP-3-Video addresses the critical challenge of token inefficiency, proving that complex video data can be processed effectively with far fewer resources. Developed by Salesforce AI Research, the model paves the way for scalable, real-time video processing across industries, including healthcare, autonomous systems, and entertainment. By combining efficiency with high performance, BLIP-3-Video sets a new standard for vision-language models, driving the practical application of AI in video-based systems. 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

Read More
AI Captivates the World

AI Captivates the World

In the late 1990s, a transformative moment unfolded that expanded the world to enquiring minds—the screeching of a dial-up modem, followed by a pixelated “Welcome” screen that connected users to a vast, invisible network spanning the globe. The internet revolution redefined how people interacted with information and one another, permanently reshaping digital communication. Fast forward to 2024, and a similar wave of innovation is underway. Artificial intelligence is captivating the world with its ability to understand, create, and process information. Massive datasets can now be uploaded to AI tools, which instantly distill complex insights—tasks that once took teams of analysts weeks to complete are now executed in seconds. Just as the internet linked people and information, AI is deepening connectivity across all aspects of life, from healthcare and finance to workplaces and homes. In this evolving digital divide, designers hold a critical role—not only in making AI usable but in ensuring it remains understandable, trustworthy, and human-centered. As Fei-Fei Li, Co-Director of Stanford’s Human-Centered AI Institute, states, “If we want machines to think, we need to teach them to see.” The traditional linear process of problem ideation, design, prototyping, and delivery is no longer sufficient for AI design. Instead, designers find themselves on an “AI design rollercoaster”—a dynamic cycle of constant iteration. One day, a seemingly impossible feature is prototyped, and the next, the entire approach pivots due to breakthroughs in large language model (LLM) capabilities. Many teams develop working prototypes before even defining their target audience. It is akin to painting a landscape from a moving train—compelling, challenging, and occasionally bewildering. However, this state of flux is where innovation thrives. Strategies for Designers: Understanding AI’s Capabilities and Limitations Designing for AI requires an understanding of its strengths and weaknesses. While designers do not need to become machine learning engineers, they must grasp AI fundamentals to communicate effectively with technical teams. For example, neural networks excel at recognizing patterns in unstructured data but often struggle with logical reasoning. Recognizing these limitations prevents the development of features that sound promising in theory but fail in practice. Strategies for Designers: Designing for Data Scalability Data is the lifeblood of AI systems, yet its quality and availability fluctuate over time. Designers must create interfaces that can adapt to changing data landscapes. For instance, an AI-powered personal finance app may initially rely on basic transaction data but later incorporate richer datasets for advanced investment recommendations. Interfaces should be modular and scalable, capable of accommodating evolving AI functionalities. Strategies for Designers: The Role of Prototyping in AI Design Static wireframes and basic mockups are insufficient for AI-driven products. AI prototypes must capture the responsive, dynamic nature of intelligent systems. Interactive prototypes offer stakeholders a tangible preview of AI’s potential, highlighting both opportunities and challenges early in the design process. Strategies for Designers: Developing AI Design Intuition To navigate AI design effectively, professionals must cultivate an “AI design sixth sense”—an intuitive understanding of what works well in AI-driven interactions. Immersing in AI experiences, exploring different tools, and analyzing emerging design patterns help build this expertise. Strategies for Designers: Pushing Boundaries in AI Design There are no established rulebooks for AI design—only a vast frontier waiting to be explored. The absence of rigid norms offers designers the freedom to experiment and push boundaries. Some of the most groundbreaking innovations stem from unconventional ideas once deemed impractical. Strategies for Designers: Strengthening Collaboration Between Design and Engineering In AI product design, the traditional “design then handoff” model is giving way to a more integrated approach. Designers and engineers increasingly work in tandem, refining AI experiences through continuous iteration. Some of the most effective design solutions emerge from close collaboration with technical teams. Strategies for Designers: The Next Frontier of Design As AI design continues to evolve, the parallels to the early days of the internet are striking. The excitement, potential, and magnitude of change are reminiscent of Web 1.0, yet amplified in scope. Looking ahead, the field must address profound questions: Will AI become indistinguishable from human intelligence? Will designers craft interfaces for AI-human hybrids yet to be imagined? Designers play an essential role in shaping this future—not as passive observers, but as architects of the next digital revolution. The experiences they create will define humanity’s interactions with artificial intelligence. This responsibility should inspire innovation, challenge conventions, and push the boundaries of what is possible. Call to Action Begin the AI design journey today. Choose an AI tool, explore its interface, and analyze its capabilities. Identify strengths, weaknesses, and opportunities for improvement. Share insights with fellow designers and contribute to the evolving conversation on AI design. The next breakthrough may arise from a single moment of curiosity. 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

Read More
AI Leader Salesforce

Sales Leads and Lead Scoring

Sales teams often face a growing pile of leads, making it overwhelming to determine where to focus their energy. How do you prioritize effectively? Lead scoring is the answer. This methodology helps rank prospects based on their likelihood to convert into customers. By mastering lead scoring, sales teams can win more deals and drive revenue growth. What is Lead Scoring? Lead scoring is a strategy used by sales teams to evaluate and rank potential customers by assigning values based on their behavior, demographics, and interactions with the business. This process identifies high-quality leads and determines their likelihood of conversion. By implementing lead scoring, sales teams can focus their time and resources on the most promising prospects. Why is Lead Scoring Important? According to the Salesforce State of Sales Report, sales reps spend 25% of their workweek researching, prospecting, and prioritizing leads. These activities are essential for moving prospects through the sales funnel, yet balancing them with other responsibilities is a challenge. Lead scoring streamlines this process, enabling teams to be more productive by focusing on high-value leads. This improves conversion rates while helping sales leadership better forecast pipelines and revenue. For example, imagine a sales rep for a medical software company trying to close deals with 100 hospital leads. Pursuing them randomly wastes time. However, with lead scoring, they can identify the top 10 most promising leads based on specific criteria, saving time and increasing success rates. Key Components of an Effective Lead Scoring System 1. Data Categories 2. Implicit vs. Explicit Data 3. Quality Data A reliable lead scoring system depends on accurate and up-to-date data. Keeping CRM records current and synced ensures a dependable scoring process. 4. Rule Definition Define criteria based on your most successful customer profiles. Identify patterns of attributes and behaviors that consistently lead to conversion. Similarly, assess unconverted leads to understand traits that signal low potential. 5. Manual vs. Predictive Scoring Steps to Implement Lead Scoring Common Lead Scoring Mistakes to Avoid Tools and Software for Lead Scoring The right tools can make lead scoring more efficient: If you’re short on data, opt for tools that can leverage anonymized external datasets to build your scoring model, transitioning to your own data over time as you scale. Real-World Examples Lead Scoring: Your Path to Higher Conversions By effectively implementing lead scoring, your sales team can prioritize high-value leads, boost conversion rates, and achieve sustainable revenue growth. Whether you choose manual or predictive methods, the key is to focus on what drives success for your business. Take control of your sales pipeline—lead scoring will show you the way. 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

Read More
gettectonic.com