Revaly is a Payment Performance Management platform designed to ensure that every legitimate transaction succeeds, protecting the recurring revenue businesses depend on. It uses exclusive issuer signals, network intelligence, and AI-powered optimization to maximize payment approvals across the entire lifecycle. By preventing avoidable failures at the first attempt and intelligently recovering declined payments, Revaly reduces involuntary churn and strengthens customer relationships. The platform continuously analyzes routing errors, behavioral patterns, and ecosystem signals to turn unpredictable payments into predictable revenue. Subscription-based companies rely on Revaly to lift approval rates and compound revenue growth without disrupting their existing billing stack. With over 100 integrations, the system fits seamlessly into current workflows while delivering measurable, long-term financial impact.
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FinOpsly is an AI Cost Governance platform. It brings AI, cloud, data platform and SaaS spend into one attribution, policy and control layer, so enterprises can price a workload before building it, attribute every dollar to an owner, hold spend inside budget under policy, and prove what landed in run-rate.
Your AI invoice is not what your AI costs. One request draws on model tokens, retrieval, warehouse queries, GPU capacity and storage, and only the first shows up on the AI bill. FinOpsly resolves all of it, plus the seats in procurement and the compute in an untagged cloud account, to the same dimensions: owner, team, application, line of business, customer and tenant. An AI initiative's full cost becomes one figure, charged back through one hierarchy in one cycle.
Workforce AI is the tools employees use: seats and per-user token draw across GitHub Copilot, Cursor, ChatGPT Enterprise and Microsoft 365 Copilot. Application AI is the AI your product ships: tokens, compute and data joined into cost-to-serve across OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex AI, SageMaker and Databricks.
PLAN. Price a workload from its architecture before any resource exists, across model APIs, GPU capacity, data platform consumption and storage, with assumptions visible. Compare it across candidate models on your measured usage.
EXPLAIN. Attribute spend to owner, team, application, line of business and business unit across 9+ hierarchy levels. Unified tagging reconciles providers that tag inconsistently, and AI-driven bulk labeling closes large key estates. Unattributed spend is reported in dollars.
ACT. Budgets per project, team and API key, with daily burn-rate monitoring. Anomaly detection with root cause, routed to the owner. Waste detection using FinOpsly's own algorithms and ML models. Commitment planning across AWS, Azure and Google Cloud. Policy-driven parking of idle compute.
PROVE. Chargeback across AI, cloud, data and SaaS in one cycle. Realized savings tracked into run-rate against a no-action baseline. Cost per call, cost per active user, and cost-to-serve per customer and tenant.
proof: 100% attribution of AI spend; chargeback from 12.4 days to under one day across 9+ levels; 26% realized savings in AWS and 17%+ in Azure at a payments client.
Built for CIOs, CTOs and platform leaders accountable for technology spend, FinOps and finance teams running chargeback, and engineering teams who need cost signal before they decide
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MRI-Simmons
MRI-Simmons is a leading provider of actionable insights on the American consumer, offering comprehensive data on consumer attitudes, behaviors, and media usage. It is a consumer insights and activation engine that informs marketing strategy and streamlines data usage to drive business results. MRI-Simmons features intuitive navigation, interactive charts for visual storytelling, and shareable reports and dashboards designed to enhance collaboration. It enables self-service activation, allowing users to fulfill audiences to any DMP, DSP, SSP, or addressable media. MRI-Simmons' USA study is a nationally representative survey providing insights into consumer attitudes, behaviors, media preferences, and more. It employs address-based probabilistic sampling, measuring real people randomly chosen to represent the U.S. population. This methodological approach ensures the stability of insights and provides an accurate view of the American consumer.
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