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RB America Corp to spend $16 Million to occupy 100,000 square feet of space in Kansas City Missouri.

Kansas City, Missouri — According to state and local development sources, RB America Corp plans to invest $16 Million to build out 100,000 square feet of new space in Kansas City. The company plans to…

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Connecty AI, emerging from stealth today with $1.8 million in pre-seed funding, has developed a context engine that tackles the inherent complexity in enterprise data. The round was led by Market One Capital, with participation from Notion Capital and data industry experts including Marcin Zukowski, co-founder of Snowflake and Maciej Zawadzinski, Founder of Piwik PRO.

Today, enterprise data teams navigate complexity across three critical dimensions: horizontal data pipelines (including multi-source ingestion, multi-cloud data warehousing, data lineage tools, and cataloging systems), diverse consumption patterns (spanning CRM systems, BI dashboards, and machine learning applications), and distributed human knowledge across roles like data engineers, analysts, governance teams, and functional managers. While early AI solutions attempted to automate data workflows by interpreting complex schemas, these models fall short in enterprise environments. Even 90% accuracy isn’t enough when dealing with real-world data complexity. Large Language Models need more than static schema files; they require a continuously evolving, cohesive understanding across systems and teams.

In a statement Aish Agarwal, CEO of Connecty AI said, “Our experience has shown us that effective data management is about more than just technology—it’s about connecting the dots between data sources, business objectives and the people who use them.” “Any ad-hoc ‘guerrilla style experimentation’ with LLM data agents can lead to a pilot application but it’s a lot harder to build a production level application that is reliable.” Looking ahead, Connecty AI will expand its context engine’s capabilities across additional data sources and offer it as a service via API. In a market flooded with AI tools that promise to replace human analysts but deliver unreliable results, Connecty AI is taking a fundamentally different approach – embracing the complexity of enterprise data environments and augmenting rather than replacing human expertise.

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Houston-based DocJuris, a leader in AI contract review, announced the successful closure of its series A funding round by raising $8 million in new capital. This brings the total capital raised to date to $11.2 million. Companies like Siemens, Dell, FedEx, Toyota, and Duke Energy already use DocJuris with its AI-powered contract negotiation software that automates important tasks during the review, redlining, and negotiation of contracts. DocJuris uses a platform to screen third-party contracts in seconds, can redline clauses with playbook-compliant edits in one click, and also generate formatted track changes, exception tables, and amendments with a cloud-based application.

In a statement Henal Patel, DocJuris CEO and Founder said, "DocJuris AI has become an industry-leading platform that empowers enterprise legal, procurement, and sales teams to close deals faster while reducing risk." "With this funding, we will continue scaffolding our platform around generative AI, expand our customer success team, and grow our user base." DocJuris's AI-powered contract negotiation software automates key tasks during the review, redlining, and negotiation of contracts. These capabilities come at a critical time for enterprise teams and general counsels. In a DocJuris survey of 700+ in-house attorneys and contract administrators, an astonishing 74% of companies manually review and draft contracts, amounting to significant errors and time lost in legal operations, sales cycles, and supply chain management. Headquartered in Houston, DocJuris is a dynamic legal tech company committed to revolutionizing contract review and negotiation with its state-of-the-art AI platform. Led by a seasoned team of legal professionals and tech enthusiasts, DocJuris optimizes contract review processes, guarantees policy alignment, and reduces contract negotiation timeframes.

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