Summary of findings from reviewing happyrobot.ai and product pages. Product overview: HappyRobot presents an AI-native operating system for building, deploying, and supervising autonomous AI workers that perform end-to-end tasks for real-time business operations (initial vertical focus on supply chain/logistics). Key marketed capabilities include real-time decision-making and execution, unified natural-language management across systems, a centralized control-tower or embeddable options, observability and auditing, and claims of high ROI and operational metrics (vendor claims such as “100% response rate”, “0 minutes first response”, “120x ROI”, and “10x cost reduction” appear on marketing materials and should be validated in demos/POC). Architecture & technical claims: The technical overview and blog describe a cloud-native, Kubernetes-based architecture with split orchestration vs durable artifacts, pluggable pipelines for ASR/LM/TTS, vendor-adapter patterns (to swap vendors or self-host), GPU-backed voice nodes, per-tenant routing/data-sovereignty options, SIP/SRTP and WebRTC telephony support, low-latency design, TLS 1.3, OAuth SSO, MFA, and fine-grained RBAC. The auditing system is described as combining LLM, classical ML, and rule-based evaluators with multimodal evaluations (transcripts, logs, voice). The product emphasizes native integrations, APIs/webhooks, and browser agents for sites without APIs. Security & compliance: The site cites SOC 2 and GDPR compliance, TLS everywhere, OAuth SSO, MFA, fine-grained RBAC, and continuous threat monitoring. It references governance features such as versioning/staging, AI auditor oversight, built-in supervision, and smart fallbacks/retries. Product capabilities & use cases: AI workers are described as omnichannel (voice, chat, email), able to access documents and systems in real time, perform advanced reasoning (not script-only), and escalate to humans for exceptions. Use cases and industry pages focus on logistics, supply chain, procurement, customer service, scheduling/interviews/recruiting, accounts receivable follow-ups, and similar operational workflows. The site lists support for 15+ languages. Evidence and gaps: The review identified many marketing and technical claims on public pages and a restricted docs site (docs.happyrobot.ai requires an access code). Notable gaps found: no public pricing or pricing tiers; no full public API documentation (docs site restricted); no detailed public roster of supported integration vendors; no definitive single founding year on the public site (external sources conflict: YC lists 2022, some press say 2023); no public SLA details beyond high-level uptime claims; and no direct public sales contact email/phone (primary contact is via “Book a demo” form). A Series B funding round (~$44M in 2025) is referenced in press/blog posts and site announcements (not verified here beyond those references). Recommendations (next steps to complete information collection): 1) Request access to docs.happyrobot.ai (access code) to obtain API docs and integration guides. 2) Book a demo / contact sales to request pricing/licensing, SLAs, full integrations roster, SOC 2 report and other compliance artifacts (DPA/DPIAs), detailed API docs and sandbox, and performance/ROI benchmarks or customer references. 3) Verify company facts (founding year, leadership) via LinkedIn/Y Combinator/press or ask PR during demo. 4) Request architecture/ops whitepaper and SOC 2 evidence if technical/compliance validation is required. 5) Ask for customer case studies or references to validate vendor ROI claims and operational metrics. The information in this summary is restricted to what was observed on public pages and referenced third-party press links; nothing has been fabricated.