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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JAMS is an automation orchestration and job scheduling solution that runs, monitors, and manages critical IT processes from a single console, from simple batch jobs to complex, cross-platform workflows. JAMS automates jobs across Windows, Linux, UNIX, IBM i, z/OS, and OpenVMS, with native integrations for the databases, BI tools, and ERP systems already running your business, including SQL Server and SAP. Jobs run on any schedule or trigger off other events, and dependency management keeps multi-step workflows in the right order.
Every job is centrally monitored, with notifications on success or failure and an audit trail of every execution. Built-in conversion tools migrate existing jobs from Windows Task Scheduler, SQL Agent, or Cron without rebuilding them, and JAMS replaces homegrown, single-platform scripts with one centrally managed system.
JAMS includes two AI capabilities at no additional cost. JAX is an AI agent built into the JAMS Web Client. Ask it a question in plain language, and it finds a job, troubleshoots a failure, or looks up how to do something, grounded in JAMS documentation, not general AI guesswork. It acts only when asked, and every change waits for your approval. JAMS MCP brings JAMS into the AI coding tools teams already use, including Cursor, Claude Code, GitHub Copilot, and Claude Desktop.
Both run inside the customer's network with the signed-in user's permissions and no elevated AI account, and every action, AI-driven or not, lands in the same audit trail as everything else in JAMS.
For teams managing thousands of jobs across SQL Server, ADF, Airflow, SAP, JDE, and Banner, this cuts tribal knowledge and middle-of-the-night troubleshooting. Knowledge that once lived in one person's head becomes something any team member can ask about directly.
The AI lives in the product, not in the support queue. Support is staffed by humans JAMS will never outsource, based in the United States, the United Kingdom, and Australia. New tickets go to long-tenured engineers, and every JAMS customer has the CEO's cell phone number.
JAMS' mission is to reduce the operational burden of critical automation, so teams spend more time on the work automation was meant to free them for.
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NudgeBee
NudgeBee is an AI Agents and Agentic Workflow platform built for SRE, CloudOps, and DevOps teams. It combines pre-built AI Assistants for incident troubleshooting, cloud cost optimization, and Kubernetes operations with a visual no-code Workflow Builder for custom automation. NudgeBee's AI engine auto-investigates alerts using a live semantic Knowledge Graph, grounded in your actual infrastructure topology. It queries data in place from existing tools (Prometheus, Datadog, Grafana, Loki) with zero data ingestion. The Workflow Builder supports 20+ action categories, native AWS/Azure/GCP CLI nodes, A2A and MCP protocol support, and human-in-the-loop approval gates. 49+ integrations. Enterprise-ready with RBAC, audit trails, BYOM (Bring Your Own Model), and self-hosted deployment. SOC-2 Type II and ISO 27001 compliant.
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