View Future GrowthPredictiv AI 過去の業績過去 基準チェック /06Predictiv AIの収益は年間平均-1293.6%の割合で減少していますが、 Software業界の収益は年間 増加しています。収益は年間21.9% 651.5%割合で 増加しています。主要情報-1,293.58%収益成長率n/aEPS成長率Software 業界の成長17.33%収益成長率651.45%株主資本利益率-1,265.75%ネット・マージン-10,095.28%前回の決算情報31 Dec 2025最近の業績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.収支内訳Predictiv AI の稼ぎ方とお金の使い方。LTMベースの直近の報告された収益に基づく。収益と収入の歴史OTCPK:PCIV.F 収益、費用、利益 ( )CAD Millions日付収益収益G+A経費研究開発費31 Dec 250-90031 Dec 240-100質の高い収益: PCIV.Fは現在利益が出ていません。利益率の向上: PCIV.Fは現在利益が出ていません。フリー・キャッシュフローと収益の比較過去の収益成長分析収益動向: PCIV.Fの過去 5 年間の前年比収益成長率がプラスであったかどうかを判断するにはデータが不十分です。成長の加速: PCIV.Fの過去 1 年間の収益成長を 5 年間の平均と比較することはできません。現在は利益が出ていないためです。収益対業界: PCIV.Fは利益が出ていないため、過去 1 年間の収益成長をSoftware業界 ( 13% ) と比較することは困難です。株主資本利益率高いROE: PCIV.Fは現在利益が出ていないため、自己資本利益率 ( -1265.75% ) はマイナスです。総資産利益率使用総資本利益率過去の好業績企業の発掘7D1Y7D1Y7D1YSoftware 、過去の業績が好調な企業。View Financial Health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.