生成的AI ROI平台付款I筹集了由Fuse Partners和Tola Capital领导的490万美元种子回合，以跟踪和预测Genai返回

在Fuse Partners和Tola Capital的带领下，种子返回了一类新的Roi-Intelligence平台，旨在追踪和预测Genai实时返回

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.

企业正在对Genai进行大量投资，但努力衡量其投资回报率，C-Suite高管现在要求的关键因素要求。 Pay-I是Genai的新价值平台，如今正从隐形中出现来解决此问题。该公司宣布了Fuse Partners和Tola Capital共同领导的490万美元的种子资金。

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的平台为产品，财务和工程领导者提供了一个实时仪表板，该仪表板将每个模型调用，提示和令牌链接到特定用例的可衡量业务成果。这允许用户为KPI分配明确的美元或时间值，比较用例的多个版本，并立即查看哪种模型，代理或提示可提供最强的返回。内置的预测引擎项目投射了这些返回，使公司能够优先考虑有效的方法，什么无效，并在其投入生产之前充满信心地扩展了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，大多数团队都衡量了代币计数或延迟的成功，这些指标无法捕获业务价值或证明成本合理。付费我通过将循环从Genai行动到KPIS解决。例如，客户支持团队可以将$ 5,000的价值分配给与代理商完全自动化的Tier-2支持案例。然后，我跟踪代理能力的每个变化如何影响案例的完成时间，而更快的时间推动了更高的价值。团队可以A/B可以测试代理的多个版本，也可以提示哪些版本可以带来吞吐量，CSAT或其他KPI的最高回报。

"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的联合创始人兼首席执行官David Tepper说：“ C-Suite不需要其他用法图表 - 他们需要证明和预测。” “付费I查明点，Genai用例当今创造了净值，量化了美元或小时数的价值，并预测明天的净值将如何复杂化。领导者可以使获胜者倍增并更快地达到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.

企业团队已经使用该产品将硬美元价值分配给Genai增强功能，例如客户支持副驾驶或AI生成的报告。然后，他们A/B测试了不同的代理商或提示发送电子邮件活动，并查看每个变化如何影响任务完成时间，收入转换或诸如CSAT之类的KPI。付费我还预测在全面推出之前的业务影响。

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，并且是Azure内部Genai消费策略的领导者。他在Genai上的第一个专利可以追溯到2011年。此后，他向F500董事会，大学，国会议员和联合国经济学代表团介绍了AI经济学。他与CTO Doron Holan共同创立了Pay-I，他在Microsoft工作了27年，并且是Windows和Azure的节流层的核心建筑师，以及Coo E Erik Winters，他是一家经验丰富的经营者，他在金融和SaaS方面扩展了早期公司。

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.

该产品反映了他们学到的与世界上最大的云购买者合作的知识：传统的成本工具停止使用，而实际决策会在成本符合价值的情况下发生。在Genai中尤其如此，基于令牌的计费，多模式输入，推理模型和代理工作流程使单位经济学不透明和ROI比以往任何时候都更难跟踪。

"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补充说：“借助传统软件，我们可以准确跟踪如何使用功能。” “但是有了Genai，这种可见性就会丢失。付费我缩小了这一差距，并准确地显示了实时创造价值的位置。”

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长。 IDC Projects Enterprise Genai投资到2028年将高达6320亿美元，但CIOS的72％引用了ROI的测量和预测为其排名第一。

"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正在从飞行员到关键任务生产毕业。麦肯锡高级合伙人LariHämäläinen说。

"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，开放式Genai支出的耐心疲软。Pay-I最终为领导者提供了信念投资的数据，将Genai从不透明的成本中心转变为增长引擎。”

"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.

“付费我将每个AI决定变成了一个明确的成本与价值比率，让企业在实时看到模型和设计选择如何影响他们的指标。这种透明度使企业能够控制其AI支出并最佳地分配资源。Pay-I为AI提供了最大的过渡到AI的路线图，''

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

有了新的资金，Pay-I将加速产品开发，并将其平台带到希望以精确度扩展Genai的更多企业团队。已经与早期客户一起生活