
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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StackAI is an enterprise AI automation platform to build end-to-end internal tools and processes with AI agents in a fully compliant and secure way. Designed for large, regulated organizations, it enables teams to automate complex workflows across operations, compliance, finance, IT, and support without heavy engineering.
With StackAI you can:
• Connect knowledge bases (SharePoint, Confluence, Notion, Google Drive, databases) with versioning, citations, and access controls
• Publish AI agents as chat assistants, advanced forms, or APIs integrated into Slack, Teams, Salesforce, HubSpot, or ServiceNow
• Govern usage with enterprise security: SSO (Okta, Azure AD, Google), RBAC, audit logs, PII masking, data residency, and cost controls
• Route across OpenAI, Anthropic, Google, or local LLMs with guardrails, evaluations, and testing
• Deploy in multi-tenant cloud, dedicated cloud, private cloud, or on-premise
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Intervo.ai
Intervo is an open source, enterprise-grade voice and chat AI agent platform designed to automate real-time customer interactions across voice and text channels. It allows businesses to build, train, and deploy custom agents in minutes without code; you define the agent’s purpose, upload domain knowledge (documents, files), choose a voice engine (e.g., ElevenLabs, Azure), and publish it to embedded channels. Its agents support use cases like lead qualification, customer support, AI receptionist/scheduling, interactive product assistance, and internal help agents (for HR, IT, etc.). They can integrate with telephony via Twilio, connect to multiple LLM backends (OpenAI, Claude, Gemini), orchestrate AI workflows, and embed on websites as widgets. It emphasizes scalability, compliance, and flexibility, letting organizations embed context-aware conversational agents that understand complex queries, route calls, and interact via speech or chat.
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