
Most enterprises can report what AI cost them. Far fewer can say which team owns it, whether it was approved, or what it returned.
FinOpsly closes that gap. The platform governs AI spend on the same cost model that carries the cloud, data platform and SaaS an AI workload consumes, so a business unit sees the full cost of an AI initiative instead of four disconnected bills.
Capabilities include:
Cost estimation before deployment. Model an architecture and get a priced workload across model APIs, GPU capacity, warehouse consumption and storage, with the assumptions on screen. Weigh model choices against consumption you have actually measured.
Attribution that holds up in a chargeback cycle. Spend resolves to owners, teams, applications, business units and customers through hierarchies nine or more levels deep. Tagging is standardized across providers, keys and resources are labeled in bulk from plain-language rules, and whatever remains unattributed is published as a number, not absorbed.
Guardrails that act. Set budgets by project, team or API key. Catch anomalies with root cause and route them to whoever owns the resource. Surface waste that provider tooling misses, using FinOpsly's own detection models. Plan commitments across AWS, Azure and Google Cloud. Park idle compute on approved schedules, reversibly.
Financial results you can defend. Automated chargeback in a single cycle. Savings measured as what reached run-rate against a no-action baseline. Unit economics down to cost per call, per active user and per customer served.
For technology and finance leaders accountable for what AI spend returns.
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With TrafficGuard, you can put an end to the worry of polluted traffic disrupting your campaign success.
Our advanced ML/AI-powered technology identifies and blocks both simple and complex fraudulent traffic in real time, ensuring your ad spend targets genuine, high-quality clicks and conversions. This leads to better campaign outcomes and an enhanced return on ad spend (ROAS).
This robust solution safeguards every dollar of your advertising budget, allowing you to concentrate on reaching your marketing objectives without stress. Let TrafficGuard handle ad fraud protection, so you can confidently manage your:
Google Search (PPC) campaigns
Mobile user acquisition campaigns
Affiliate spending
Social media advertising
In addition to our technology, we provide expert campaign management and exceptional customer support, making us a reliable partner for all your ad fraud protection needs.
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Stella
Stella is a platform designed for marketing measurement, providing marketers with robust, scientifically validated insights into which advertisements, campaigns, and media channels effectively contribute to increased revenue. The platform is equipped with three primary tools: Incrementality Testing, Always-On Incrementality, and Media Mix Modeling (MMM). Through Incrementality Testing, Stella conducts geo-holdout studies, also known as inverse holdouts, to evaluate performance differences between test and control areas, effectively isolating the causal effects of advertisements as opposed to relying solely on attribution methods. This tool simplifies complex statistical processes, including causal inference and confidence intervals, allowing users to understand the potential outcomes without a specific campaign, thus uncovering the genuine “lift” attributed to each advertisement. Furthermore, its Media Mix Modeling feature employs a unique Bayesian approach to dissect historical marketing expenditures and various external influences, such as seasonality and promotional events, to assess the contribution of each channel to overall sales effectively. By leveraging these advanced methodologies, Stella empowers marketers to make informed decisions based on accurate data analysis.
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Robyn
Robyn is a cutting-edge, open-source Marketing Mix Modeling (MMM) tool created by Meta’s Marketing Science team for experimental purposes. It aims to assist advertisers and analysts in constructing thorough, data-driven models that assess how various marketing channels affect business results, such as sales and conversions, while ensuring privacy through aggregated data. Instead of depending on tracking individual users, Robyn delves into historical time-series data by integrating marketing expenditure or reach information—encompassing ads, promotions, and organic initiatives—with performance indicators to evaluate incremental impacts, saturation effects, and carry-over dynamics. The package utilizes a combination of classical statistical techniques and contemporary machine learning methods; it employs ridge regression to mitigate multicollinearity in complex models, performs time-series decomposition to differentiate between trends and seasonal patterns, and incorporates a multi-objective evolutionary algorithm for optimization. This innovative approach allows businesses to gain deeper insights into their marketing effectiveness and make more informed decisions based on robust analysis.
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