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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Haus
Haus is a marketing science platform that enables brands to measure the precise business impact of their advertising efforts, both online and offline, through automated incrementality experiments. It offers products like GeoLift for geo-based incrementality testing, Causal Attribution for day-to-day incrementality reporting, and the upcoming Causal MMM for incrementality-powered media mix modeling. These tools allow users to design and launch experiments in minutes, obtain results in as little as two weeks, and optimize marketing investments with daily incrementality reporting. Haus emphasizes privacy-durable solutions that do not rely on pixels, cookies, or personally identifiable information, ensuring compliance with evolving privacy regulations.
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