生成的AI ROI平台付款I籌集了由Fuse Partners和Tola Capital領導的490萬美元種子回合，以跟踪和預測Genai返回

在Fuse Partners和Tola Capital的帶領下，種子返回了一類新的Roi-Intelligence平台，旨在追踪和預測Genai實時返回

Enterprises are investing heavily in GenAI but struggle to measure its ROI, a critical factor C-suite executives are now demanding. Pay-i, a new value-intelligence platform for GenAI, is emerging from stealth today to solve this. The company announced $4.9 million in seed funding co-led by Fuse Partners and Tola Capital.

企業正在對Genai進行大量投資，但努力衡量其投資回報率，C-Suite高管現在要求的關鍵因素要求。 Pay-I是Genai的新價值平台，如今正從隱形中出現來解決此問題。該公司宣布了Fuse Partners和Tola Capital共同領導的490萬美元的種子資金。

Pay-i's platform provides product, finance, and engineering leaders with a real-time dashboard linking every model call, prompt, and token to measurable business outcomes for specific use cases. This allows users to assign explicit dollar or time values to KPIs, compare multiple versions of a use case, and instantly see which model, agent, or prompt delivers the strongest return. A built-in forecasting engine projects those returns forward, enabling companies to prioritize what works, sunset what doesn't, and scale GenAI with confidence before it even goes into production.

Pay-I的平台為產品，財務和工程領導者提供了一個實時儀表板，該儀表板將每個模型調用，提示和令牌鏈接到特定用例的可衡量業務成果。這允許用戶為KPI分配明確的美元或時間值，比較用例的多個版本，並立即查看哪種模型，代理或提示可提供最強的返回。內置的預測引擎項目投射了這些返回，使公司能夠優先考慮有效的方法，什麼無效，並在其投入生產之前充滿信心地擴展了Genai。

Most teams today measure success in token counts or latency, metrics that don't capture business value or justify costs. Pay-i solves this by closing the loop from GenAI actions to KPIs. For instance, customer support teams can assign a $5,000 value to fully automating tier-2 support cases with an agent. Pay-i then tracks how each change in the agent's capabilities impacts case completion time, with faster times driving greater value. Teams can A/B test multiple versions of the agent or prompt to see which delivers the strongest return on throughput, CSAT, or other KPIs.

如今，大多數團隊都衡量了代幣計數或延遲的成功，這些指標無法捕獲業務價值或證明成本合理。付費我通過將循環從Genai行動到KPIS解決。例如，客戶支持團隊可以將$ 5,000的價值分配給與代理商完全自動化的Tier-2支持案例。然後，我跟踪代理能力的每個變化如何影響案例的完成時間，而更快的時間推動了更高的價值。團隊可以A/B可以測試代理的多個版本，也可以提示哪些版本可以帶來吞吐量，CSAT或其他KPI的最高回報。

"The C-suite doesn't need another usage chart - they need proof and a forecast,” said David Tepper, co-founder and CEO of Pay-i. "Pay-i pinpoints which GenAI use cases create net-new value today, quantifies that value in dollars or hours, and predicts how it will compound tomorrow. Leaders can double-down on winners and reach ROI faster.”

Pay-I的聯合創始人兼首席執行官David Tepper說：“ C-Suite不需要其他用法圖表 - 他們需要證明和預測。” “付費I查明點，Genai用例當今創造了淨值，量化了美元或小時數的價值，並預測明天的淨值將如何復雜化。領導者可以使獲勝者倍增並更快地達到ROI。”

The product is already being used by enterprise teams to assign hard dollar values to GenAI-enhanced features like customer support copilots or AI-generated reports. They then A/B test different agents or prompts for an email campaign and see how each change impacts task completion time, revenue conversion, or KPIs like CSAT. Pay-i also forecasts the business impact before full rollout.

企業團隊已經使用該產品將硬美元價值分配給Genai增強功能，例如客戶支持副駕駛或AI生成的報告。然後，他們A/B測試了不同的代理商或提示發送電子郵件活動，並查看每個變化如何影響任務完成時間，收入轉換或諸如CSAT之類的KPI。付費我還預測在全面推出之前的業務影響。

Tepper previously spent 19 years at Microsoft and was a leader in Azure's internal GenAI consumption strategy. His first patent on GenAI dates back to 2011. He has since briefed F500 boards, universities, members of Congress, and UN delegations on AI economics. He co-founded Pay-i with CTO Doron Holan, who spent 27 years at Microsoft and was a core architect for Windows and Azure's throttling layer, and COO Erik Winters, a veteran operator who has scaled early-stage companies across finance and SaaS.

Tepper以前在Microsoft工作了19年，並且是Azure內部Genai消費策略的領導者。他在Genai上的第一個專利可以追溯到2011年。此後，他向F500董事會，大學，國會議員和聯合國經濟學代表團介紹了AI經濟學。他與CTO Doron Holan共同創立了Pay-I，他在Microsoft工作了27年，並且是Windows和Azure的節流層的核心建築師，以及Coo E Erik Winters，他是一家經驗豐富的經營者，他在金融和SaaS方面擴展了早期公司。

The product reflects what they learned working with the largest cloud buyers in the world: traditional cost tooling stops at usage, while real decision-making happens where cost meets value. This is especially true in GenAI, where token-based billing, multimodal inputs, reasoning models, and agentic workflows have made unit economics opaque and ROI harder to track than ever.

該產品反映了他們學到的與世界上最大的雲購買者合作的知識：傳統的成本工具停止使用，而實際決策會在成本符合價值的情況下發生。在Genai中尤其如此，基於令牌的計費，多模式輸入，推理模型和代理工作流程使單位經濟學不透明和ROI比以往任何時候都更難跟踪。

"With traditional software, we could track exactly how features were used,” added Holan. "But with GenAI, that visibility gets lost. Pay-i closes that gap and shows exactly where value is being created, in real time.”

Holan補充說：“借助傳統軟件，我們可以準確跟踪如何使用功能。” “但是有了Genai，這種可見性就會丟失。付費我縮小了這一差距，並準確地顯示了實時創造價值的位置。”

The need for clarity is only growing. IDC projects enterprise GenAI investment will top $632 billion by 2028, but 72% of CIOs cite ROI measurement and forecasting as their #1 blocker.

需要清晰的需求只是在增長。 IDC Projects Enterprise Genai投資到2028年將高達6320億美元，但CIOS的72％引用了ROI的測量和預測為其排名第一。

"Generative AI is graduating from pilots to mission-critical production. Enterprises are rolling out knowledge augmentation tools, automated workflows, and starting to create agentic services that reshape core operations and customer journeys. Scaling and managing this responsibly requires two disciplines: high-fidelity observability of entire GenAI use cases and rigorous focus on the impact of these systems through understanding the unit economics and business KPIs affected.” said Lari Hämäläinen, Senior Partner at McKinsey.

“生成的AI正在從飛行員到關鍵任務生產畢業。麥肯錫高級合夥人LariHämäläinen說。

"Across the C-suite, patience for open-ended GenAI spending is wearing thin. Pay-i finally gives leaders the data-backed clarity to invest with conviction, transforming GenAI from an opaque cost center into a growth engine,” said John Connors, former CFO of Microsoft and Operating Partner at Fuse Partners.

“在C套房中，開放式Genai支出的耐心疲軟。Pay-I最終為領導者提供了信念投資的數據，將Genai從不透明的成本中心轉變為增長引擎。”

"Pay-i turns every AI decision into a clear cost-to-value ratio, letting enterprises see, in real time, how model and design choices affect their metrics. This transparency enables businesses to control their AI spend and allocate resources optimally. Pay-i provides a roadmap for the all-important transition to AI,” said Sheila Gulati, Managing Director at Tola Capital.

“付費我將每個AI決定變成了一個明確的成本與價值比率，讓企業在實時看到模型和設計選擇如何影響他們的指標。這種透明度使企業能夠控制其AI支出並最佳地分配資源。Pay-I為AI提供了最大的過渡到AI的路線圖，''

With the new funding, Pay-i will accelerate product development and bring its platform to more enterprise teams looking to scale GenAI with precision. Already live with early customers

有了新的資金，Pay-I將加速產品開發，並將其平台帶到希望以精確度擴展Genai的更多企業團隊。已經與早期客戶一起生活