intelli.dsintelligence.com

Autor: d.david.s.saavedra@gmail.com

  • The Developers Dilemma

    October 27, 2025

    The Developer’s Dilemma: Simplifying AI Deployment in a Cloud-Dominated World

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    Executive Summary

    AI development has largely revolved around cloud-first architectures, but these environments bring complexity, latency, and integration challenges that slow innovation. This whitepaper explores the hurdles developers face in cloud-centric AI deployment, demonstrates how edge computing simplifies workflows, and introduces INTELLI — the world’s first truly private and sovereign EdgeAI API — as a tool that empowers developers to build faster, more flexible, and low-latency AI applications.

    Cloud Development Challenges

    Developers working with cloud-based AI must contend with multiple layers of complexity. Data must be transmitted to remote servers, models require careful orchestration across cloud instances, and network latency can impact performance for real-time applications. Integrating cloud AI with local devices, mobile apps, or IoT systems adds additional friction, making development cycles longer and more error-prone.

    Edge AI for Simplicity

    Edge AI addresses these challenges by allowing computation to happen closer to the device or user. By processing data locally, developers can build applications that respond instantly, reduce dependency on cloud connectivity, and simplify deployment pipelines. Edge-first architectures streamline integration with hardware, mobile devices, and IoT networks, enabling faster development cycles and more predictable performance.

    Developer Use Cases

    Edge AI opens the door to new possibilities for developers. Real-time video analytics can run directly on security cameras or drones, delivering immediate insights without relying on cloud servers. Mobile applications can offer smarter, offline-first experiences that respond instantly to user inputs. In industrial contexts, edge AI enables on-site analytics for machinery, reducing delays and downtime.

    Tooling and API Considerations

    To fully leverage edge AI, developers require robust, user-friendly APIs and frameworks. INTELLI provides a comprehensive, private, and sovereign EdgeAI API that simplifies model deployment, manages resource constraints, and integrates seamlessly with local and edge devices. With developer-friendly tools, teams can focus on building applications rather than wrestling with complex cloud orchestration.

    The future of AI development is at the edge. By embracing edge computing, developers can create faster, more reliable, and innovative applications while maintaining full control over data and latency. Explore how INTELLI can empower you to simplify AI deployment and unlock the full potential of edge-native development.

    Autor

    David Saavedra

    Founder & CEO

  • The Centralized AI Bottleneck

    October 20, 2025

    The Centralized AI Bottleneck: Why Edge AI is Critical for Industry 4.0

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    Executive Summary

    Industry 4.0 promises smarter factories, automated workflows, and data-driven insights — but these advancements are limited by the latency and connectivity issues inherent in centralized AI systems. This whitepaper examines the challenges of cloud-based AI in industrial environments, explores how edge computing enables real-time decision-making, and introduces INTELLI, the world’s first truly private and sovereign EdgeAI API, as a transformative solution for Industry 4.0.

    Limitations of Centralized AI in Industry

    Cloud-based AI requires transmitting data from factory floors to remote servers for analysis, introducing delays that are unacceptable in time-critical industrial applications. Network interruptions or bandwidth constraints further exacerbate the problem, resulting in missed opportunities for predictive maintenance, delayed quality inspections, and inefficiencies across production lines. Centralized AI architectures also struggle to scale effectively across large industrial facilities, creating bottlenecks in operations.

    Edge AI for Industry 4.0

    Edge AI overcomes these limitations by processing data locally on devices, gateways, or on-premise servers. This enables real-time applications such as predictive maintenance, where sensors detect early signs of equipment failure and trigger interventions immediately, or automated quality control, where visual inspections occur instantly on the production line. Local processing ensures faster, more reliable decision-making, reducing downtime and increasing throughput.

    Case Study: The Smart Factory

    In a leading manufacturing facility, edge AI has been deployed to monitor machinery and production lines. By analyzing sensor data on-site, the factory can identify anomalies before they escalate into failures, schedule maintenance dynamically, and optimize production sequences in real time. This has led to measurable reductions in downtime, improved product quality, and significant cost savings.

    Resilience in Low-Connectivity Environments

    Industrial facilities often operate in environments with limited or unstable network connectivity. Edge AI ensures operational continuity even when cloud connections are interrupted, maintaining real-time control over critical processes. This resilience is crucial for industries where downtime can translate into millions of dollars in losses or compromised safety.

    Transforming Manufacturing Efficiency

    Edge AI is not just a technological improvement; it represents a paradigm shift in manufacturing strategy. By enabling faster insights, autonomous decision-making, and operational resilience, edge computing positions factories to compete in an increasingly data-driven global market. Companies that adopt edge-native AI today will define the next generation of industrial efficiency.

    INTELLI: The Edge Engine for Industry 4.0

    INTELLI allows manufacturers to deploy AI models directly at the edge, ensuring low-latency, high-reliability operations without sending sensitive data to the cloud. As the first truly private and sovereign EdgeAI API, INTELLI empowers industrial organizations to enhance efficiency, safeguard data, and accelerate innovation across their operations.

    The future of Industry 4.0 depends on intelligence at the edge. Organizations that integrate edge AI today will achieve faster, safer, and more efficient industrial processes. Discover how INTELLI can transform your manufacturing operations into a smart, resilient, and competitive powerhouse.

    Autor

    David Saavedra

    Founder & CEO

  • Empowering the Edge

    October 13, 2025

    Empowering the Edge: How Local AI Processing is Transforming IoT Ecosystems

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    Executive Summary

    The Internet of Things (IoT) is rapidly connecting billions of devices worldwide, generating unprecedented volumes of data. While cloud computing has powered the first wave of IoT, it is increasingly unable to keep pace with the demands for real-time insights, low latency, and secure data handling. This whitepaper examines how Edge AI is transforming IoT ecosystems by enabling local data processing, reducing bandwidth dependency, and strengthening security. Finally, we introduce INTELLI, the world’s first truly private and sovereign EdgeAI API, which empowers businesses to build the next generation of intelligent, connected devices.

    The IoT Data Deluge

    Every connected sensor, device, and machine continuously generates streams of data — from temperature readings to video feeds. When multiplied across millions of devices, this creates an overwhelming volume of information that is impractical to transmit and store entirely in the cloud. This flood of data leads to network congestion, increased costs, and slower decision-making, hindering the potential of IoT-driven innovation.

    Edge AI as the Solution

    Edge AI addresses these challenges by processing data locally, at or near the device. This enables real-time analytics and decisions without relying on continuous cloud connectivity. By analyzing data at the edge, businesses can filter out noise, transmit only actionable insights, and dramatically reduce bandwidth usage. This approach also improves system responsiveness — a critical factor for time-sensitive applications like predictive maintenance, robotics, and connected healthcare devices.

    Strengthening IoT Security

    IoT networks are notoriously vulnerable to cyberattacks because of their sheer scale and diversity. Transmitting data to centralized servers only increases the attack surface. By keeping sensitive information at the edge, organizations reduce the exposure of critical data and limit opportunities for interception. Local AI processing also allows for faster anomaly detection, helping to identify security threats in real time before they spread.

    Real-World Applications

    Edge AI is already reshaping IoT ecosystems. In smart homes, local AI enables devices to respond instantly to voice commands and optimize energy use without sending personal data to external servers. In industrial IoT, sensors equipped with edge intelligence can predict equipment failures on the spot, reducing downtime. In healthcare, connected devices can monitor patient vitals and trigger alerts locally, ensuring faster response times while safeguarding medical data.

    The Future of Intelligent IoT

    The next generation of IoT will be defined by intelligence at the edge. As models become smaller and hardware more powerful, we will see IoT devices that are not just connected, but capable of autonomous decision-making. This will enable more scalable, resilient, and secure ecosystems that drive innovation across industries.

    INTELLI: Powering the Intelligent Edge

    INTELLI enables businesses to deploy AI models directly within IoT devices and gateways, eliminating unnecessary cloud dependence and unlocking real-time intelligence. As the first truly private and sovereign EdgeAI API, INTELLI makes it possible to build smarter, safer, and more efficient IoT ecosystems without sacrificing privacy or control.

    IoT’s potential can only be fully realized with intelligence at the edge. Organizations that embrace Edge AI today will be the ones shaping the connected, autonomous systems of tomorrow. Learn how INTELLI can help you transform your IoT strategy into a secure, scalable, and future-ready reality.

    Autor

    David Saavedra

    Founder & CEO

  • The Hidden Costs of Cloud AI

    October 06, 2025

    The Hidden Costs of Cloud AI: Why Edge Computing is the Future of Scalability

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    Executive Summary

    Cloud computing has been a driving force behind the AI revolution — but its costs are starting to outpace its benefits. From expensive GPU time to massive data egress fees, cloud-based AI can quickly become an economic burden, especially for enterprises deploying AI at scale. This whitepaper examines the hidden costs of cloud AI, explores the scalability challenges of centralized architectures, and makes the case for edge computing as a cost-effective alternative. Finally, we introduce INTELLI, the world’s first truly private and sovereign EdgeAI API, designed to help businesses scale AI efficiently and sustainably.

    Cloud AI Cost Drivers

    Running AI workloads in the cloud involves a complex and often opaque cost structure. Businesses must pay for GPU usage, data storage, and network bandwidth — and these expenses grow exponentially as the volume of data and frequency of inference requests increase. Additionally, transmitting raw data to centralized servers incurs egress fees that can quickly erode margins, particularly in data-intensive industries like logistics, retail, and manufacturing.

    The Economics of Edge AI

    Edge AI dramatically reduces these costs by processing data locally, near the point of generation. Instead of transmitting massive datasets to the cloud, only high-value insights are sent upstream when needed. This minimizes bandwidth costs, reduces reliance on expensive cloud GPUs, and allows businesses to leverage more affordable edge hardware for day-to-day inference tasks. The result is a leaner, more predictable cost model that scales with business needs.

    Scalability Challenges in the Cloud

    Cloud infrastructure is powerful but not always optimal for large-scale, distributed deployments. Connecting thousands of devices to a central server creates bottlenecks and increases latency. Edge computing, by contrast, distributes processing power across many nodes, enabling near-linear scalability. Each device or gateway can operate semi-autonomously, reducing the load on central infrastructure and improving overall system resilience.

    Industry Examples and Cost Savings

    In logistics, edge AI can optimize fleet routes in real time without transmitting every data point back to the cloud, cutting both bandwidth usage and operational costs. In smart cities, traffic optimization systems can process video feeds locally to adjust signals dynamically, saving millions in cloud storage and processing fees annually. These examples demonstrate how edge deployments unlock both cost efficiency and operational agility.

    Building an ROI Framework

    To evaluate the financial benefits of edge AI adoption, businesses should model their total cost of ownership (TCO) over time, comparing cloud-only versus hybrid or edge-first approaches. Key factors include bandwidth usage, GPU time, latency costs (e.g., downtime or delays), and hardware investment. The analysis often reveals that while edge requires upfront investment, the long-term ROI is significantly higher due to reduced recurring cloud expenses.

    INTELLI: Scaling AI the Smart Way

    INTELLI enables organizations to deploy AI models directly at the edge with minimal cloud dependency, resulting in lower costs and higher scalability. As the first truly private and sovereign EdgeAI API, INTELLI offers a predictable economic model and empowers businesses to grow their AI capabilities without ballooning operational expenses.

    The future of AI scalability lies beyond the cloud. By adopting edge computing today, businesses can control costs, enhance performance, and unlock sustainable growth. Learn how INTELLI can help you build scalable AI systems that are as cost-efficient as they are powerful.

    Autor

    David Saavedra

    Founder & CEO

  • Breaking Free from the Cloud

    September 29, 2025

    Breaking Free from the Cloud: The Case for Decentralized AI in a Connected World

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    Executive Summary

    Artificial intelligence has been largely synonymous with cloud computing — but this dependence comes at a cost. Centralized AI architectures create single points of failure, drive up bandwidth expenses, and limit control over mission-critical workloads. This whitepaper explores why businesses should rethink their cloud-first strategies, making a compelling case for decentralized edge AI. We also introduce INTELLI, the world’s first truly private and sovereign EdgeAI API, which enables organizations to take back control of their data, costs, and innovation roadmap.

    The Problem with Cloud Dependency

    Centralized cloud infrastructure has been instrumental in scaling AI adoption, but it creates significant risks. Outages at major cloud providers can bring entire businesses to a standstill. Bandwidth costs escalate as data volumes grow, and organizations lose direct control over where and how their data is processed. For industries where uptime, sovereignty, and cost efficiency are non-negotiable, relying solely on the cloud introduces fragility.

    Decentralized AI Advantages

    Decentralized edge AI mitigates these risks by distributing processing power closer to where data is generated. This architecture eliminates single points of failure, improves uptime, and allows businesses to maintain sovereignty over their most sensitive information. It also increases resilience — if one edge node fails, others can continue operating independently, keeping critical systems running even during network disruptions.

    Use Cases in the Real World

    Smart retail systems can run analytics locally to track inventory and foot traffic in real time, without exposing customer data to external servers. In IoT-heavy industries, edge-based AI can manage device networks autonomously, reducing the need for constant cloud connectivity. These decentralized setups not only improve performance but also strengthen privacy and security.

    Cost and Efficiency Benefits

    Processing data locally dramatically reduces bandwidth requirements, lowering operational costs associated with transmitting terabytes of information to the cloud. It also optimizes compute resources by only sending high-value insights, not raw data, to centralized servers when necessary. The result is leaner, faster, and more cost-effective AI operations.

    A Strategic Shift for Business Leaders

    The shift to decentralized AI is more than a technology decision — it is a strategic one. Companies that adopt edge-native approaches gain greater control over their infrastructure, reduce operational risk, and position themselves as leaders in efficiency and resilience.

    INTELLI: The Engine of Decentralized AI

    INTELLI empowers businesses to deploy powerful AI models directly at the edge, without relying on centralized servers. As the first truly private and sovereign EdgeAI API, INTELLI gives organizations the freedom to own their data, manage their infrastructure, and scale AI deployments on their terms.

    Breaking free from the cloud is no longer optional — it’s essential for competitiveness in a hyperconnected world. Explore how INTELLI can help your business decentralize AI, cut costs, and take control of your future.

    Autor

    David Saavedra

    Founder & CEO

  • Latency Kills

    September 22, 2025

    Latency Kills: Why Real-Time AI Demands a Shift to Edge Computing

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    Executive Summary

    In mission-critical applications, every millisecond counts. Whether controlling autonomous vehicles, coordinating industrial robots, or powering emergency response systems, AI must respond in real time. Traditional cloud-based AI introduces latency that can cost lives, money, and opportunities. This whitepaper explores why low-latency performance is non-negotiable, how edge computing solves the problem, and why INTELLI — the world’s first truly private and sovereign EdgeAI API — is redefining the future of real-time AI.

    Latency Challenges in Cloud AI

    Cloud AI architectures rely on sending data to remote servers for processing before returning results. This round trip introduces latency that is unacceptable in time-sensitive scenarios. For autonomous vehicles, even a fraction of a second delay in object detection can result in accidents. In industrial automation, a delayed signal can halt a production line or allow a defective product to slip through undetected. Internet connectivity, bandwidth fluctuations, and congestion only compound these delays, making cloud-first AI a risky choice for real-time applications.

    Edge AI Benefits

    Edge AI eliminates this latency problem by processing data locally, right where it is generated. By removing the dependency on constant internet connectivity, decisions are made in near real time — enabling self-driving cars to react instantly to hazards, robots to adapt dynamically on factory floors, and emergency response systems to coordinate faster and more efficiently. Local processing not only improves responsiveness but also enhances reliability, since operations continue even if network connectivity is lost.

    Case Studies and Applications

    In manufacturing, edge AI enables real-time quality control by inspecting products as they come off the line, rejecting defects before they reach customers. In public safety, edge-enabled drones can analyze live video feeds on the spot to locate victims or assess hazards, transmitting only essential insights back to command centers. These examples highlight how low-latency AI is not just a performance advantage but a mission-critical requirement.

    Scalability Considerations

    While edge computing introduces challenges such as limited computational resources and power constraints, these are being mitigated by advances in efficient AI models, hardware acceleration, and distributed edge architectures. Businesses adopting edge AI must consider infrastructure planning, model optimization, and lifecycle management — but the payoff is a more reliable, responsive system that scales intelligently.

    Market Outlook

    The demand for low-latency AI is fueling rapid growth in the edge computing market. Analysts project a surge in edge deployments across automotive, manufacturing, logistics, and public safety sectors. Organizations that invest early in edge AI will enjoy not just faster performance, but a first-mover advantage in markets where responsiveness is a competitive differentiator.

    INTELLI: Real-Time AI, Reimagined

    INTELLI is built for the latency-sensitive future. As the first truly private and sovereign EdgeAI API, INTELLI allows developers to deploy advanced AI models directly on devices or edge servers, bypassing the delays of cloud processing. This means faster decisions, safer systems, and more resilient operations — without compromising on privacy or compliance.

    Real-time AI can no longer wait on the cloud. Organizations that embrace edge computing today will set the standard for speed, safety, and reliability tomorrow. Discover how INTELLI can help you build AI systems that think — and act — at the speed of life.

    Autor

    David Saavedra

    Founder & CEO

  • The Privacy Paradox

    September 15, 2025

    The Privacy Paradox: Balancing AI Innovation with Data Security at the Edge

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    Executive Summary

    The rapid rise of AI is transforming how businesses operate — from real-time decision-making to predictive analytics. Yet, this transformation comes with a dilemma: how do we leverage AI innovation without compromising the security and privacy of sensitive data? This whitepaper explores the privacy paradox, outlines the risks of cloud-based AI, and demonstrates why edge computing represents the most secure and scalable path forward. Finally, we introduce INTELLI, the world’s first truly private and sovereign EdgeAI API, designed to help organizations achieve the perfect balance between AI innovation and data protection.

    The Rising Privacy Concerns

    Global awareness of data privacy has never been higher. Regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States demand that organizations maintain strict control over user data. Beyond regulatory compliance, consumers themselves are demanding greater transparency and sovereignty over their personal information. In this climate, companies that mishandle data risk not just fines but reputational damage and loss of trust.

    The Risks of Cloud AI

    Traditional cloud-based AI architectures rely on transmitting raw data to centralized servers for processing. This approach introduces multiple points of vulnerability: data can be intercepted during transmission, breached on remote servers, or delayed due to network latency. For industries such as healthcare, finance, and critical infrastructure, these risks are unacceptable. The cost of a data breach — both financially and in terms of public trust — is simply too high.

    Edge AI as the Solution

    Edge AI offers a paradigm shift. By processing data locally — on devices, gateways, or dedicated edge servers — organizations dramatically reduce the exposure of sensitive information. Data never leaves its point of origin, ensuring compliance with stringent privacy regulations and eliminating most network transmission risks. Additionally, edge computing reduces latency, enabling real-time decision-making where milliseconds matter, such as in patient monitoring systems or financial fraud detection engines.

    Industry Applications

    In healthcare, edge AI enables secure, real-time monitoring of patient vitals without transmitting private health data to external servers, preserving both privacy and regulatory compliance. In finance, edge-based fraud detection systems can analyze transactions in real time, blocking suspicious activity immediately while keeping customer data safe. Other industries, including manufacturing and energy, are exploring edge solutions to maintain operational confidentiality and reduce cybersecurity risks.

    Future Trends and Business Strategy

    The future of AI will be privacy-first. As businesses adapt to global regulations and consumer expectations, edge-native architectures will become a strategic advantage. Organizations that adopt edge AI early will position themselves as leaders in trust, innovation, and compliance — avoiding costly retrofits later.

    INTELLI: Making Private AI Practical

    INTELLI is the first EdgeAI API designed to be truly private and sovereign. By allowing businesses to run powerful AI models locally, INTELLI eliminates the need to send data to the cloud, aligning with regulatory requirements and consumer expectations. With INTELLI, innovation and privacy are no longer at odds — they work together.

    For businesses navigating the privacy paradox, the time to act is now. Deploying edge-based AI solutions not only safeguards your data but also accelerates your ability to innovate. Learn how INTELLI can help you turn privacy into a competitive advantage.

    Autor

    David Saavedra

    Founder & CEO

  • Finalist at Fintech World Cup Mexico 2025

    Finalist at Fintech World Cup Mexico 2025

    January 31, 2025

    INTELLI as Finalist at FinTech World Cup Mexico 2025 for Revolutionizing Financial Access Offline

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    On April 7, 2025, at the University of La Libertad in Mexico City, INTELLI (DS Intelligence) emerged as one of the standout finalists during the FinTech World Cup Mexico qualifier. The event brought together visionary fintech startups to compete for a coveted spot at the Grand Finale in Dubai. DS Intelligence was celebrated alongside innovative peers for its groundbreaking approach to decentralized, offline‑first AI tools in financial services.

    A Platform Built for Financial Sovereignty

    Competing in a Shark‑Tank format before a panel of investors and industry experts, INTELLI showcased its mission: deploying AI offline, ensuring complete data privacy, and empowering financial institutions—even in areas with limited connectivity. The approach aligned seamlessly with the competition’s core values—innovation, inclusion, and strategic scalability.

    INTELLIs Solution

    INTELLI developed in less than a month a Financial Assistant with an offline decentralized AI API that allows micro and small SMEs to run their day to day activities with all the finance assiatnce they needed. Capable of receive data via text or voice, it captures data and delivers every week crucial financial data such as margins, cost system, metrics, and other data that is vital for their operation; also advicing with information for decision-making and projections on sales and costs.

    Solution Impact

    In Latin America alone, over 400 million people remain underbanked or underserved by traditional financial services. With rising data protection regulations and unequal access to internet infrastructure, cloud-based AI solutions leave millions behind. Its offline capability ensures consistent performance in volatile network conditions, while its privacy-first model aligns with regional data laws like Mexico’s LFPDPPP and Brazil’s LGPD. INTELLI is not just a technological shift—it’s an inclusive infrastructure for the future of financial access.

    INTELLI’s recognition as a national finalist marks a major milestone for DS Intelligence—validating its vision of financial sovereignty through offline, privacy-conscious AI. As DS Intelligence gears up for the Grand Finale in Dubai, the company stands ready to amplify its mission on the world stage.

    Autor

    David Saavedra

    Founder & CEO

  • INTELLI joins IFE Conf 2025

    INTELLI joins IFE Conf 2025

    January 31, 2025

    INTELLI Joins AI Innovation Wave in Education at IFE Conference 2025 in Monterrey, Mexico

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    At the 11th annual IFE Conference—INTELLI was spotlighted amid a powerful gathering of over 4,650 attendees from 261 institutions across 27 countries, united by the theme “Driving the Future of Education with Innovation and Technology.

    Diving Deep into AI in Education

    The event kicked off with the “Artificial Intelligence in Education: Expectation vs. Reality” session, led by experts from IFE, discussing both opportunities and ethical considerations of AI in schools. INTELLI was featured as a decentralized, privacy-first platform—showing how on‑premise AI can safeguard student data while enabling personalized learning.

    Spotlight on Decentralized Infrastructure

    Aligned with the conference’s key topics—ethics, data privacy, and ed‑tech infrastructure—INTELLI stood out for enabling schools and universities to deploy AI locally, without dependency on cloud services or internet connections. This model resonated as a practical approach to educational sovereignty.

    Building Policy & Partnership Bridges

    With over 250 activities, including panels, keynotes, workshops, and an ed‑tech startup fair attended by more than 110 university presidents, the conference facilitated strategic discussions. DS Intelligence connected with policymakers, educators, and startups—advancing INTELLI’s mission in Latin America

    By showcasing INTELLI at IFE 2025, DS Intelligence reinforced its commitment to delivering resilient, ethical, and sovereign AI tools for education—helping shape the future of learning in Latin America and beyond.

    Autor

    David Saavedra

    Founder & CEO

  • Where It All Began

    Where It All Began

    October 20, 2024

    Where It All Began: David Saavedra Discovers Offline AI Infrastructure at META Hackathon CDMX

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    Before INTELLI became the world’s first decentralized AI API, its spark was ignited at the META Hackathon held at CENTRO in Mexico City. It was here that David Saavedra, then exploring the frontiers of AI, encountered the concept of offline-first infrastructure—an idea that would later become the foundation of DS Intelligence.

    The Seed of a Movement

    The 2024 hackathon, hosted by META and CENTRO, brought together the brightest minds in Latin America to imagine the future of technology. Among them was Saavedra, whose exposure to edge computing and offline neural models led him to a single insight: AI doesn’t need the cloud to be powerful. It needs to be sovereign.

    From Inspiration to Execution

    What began as curiosity turned into a mission. Over the following months, Saavedra assembled a team and began developing what would become INTELLI: a fully modular, offline-first AI API platform for businesses and institutions. The principles of privacy, local execution, and agentic design—planted during that hackathon—are now embedded in every layer of the INTELLI ecosystem.

    Disruptive from the Bones

    While many startups pitch cloud supremacy, INTELLI was born from a different perspective. A single encounter in Mexico City sparked a vision of decentralized, resilient, human-centered AI—and that origin continues to define its trajectory today.

    Get Started with INTELLI Today!

    Autor

    David Saavedra

    Founder & CEO