Side-by-side comparison of AI visibility scores, market position, and capabilities
TeamOhana is a headcount planning and compensation management platform for finance and HR teams, raised $14M+ in San Francisco.
TeamOhana was founded in 2020 in San Francisco and raised over $14M to build a headcount planning platform that bridges the gap between finance teams modeling headcount budgets in spreadsheets and HR teams managing actual employee data in HRIS systems. The platform creates a live, collaborative headcount plan that connects financial headcount models with real-time HRIS data, ensuring that finance and HR are always working from the same information rather than reconciling divergent spreadsheets at the end of each quarter.\n\nThe platform supports seat-level headcount planning where each open and filled role has associated compensation details, benefits costs, start dates, and budget owner attribution. Recruiters, HR business partners, and finance teams can all view and contribute to the plan within their respective areas of ownership, with approval workflows that gate headcount additions and compensation changes behind the appropriate decision makers. TeamOhana also integrates with ATS systems to connect approved headcount to active recruiting pipelines.\n\nTeamOhana targets mid-market and growth-stage technology companies with 100 to 2,000 employees that have moved beyond spreadsheet headcount planning but are not yet ready for the complexity of enterprise workforce planning tools like Anaplan or Workday Planning. The company competes with Mosaic, Pigment, and Planful in the broader business planning space, while occupying a more HR-specific niche that emphasizes compensation management alongside headcount.
Adept AI raised $415M to pioneer computer-use AI agents; its core research and agent team moved to Amazon in 2024 in a landmark talent acquisition while the company continues developing ACT-1 for enterprise automation.
Adept AI was founded in 2022 by a team of former OpenAI, DeepMind, and Google Brain researchers to build AI that can take actions on computers — navigating software interfaces, filling forms, and executing multi-step workflows in any application. Its ACT-1 model demonstrated the ability to control web browsers and desktop applications through natural language instructions, pioneering the computer-use agent paradigm that Anthropic later commercialized with Claude's computer use feature.
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