Regal
Regal is a voice first AI agent platform for contact centers, strong at outbound, pairing a smart dialer with event driven journeys and a unified profile of every customer.
Regal is a voice first AI agent platform built for contact centers, founded in 2020 and based in New York. Its founders come from contact center operations, and that shows in the focus: rather than treating a call as a standalone event, Regal treats every conversation as part of the customer journey. It serves brands in insurance, healthcare, financial services, education and home services, where a well timed call drives revenue.
Regal's emphasis is outbound and event driven engagement. Triggered outbound calls fire when a customer event reaches Regal, batch dialing works through contact lists, callbacks are scheduled, and a Journey Builder sequences calls, texts and emails across channels. Agents are built as single or multi state prompts with branching and chained actions, run on the customer's choice of OpenAI, Anthropic or Google models, answer inbound calls and SIP transfers, and also work over SMS, chat and WebRTC.
Underneath sits a unified contact profile that merges a company's customer attributes, events and conversation history into one record per person, which agents read to personalize each call. Custom actions and more than forty integrations connect agents to CRMs, calendars, contact center platforms and data tools, and knowledge bases hold websites, documents and text in a vector index. Transcripts show every function call, knowledge lookup and state change, and live transfers hand callers to people with context.
Testing before launch is documented: simulation test cases run scripted callers against an agent and score each run pass or fail, and variants A/B test live traffic. An API and a Regal MCP server let teams manage agents, tests and numbers from their own tools. Access runs through SAML single sign on with Okta or Azure, Google SSO and Okta SCIM, and the homepage lists SOC 2, HIPAA, GDPR and CCPA. Pricing is handled through sales. Regal fits contact center teams whose results hinge on pickup rates, conversion and high volume outbound.
Vendor details
Canonical URL
https://regal.ai
Category
Voice agent
Company status
independent
Use cases & customers
In practice
An insurer runs an outbound Regal campaign on a predictive dialer staffed by AI agents that qualify leads, then transfers the strongest prospects to human reps with the full conversation and customer context.
A lender builds a Regal journey that triggers on a missed payment, placing a compliant reminder call whose agent already knows the account history from the unified customer profile.
Before launch, a team runs its Regal agent through simulation based test suites across many scenarios, catching broken logic and prompt regressions so the agent behaves reliably once it goes live at scale.
Agentic Index coverage score
10.5 / 14 capabilities · 75%
| Integrations & Tool Calling | Full |
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Custom actions plus a large catalog. Agents run custom actions that call the customer's APIs, book through Cal.com, Google Calendar and Outlook, update the contact profile and send events, and documented integrations cover Salesforce, HubSpot, Zendesk, Microsoft Dynamics 365, Segment, Braze, Five9, Talkdesk, 8x8 and more than thirty others. SourceRegal, developer.regal.ai (Agent Actions, Custom Actions, integration guides)read 2026-09-28 |
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| Workflow Orchestration | Full |
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Multi state agents and event driven journeys. Multi state agent prompts move a call through defined states, a branching action and action sequences chain steps, drafts and versions govern changes, and the Journey Builder sequences calls, texts and emails across channels on customer events. SourceRegal, developer.regal.ai (Multi State Agent Prompt, Branching Action, Action Sequences, Drafts and Versioning) and regal.ai/journey-builderread 2026-09-28 |
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| Knowledge Grounding & RAG | Full |
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A maintained vector index over the customer's content. Knowledge bases take up to 300 URLs, 25 documents and 50 text snippets each, are chunked semantically into a vector database, re scrape websites on a schedule the customer sets, and are queried before each response; retrievals appear in transcripts as function calls, and knowledge base testing and a coverage gap dashboard sit beside them. SourceRegal, developer.regal.ai (Knowledge Base, Knowledge Base Testing, Post-Call Transcripts)read 2026-09-28 |
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| Human Oversight & Guardrails | Partial |
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Transfers and handoff, no approval step. The Transfer Call action and the Call Handoff API pass a call to a person with context, and the Unified Agent Desktop puts AI and human agents in one workspace. No action waits for a person's sign off before it commits. SourceRegal, developer.regal.ai (Transfer Call, Call Handoff API) and regal.airead 2026-09-28 |
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| Security, Identity & Governance | Full |
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Access controls and a listed attestation. Sign in supports SAML SSO through Okta and Azure as well as Google SSO, with Okta SCIM provisioning and brand scoped OAuth for the MCP server. The homepage lists SOC 2, HIPAA, GDPR and CCPA and says LLM partners may not train on or store customer data, and a trust center sits at trust.regal.ai; no SOC 2 report type or dates appear on the homepage. SourceRegal, developer.regal.ai (Okta SSO, Okta SCIM, Azure SSO) and regal.airead 2026-09-28 |
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| Observability & Auditability | Full |
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Run level transcripts with the actions taken. Post call transcripts show each AI action, meaning each function call and its result, knowledge base queries and state transitions in line with the conversation, and failed actions surface as call level warnings; call observability metrics, transcript search and reporting webhooks sit beside them. SourceRegal, developer.regal.ai (Post-Call Transcripts, Call Observability Metrics)read 2026-09-28 |
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| Memory & State Persistence | Partial |
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The agent reads the customer record across calls rather than keeping a memory of its own. The unified contact profile merges attributes, events and conversation data per customer, and agents read it to personalize each call and can update it through actions. That profile is the business's own customer record, and no separate agent memory with a stated scope and lifetime is documented. SourceRegal, developer.regal.ai (Intro to Regal Data Model, Update Contact Profile, Personalization)read 2026-09-28 |
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| Deployment & Data Residency | Not documented |
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Hosted only; no deployment or region choice documented. The developer documentation (135 pages) covers SIP and contact center connections, WebRTC and concurrency but no private cloud, self hosted or region option, and connecting through SIP to the customer's telephony is not deployment into customer infrastructure. The documentation does not state where data is hosted. SourceRegal, developer.regal.ai (llms.txt, CCaaS / SIP Integration)read 2026-09-28 |
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| Prebuilt Agents, Templates & Packs | Partial |
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Prebuilt actions and use case guides the customer assembles. The docs offer ready actions (store locator, zip code validation, pricing and inventory lookup, calendar booking) and a guide to planning a new agent use case, and the marketing site names vertical uses. No catalog of adoptable prebuilt agents is published. SourceRegal, developer.regal.ai (Agent Actions, Planning a New AI Voice Agent Use Case)read 2026-09-28 |
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| Triggers & Channel Coverage | Full |
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Autonomous wakes on several seams. Triggered outbound calls fire on customer events sent to Regal, batch dialing works through contact lists, scheduled callbacks call back at a set time, inbound voice agents answer numbers and SIP transfers, and agents also run over SMS, chat and WebRTC. SourceRegal, developer.regal.ai (Triggered Outbound Calls, Batch Dialing, Schedule Callback, Deploy Inbound Voice Agent)read 2026-09-28 |
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| Model Flexibility & Routing | Full |
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The customer picks the LLM from several providers. Agent configuration offers OpenAI models (GPT-4o Mini by default, GPT-4.1 and GPT-5 variants), Anthropic Claude 3.5 Haiku, 3.7 Sonnet and 4.0 Sonnet, and Google Gemini 2.5 and 3 Flash, with an open weight model on request and bring your own LLM integrated through Regal. SourceRegal, developer.regal.ai (LLM Models)read 2026-09-28 |
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| APIs, SDKs & MCP Extensibility | Full |
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A documented API and an MCP server that change Regal's own objects. The API reference covers custom events, messages, campaigns, dispositions, users, call handoff and branded phone numbers, and the Regal MCP server at mcp.regal.ai, on OAuth 2.0 through Okta and scoped to the brand, lists tools that create, edit and publish agents, create test cases and manage branded numbers. SourceRegal, developer.regal.ai (API reference overview, Regal MCP)read 2026-09-28 |
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| Testing, Debugging & Optimization | Full |
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An evaluation harness on the customer's agent. Simulation test cases define the simulated caller and success criteria, auto evaluate each run to pass or fail, and run individually or in bulk before go live and after changes; agent variants A/B test live traffic and knowledge base testing checks retrieval. SourceRegal, developer.regal.ai (Simulation Testing, Variants, Knowledge Base Testing)read 2026-09-28 |
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| Browser & Computer Use | Not documented |
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No browser, desktop or computer control. The Press Digit action walks a phone menu by keypad, and the Chrome extension embeds the dialer for human reps, which is the product running in a browser rather than an agent operating one. SourceRegal, developer.regal.ai (Press Digit, Google Chrome Extension)read 2026-09-28 |
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The Agentic Index coverage score grades every vendor Full, Partial or Not documented against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded
Pricing
Custom (contact sales)
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Alternatives to Regal
The closest documented capability profiles to Regal among voice agents tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.
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- Vapi11.5 / 14Adds documented Deployment & Data Residency
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- Phonely11.5 / 14Adds documented Deployment & Data Residency
Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded