View Financial HealthPredictiv AI 配当と自社株買い配当金 基準チェック /06Predictiv AI配当金を支払った記録がありません。主要情報n/a配当利回り-10.3%バイバック利回り総株主利回り-10.3%将来の配当利回りn/a配当成長n/a次回配当支払日n/a配当落ち日n/a一株当たり配当金n/a配当性向n/a最近の配当と自社株買いの更新更新なしすべての更新を表示Recent updatesお知らせ • Apr 17Predictiv AI Inc Announces Completion of Phase 1 of Shiftmatics Hardware PlatformPredictiv AI Inc. announced the completion of Phase 1 of its Shiftmatics hardware platform and the receipt of its first client order. Shiftmatics establishes a device-level foundation for continuous data capture, connectivity, and operational visibility. The platform captures high-frequency vehicle and environmental data—beyond traditional GPS tracking—and is supported by onboard storage and backup systems for reliability in real-world environments. Shiftmatics is being developed in stages to transition fleets from tracking to real-time intelligence and edge AI. Stages 1 & 2 (Completed): Core Data & Connectivity. Production-ready hardware platform. High-frequency location, vehicle performance, and environmental data. Telemetry including speed, fuel usage and driving behavior. Environmental sensing (temperature, pressure, humidity). On-device memory and backup power. Stage 3 (In Development): Vision-Enabled Intelligence. Integration of onboard camera systems for operational visibility. Local video storage with optimized data retrieval. Foundation for visual analytics and safety insights. Stage 4 (Future): Edge AI Infrastructure. On-device processing and real-time analysis. Transformation into intelligent edge decision nodes. Support for event detection, predictive insights, and automated responses. Reduced reliance on centralized cloud infrastructure. Evolution from tracking ? intelligence ? execution. Scalable hardware footprint for AI deployment. Foundation for automation, autonomy, and robotics-driven systems. Predictiv AI noted a broader industry shift from passive tracking toward real-time, data-driven operations. While traditional systems focus on location visibility and historical reporting, fleet operators are increasingly seeking real-time insights to improve efficiency, reduce costs, and accelerate decision-making. This shift is driving demand for platforms that not only capture data but also process and act on it in real time—enabling more responsive, intelligent, and automated workflows across fleet operations. Shiftmatics is designed to support this transition, moving from basic visibility toward integrated intelligence where data can be captured, interpreted, and used to inform operational decisions directly within fleet environments. The data generated by the Shiftmatics platform is expected to enable advanced AI-driven applications across Predictiv AI’s ecosystem. Continuous streams of operational and contextual data provide the foundation for domain-specific AI systems that support business workflows, decision-making, and automation. The Company confirmed it has received a request from an initial client to deploy its first set of Shiftmatics devices, with installation expected to begin shortly. This represents the first commercial use of the platform in a live operating environment and a key step toward broader rollout and scaling. Looking ahead, Predictiv AI is developing Shiftmatics as a foundational layer for a broader class of intelligent systems extending beyond traditional fleet management. With the introduction of onboard vision systems and edge-based processing, the platform is expected to enable real-time analysis and decision-making directly at the device level. This localized intelligence will support faster response times, reduced data transmission, and more adaptive operational systems. Over time, these capabilities are expected to enable: Event detection and real-time alerts. Driver behavior analysis. Predictive maintenance. Semi-autonomous operational workflows. Intelligent fleet coordination. The underlying architecture may also extend into adjacent applications where sensing, intelligence, and automation converge, including robotics and real-time physical systems. Shiftmatics operates as part of Predictiv AI’s broader ecosystem, alongside ShiftFleet.ai and CloudRep.ai, creating an integrated stack that connects physical operations with AI-driven insights and workflow automation.決済の安定と成長配当データの取得安定した配当: PCIV.Fの 1 株当たり配当が過去に安定していたかどうかを判断するにはデータが不十分です。増加する配当: PCIV.Fの配当金が増加しているかどうかを判断するにはデータが不十分です。配当利回り対市場Predictiv AI 配当利回り対市場PCIV.F 配当利回りは市場と比べてどうか？セグメント配当利回り会社 (PCIV.F)n/a市場下位25% (US)1.4%市場トップ25% (US)4.2%業界平均 (Software)0.9%アナリスト予想 (PCIV.F) (最長3年)n/a注目すべき配当: PCIV.Fは最近配当金を報告していないため、配当金支払者の下位 25% に対して同社の配当利回りを評価することはできません。高配当: PCIV.Fは最近配当金を報告していないため、配当金支払者の上位 25% に対して同社の配当利回りを評価することはできません。株主への利益配当収益カバレッジ: PCIV.Fの 配当性向 を計算して配当金の支払いが利益で賄われているかどうかを判断するにはデータが不十分です。株主配当金キャッシュフローカバレッジ: PCIV.Fが配当金を報告していないため、配当金の持続可能性を計算できません。高配当企業の発掘7D1Y7D1Y7D1YUS 市場の強力な配当支払い企業。View Management企業分析と財務データの現状データ最終更新日（UTC時間）企業分析2026/05/07 21:14終値2026/05/07 00:00収益2025/12/31年間収益2025/12/31データソース企業分析に使用したデータはS&P Global Market Intelligence LLC のものです。本レポートを作成するための分析モデルでは、以下のデータを使用しています。データは正規化されているため、ソースが利用可能になるまでに時間がかかる場合があります。パッケージデータタイムフレーム米国ソース例会社財務10年損益計算書キャッシュ・フロー計算書貸借対照表SECフォーム10-KSECフォーム10-Qアナリストのコンセンサス予想+プラス3年予想財務アナリストの目標株価アナリストリサーチレポートBlue Matrix市場価格30年株価配当、分割、措置ICEマーケットデータSECフォームS-1所有権10年トップ株主インサイダー取引SECフォーム4SECフォーム13Dマネジメント10年リーダーシップ・チーム取締役会SECフォーム10-KSECフォームDEF 14A主な進展10年会社からのお知らせSECフォーム8-K* 米国証券を対象とした例であり、非米国証券については、同等の規制書式および情報源を使用。特に断りのない限り、すべての財務データは1年ごとの期間に基づいていますが、四半期ごとに更新されます。これは、TTM（Trailing Twelve Month）またはLTM（Last Twelve Month）データとして知られています。詳細はこちら。分析モデルとスノーフレーク本レポートを生成するために使用した分析モデルの詳細は当社のGithubページでご覧いただけます。また、レポートの使用方法に関するガイドやYoutubeのチュートリアルも掲載しています。シンプリー・ウォールストリート分析モデルを設計・構築した世界トップクラスのチームについてご紹介します。業界およびセクターの指標私たちの業界とセクションの指標は、Simply Wall Stによって6時間ごとに計算されます。アナリスト筋Predictiv AI Inc. 0 これらのアナリストのうち、弊社レポートのインプットとして使用した売上高または利益の予想を提出したのは、 。アナリストの投稿は一日中更新されます。0
お知らせ • Apr 17Predictiv AI Inc Announces Completion of Phase 1 of Shiftmatics Hardware PlatformPredictiv AI Inc. announced the completion of Phase 1 of its Shiftmatics hardware platform and the receipt of its first client order. Shiftmatics establishes a device-level foundation for continuous data capture, connectivity, and operational visibility. The platform captures high-frequency vehicle and environmental data—beyond traditional GPS tracking—and is supported by onboard storage and backup systems for reliability in real-world environments. Shiftmatics is being developed in stages to transition fleets from tracking to real-time intelligence and edge AI. Stages 1 & 2 (Completed): Core Data & Connectivity. Production-ready hardware platform. High-frequency location, vehicle performance, and environmental data. Telemetry including speed, fuel usage and driving behavior. Environmental sensing (temperature, pressure, humidity). On-device memory and backup power. Stage 3 (In Development): Vision-Enabled Intelligence. Integration of onboard camera systems for operational visibility. Local video storage with optimized data retrieval. Foundation for visual analytics and safety insights. Stage 4 (Future): Edge AI Infrastructure. On-device processing and real-time analysis. Transformation into intelligent edge decision nodes. Support for event detection, predictive insights, and automated responses. Reduced reliance on centralized cloud infrastructure. Evolution from tracking ? intelligence ? execution. Scalable hardware footprint for AI deployment. Foundation for automation, autonomy, and robotics-driven systems. Predictiv AI noted a broader industry shift from passive tracking toward real-time, data-driven operations. While traditional systems focus on location visibility and historical reporting, fleet operators are increasingly seeking real-time insights to improve efficiency, reduce costs, and accelerate decision-making. This shift is driving demand for platforms that not only capture data but also process and act on it in real time—enabling more responsive, intelligent, and automated workflows across fleet operations. Shiftmatics is designed to support this transition, moving from basic visibility toward integrated intelligence where data can be captured, interpreted, and used to inform operational decisions directly within fleet environments. The data generated by the Shiftmatics platform is expected to enable advanced AI-driven applications across Predictiv AI’s ecosystem. Continuous streams of operational and contextual data provide the foundation for domain-specific AI systems that support business workflows, decision-making, and automation. The Company confirmed it has received a request from an initial client to deploy its first set of Shiftmatics devices, with installation expected to begin shortly. This represents the first commercial use of the platform in a live operating environment and a key step toward broader rollout and scaling. Looking ahead, Predictiv AI is developing Shiftmatics as a foundational layer for a broader class of intelligent systems extending beyond traditional fleet management. With the introduction of onboard vision systems and edge-based processing, the platform is expected to enable real-time analysis and decision-making directly at the device level. This localized intelligence will support faster response times, reduced data transmission, and more adaptive operational systems. Over time, these capabilities are expected to enable: Event detection and real-time alerts. Driver behavior analysis. Predictive maintenance. Semi-autonomous operational workflows. Intelligent fleet coordination. The underlying architecture may also extend into adjacent applications where sensing, intelligence, and automation converge, including robotics and real-time physical systems. Shiftmatics operates as part of Predictiv AI’s broader ecosystem, alongside ShiftFleet.ai and CloudRep.ai, creating an integrated stack that connects physical operations with AI-driven insights and workflow automation.