News from the AI & ML world
DeeperML - #businesses
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Lyzr Team@Lyzr AI
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The rise of Agentic AI is transforming enterprise workflows, as companies increasingly deploy AI agents to automate tasks and take actions across various business systems. Dust AI, a two-year-old artificial intelligence platform, exemplifies this trend, achieving $6 million in annual revenue by enabling enterprises to build AI agents capable of completing entire business workflows. This marks a six-fold increase from the previous year, indicating a significant shift in enterprise AI adoption away from basic chatbots towards more sophisticated, action-oriented systems. These agents leverage tools and APIs to streamline processes, highlighting the move towards practical AI applications that directly impact business operations.
Companies like Diliko are addressing the challenges of integrating AI, particularly for mid-sized organizations with limited resources. Diliko's platform focuses on automating data integration, organization, and governance through agentic AI, aiming to reduce manual maintenance and re-engineering efforts. This allows teams to focus on leveraging data for decision-making rather than grappling with infrastructure complexities. The Model Context Protocol (MCP) is a new standard developed by Dust AI that enables this level of automation, allowing AI agents to take concrete actions across business applications such as creating GitHub issues, scheduling calendar meetings, updating customer records, and even pushing code reviews, all while maintaining enterprise-grade security. Agentic AI is also making significant inroads into risk and compliance, as showcased by Lyzr, whose modular AI agents are deployed to automate regulatory and risk-related workflows. These agents facilitate real-time monitoring, policy mapping, anomaly detection, fraud identification, and regulatory reporting, offering scalable precision and continuous assurance. For example, a Data Ingestion Agent extracts insights from various sources, which are then processed by a Policy Mapping Agent to classify inputs against enterprise policies. This automation reduces manual errors, lowers compliance costs, and accelerates audits, demonstrating the potential of AI to transform traditionally labor-intensive areas.
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Chris McKay@Maginative
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Snowflake is aggressively expanding its footprint in the cloud data platform market, moving beyond its traditional data warehousing focus to become a comprehensive AI platform. This strategic shift was highlighted at Snowflake Summit 2025, where the company showcased its vision of empowering business users with advanced AI capabilities for data exploration and analysis. A key element of this transformation is the recent acquisition of Crunchy Data, a move that brings enterprise-grade PostgreSQL capabilities into Snowflake’s AI Data Cloud. This acquisition is viewed as both a defensive and offensive maneuver in the competitive landscape of cloud-native data intelligence platforms.
The acquisition of Crunchy Data for a reported $250 million marks a significant step in Snowflake’s strategy to enable more complex data pipelines and enhance its AI-driven data workflows. Crunchy Data's expertise in PostgreSQL, a well-established open-source database, provides Snowflake with a FedRAMP-compliant, developer-friendly, and AI-ready database solution. Snowflake intends to provide enhanced scalability, operational governance, and performance tooling for its wider enterprise client base by incorporating Crunchy Data's technology. This strategy is meant to address the need for safe and scalable databases for mission-critical AI applications and also places Snowflake in closer competition with Databricks. Furthermore, Snowflake introduced new AI-powered services at the Summit, including Snowflake Intelligence and Cortex AI, designed to make business data more accessible and actionable. Snowflake Intelligence enables users to query data in natural language and take actions based on the insights, while Cortex AISQL embeds AI operations directly into SQL. These initiatives, coupled with the integration of Crunchy Data’s PostgreSQL capabilities, indicate Snowflake's ambition to be the operating system for enterprise AI. By integrating such features, Snowflake is trying to transform from a simple data warehouse to a fully developed platform for AI-native apps and workflows, setting the stage for further expansion and innovation in the cloud data space.
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@pub.towardsai.net
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DeepSeek's R1 model is garnering attention as a potential game-changer for entrepreneurs, offering advancements in "reasoning per dollar." This refers to the amount of reasoning power one can obtain for each dollar spent, potentially unlocking opportunities previously deemed too expensive or technologically challenging. The model's high-reasoning capabilities at a reasonable cost are seen as a way to make advanced AI more accessible, particularly for tasks that require deep understanding and synthesis of information. One example is the creation of sophisticated AI-powered tools, like a "lawyer agent" that can review contracts, which were once cost-prohibitive.
The DeepSeek R1 model has been updated and released on Hugging Face, reportedly featuring significant changes and improvements. The update comes amidst both excitement and apprehension regarding the model's capabilities. While the model demonstrates promise in areas like content generation and customer support, concerns exist regarding potential political bias and censorship. This stems from observations of alleged Chinese government influence in the model's system instructions, which may impact the neutrality of generated content. The adoption of DeepSeek R1 requires careful self-assessment by businesses and individuals, weighing its strengths and potential drawbacks against specific needs and values. Users must consider the model's alignment with their data governance, privacy requirements, and ethical principles. For instance, while the model's content generation capabilities are strong, some categories might be censored or skewed by built-in constraints. Similarly, its chatbot integration may lead to heavily filtered replies, raising concerns about alignment with corporate values. Therefore, it is essential to be comfortable with the possible official or heavily filtered replies, and to consider monitoring the AI's responses to ensure they align with the business' values.
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