ADAudit Plus helps keep your Windows Server ecosystem secure and compliant by providing full visibility into all activities. ADAudit Plus provides a clear picture of all changes made to your AD resources including AD objects and their attributes, group policy, and more. AD auditing helps detect and respond to insider threat, privilege misuse, and other indicators of compromise, and in short, strengthens your organization's security posture. Gain granular visibility into everything that resides in AD, including objects such as users, computers, groups, OUs, GPOs, schema, and sites, along with their attributes. Audit user management actions including creation, deletion, password resets, and permission changes, along with details on who did what, when, and from where. Keep track of when users are added or removed from security and distribution groups to ensure that users have the bare minimum privileges.
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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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Paradise
Paradise uses robust, unsupervised machine learning and supervised deep learning technologies to accelerate interpretation and generate greater insights from the data. Generate attributes to extract meaningful geological information and as input into machine learning analysis. Identify attributes having the highest variance and contribution among a set of attributes in a geologic setting, Display the neural classes (topology) and their associated colors resulting from Stratigraphic Analysis that indicate the distribution of facies. Detect faults automatically with deep learning and machine learning processes. Compare machine learning classification results and other seismic attributes to traditional good logs. Generate geometric and spectral decomposition attributes on a cluster of compute nodes in a fraction of the time on a single machine.
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