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ActionAI

Category: AI Infrastructure

Enterprise AI reliability platform that enables mission-critical automations with guaranteed accuracy, transparency, and accountability. ActionAI was founded in 2024. The company is led by Miriam Haart. Based in New York, United States (offices also in Tel Aviv, Israel and Ras Al Khaimah, UAE). Team size: 101-500. Total funding raised: $10M. Latest round: Seed. Key investors include Prominent UAE-based investors (undisclosed).

Founded
2024
Headquarters
New York, United States (offices also in Tel Aviv, Israel and Ras Al Khaimah, UAE)
Team size
101-500
Total funding
$10M

Value proposition

ActionAI enables enterprises to deploy AI for mission-critical workflows with guaranteed accuracy by providing a full-stack reliability infrastructure — from data evaluation to production monitoring — that catches hallucinations, explains exceptions, and ensures every automated decision is transparent and auditable.

Products and solutions

ActOne (AI agent interface), Reliable ActionAI platform (build, run, evaluate, improve), Explainable Exceptions (ExEx) for hallucination mitigation, Multi-agent workflow orchestration, Industry solutions for Financial Services, Manufacturing & Supply Chain, Legal, Government, Healthcare, Insurance, Defense & Aerospace

Unique value

The only enterprise AI platform that can say "I don't know" — using Explainable Exceptions (ExEx) to defer to humans when uncertain, while achieving 99.6%+ accuracy in production across finance, legal, and manufacturing workflows.

Target customer

Enterprises in regulated, mission-critical industries including finance, banking, manufacturing, legal/judicial, insurance, government, supply chain/logistics, and defense where accuracy is non-negotiable and errors carry significant consequences.

Industries served

Financial Services, Manufacturing & Supply Chain, Legal & Judiciary, Government, Healthcare, Insurance, Defense & Aerospace, Logistics & Trade, Human Resources

Technology advantage

Proprietary Explainable Exceptions (ExEx) framework that eliminates LLM hallucinations via human-in-the-loop exception handling; real-time evaluation of every output against ground truth; full AI lifecycle reliability infrastructure from data to production monitoring; multi-agent workflow orchestration with controlled handoffs and data integrity.

How they differentiate

Unlike generic AI automation platforms that accept hallucination risk, ActionAI is built from the ground up for reliability — scoring every output against ground truth in real-time, flagging low-confidence results, and routing uncertain cases to humans with full context. This makes it suitable for regulated industries where errors are illegal or financially catastrophic.

Main competitors

UiPath, Automation Anywhere, Microsoft Power Automate, Ema, Vellum

Key partnerships

SAP integration (RAK Ceramics deployment), Ras Al Khaimah government (UAE)

Notable customers

RAK Ceramics ($6B global manufacturer, UAE), Ras Al Khaimah Courts (UAE judicial system)

Major milestones

April 2026: Raised $10M Seed round led by UAE-based investors, Deployed AI verdict automation for Ras Al Khaimah Courts achieving 10% higher accuracy than human judges and 240+ hours saved per verdict, Deployed invoice processing automation for RAK Ceramics achieving 18,000+ hours saved annually at 99.6% accuracy

Growth metrics

RAK Ceramics: 18,000+ hours saved/year, 15x efficiency gain, 99.6% accuracy; Ras Al Khaimah Courts: $2M ROI, 240+ hours saved per verdict, +10% accuracy over human judges

Market positioning

Positioned as the "reliable AI" alternative to general-purpose enterprise automation platforms (UiPath, Automation Anywhere, Microsoft Power Automate) and foundation model APIs (OpenAI, Anthropic) that lack built-in trust and verification layers for mission-critical use cases.

Geographic focus

Global, with strong presence in UAE, Israel, and US markets

About Miriam Haart

Stanford University youngest CS instructor and lecturer; Netflix reality star (My Unorthodox Life); TED speaker; built first app at age 13; B.S. Engineering and Computer Science from Stanford

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