“The home of the AI Workforce” / “Scale GTM results, without scaling headcount”
Relevance AI is a low-/no-code platform for building, orchestrating, and operating AI agents and multi-agent workforces. The current focus is strongly on go-to-market processes such as sales, marketing, CRM enrichment, outbound, inbound qualification, customer success, and support automation.
Relevance AI
The home of the AI Workforce / Scale GTM results, without scaling headcount
Location: Australia ⓘ Australia. The contracting party is OnSearch Pty Ltd, an Australian company with ABN 33 637 909 409,
Team For teams with larger workloads; includes everything in Pro plus Credit Rollover, five Build Users, End Users, multiple Shared Projects, Calling & Meeting Agents, A/B Testing, Analytics Dashboard, and Priority Support.
Enterprise Custom enterprise plan with unlimited users and projects, Enterprise App Triggers, Agent Evaluations, Work Hour Controls, Multi-Org Management, Enterprise Security, Dedicated Account Manager, Custom Implementation, and Priority Early Access. Other Actions Usage units for agent and workflow executions; different monthly or annual quotas are included depending on the plan.
Vendor Credits Prepaid/quota model for third-party AI model usage; according to the Terms, Vendor Credits can be used for Third-Party-AI-Model-Usage and are tied to an active subscription.
Bring Your Own LLM Users can connect their own LLM accounts or API keys, for example for OpenAI, Anthropic, or Google; processing then additionally depends on the respective provider and its DPA/Terms.
Enterprise Data Retention Enterprise feature for the automatic control of the retention and deletion of Tool Runs, Agent Conversations, Workforce Tasks, and Asset Versions; according to the documentation, it must be activated by Relevance AI.
Target audience
Relevance AI is aimed at companies, revenue teams, sales teams, marketing teams, customer success teams, support teams, operations departments, agencies, and technical builders who want to automate recurring business processes with AI agents. The tool is particularly strong for go-to-market organizations that want to scale lead research, CRM enrichment, outbound sequences, inbound qualification, customer communication, and internal workflows without having to hire additional staff.
Outstanding features
The platform combines a low-/no-code agent builder, Workforces as visual multi-agent systems, tools, knowledge, scheduling, approvals, chat, Slack, API integrations, custom apps, and more than 2,000 integrations. Particularly outstanding are the focus on agentic workflows in the GTM area, the ability to equip agents with company knowledge and integrations, as well as enterprise features such as SSO, RBAC, audit logs, data retention, and data residency. Relevance AI also emphasizes that customer data is not used for model training.
Main use cases
Typical use cases include AI BDR, outbound SDR, lead routing, account research, CRM enrichment, inbound qualification, inbox management, customer support, lifecycle marketing, SEO content creation, data analysis, document processing, reporting, and internal workflow automation. Relevance AI is also suitable for teams that want to split more complex processes into several specialized agents, such as research agents, review agents, writing agents, CRM agents, and escalation agents.
Usage & notes
Relevance AI is used via the web platform. Users build agents, tools, and Workforces, connect integrations such as CRM, email, calendar, Slack, or data sources, and define when agents should act automatically or obtain human approvals. For serious productive use, companies should clearly define roles, data sources, API keys, agent permissions, escalations, and retention rules. When handling personal data, customer data, or sensitive business data, region, subprocessors, DPA, SCCs, deletion periods, and enterprise security documentation should be reviewed in advance.
| Target audience | Assessment |
|---|---|
| Private individuals | Rather no – too strongly focused on business, sales, GTM, and workflow automation. |
| Self-employed / freelancers | Conditionally – useful for technically savvy users with recurring sales, research, content, or automation processes. |
| SMEs | Yes – suitable for teams that want to use AI agents for sales, support, marketing, research, CRM maintenance, or internal workflows. |
| Large enterprises | Yes – especially with enterprise features such as SSO, RBAC, audit logs, data retention, multi-org management, security controls, and DPA/trust center documentation. |
| Developers / technical teams | Yes – suitable for API/MCP usage, custom integrations, BYO-LLM, custom tools, Python tool steps, and agentic workflows. |
| Sales and GTM teams | Very well suited – the core target audience is BDR/SDR, outbound, lead enrichment, CRM, meeting, and pipeline automations. |
| Privacy-sensitive organizations | Conditionally to well suited – positives include SOC 2 Type II, GDPR information, encryption, region selection, and no training on customer data; the specific region, DPA, subprocessors, and external LLM providers must be reviewed. |
Hosting & Data
1) On-prem / local hosting
Meaning: The company operates the solution on its own hardware or within its own infrastructure. In the strictest sense, not only the application runs locally, but ideally the model as well.
2) Private cloud / data center
Meaning: The solution runs in a dedicated or more clearly separated cloud environment, often with a hosting provider or hyperscaler, but in a German data center or in a particularly controlled environment.
3) EU SaaS / managed
Meaning: The provider operates the solution itself as a service. The company uses the tool as a ready-made cloud service, ideally with EU data residency.
4) Hybrid
Meaning: One part of the processing remains internal / local / in a private cloud, while another part runs in an external cloud or EU SaaS.
5) AVV / DPA
Meaning: This is the data processing agreement or Data Processing Addendum. It governs that the provider processes personal data on behalf of the customer and is bound by the customer's instructions.
6) No training
Meaning: The provider does not use your prompts, uploads, attachments, chat histories, or outputs for training or improving the general model — ideally excluded by contract.
7) Open-source / transparency path
Meaning: There is a path toward greater technical transparency and sovereignty, for example through:
- open models
- documented components
- self-hostable parts
- traceable architecture
- export / switching options
| On-prem / local hosting | ❓ |
| Private cloud / data center | ✅ |
| EU SaaS / Managed | ⚠️ |
| Hybrid | ⚠️ |
| DPA / AVV | ⚠️ |
| No training on customer data | ✅ |
| Open source / transparency path | ⚠️ |
Overall assessment:
Cloud/SaaS platform for AI agents with multi-region hosting, BYO-LLM, and external integrations; not a classic on-prem or self-hosting offering as a standard product. Relevance AI describes itself as a low-/no-code platform for building AI agents and multi-agent teams that handle tasks such as sales automation, customer support, internal workflows, research, CRM updates, email drafting, and data processing. Hosting model: SaaS/cloud with a multi-region environment; enterprise features include, among other things, SSO, RBAC, audit logs, fine-grained access control, data retention, and security documentation. A publicly documented on-premise or self-hosting offering could not be reliably confirmed in the reviewed official sources. Data processing: Relevance AI distinguishes between Tools, Agents, and Knowledge. According to the security documentation, tool inputs and tool outputs are not logged by Relevance AI; however, individual tool steps may require external providers. Agent conversations are stored for user convenience, are private to the project, and can be deleted. Knowledge data is controlled by the customer and can be deleted; according to Relevance AI, agent and knowledge data are stored in the selected account region. Training on customer data: Relevance AI states that it does not train models on customer data. Integrations: officially mentioned are more than 2,000 integrations as well as connections to, among others, Salesforce, HubSpot, Slack, Gmail, Apollo, Gong, GitHub, Outlook, Notion, Confluence, WhatsApp, Google Sheets, MCP servers, and customers’ own LLM accounts such as OpenAI, Anthropic, or Google. Retention: Free/Pro/Team have plan-dependent task history limits; Enterprise can enable extended data retention features for tool runs, agent conversations, workforce tasks, and asset versions.
Conclusion:
Relevance AI is strong for scalable AI agents and GTM automation, but when handling personal sales, CRM, and communication data, the specific data region, subprocessor list, LLM provider selection, retention, and integration permissions should be properly documented before use. Sources: Relevance AI website, pricing, security overview, data retention, and integration documentation.
| On-prem / local hosting | ❓ |
| Private cloud / data center | ✅ |
| EU SaaS / Managed | ⚠️ |
| Hybrid | ⚠️ |
| DPA / AVV | ⚠️ |
| No training on customer data | ✅ |
| Open source / transparency path | ⚠️ |
Overall assessment:
Cloud/SaaS platform for AI agents with multi-region hosting, BYO-LLM, and external integrations; not a classic on-prem or self-hosting offering as a standard product. Relevance AI describes itself as a low-/no-code platform for building AI agents and multi-agent teams that handle tasks such as sales automation, customer support, internal workflows, research, CRM updates, email drafting, and data processing. Hosting model: SaaS/cloud with a multi-region environment; enterprise features include, among other things, SSO, RBAC, audit logs, fine-grained access control, data retention, and security documentation. A publicly documented on-premise or self-hosting offering could not be reliably confirmed in the reviewed official sources. Data processing: Relevance AI distinguishes between Tools, Agents, and Knowledge. According to the security documentation, tool inputs and tool outputs are not logged by Relevance AI; however, individual tool steps may require external providers. Agent conversations are stored for user convenience, are private to the project, and can be deleted. Knowledge data is controlled by the customer and can be deleted; according to Relevance AI, agent and knowledge data are stored in the selected account region. Training on customer data: Relevance AI states that it does not train models on customer data. Integrations: officially mentioned are more than 2,000 integrations as well as connections to, among others, Salesforce, HubSpot, Slack, Gmail, Apollo, Gong, GitHub, Outlook, Notion, Confluence, WhatsApp, Google Sheets, MCP servers, and customers’ own LLM accounts such as OpenAI, Anthropic, or Google. Retention: Free/Pro/Team have plan-dependent task history limits; Enterprise can enable extended data retention features for tool runs, agent conversations, workforce tasks, and asset versions.
Conclusion:
Relevance AI is strong for scalable AI agents and GTM automation, but when handling personal sales, CRM, and communication data, the specific data region, subprocessor list, LLM provider selection, retention, and integration permissions should be properly documented before use. Sources: Relevance AI website, pricing, security overview, data retention, and integration documentation.
Strengths & weaknesses at a glance
| Strengths | Weaknesses |
|---|---|
| • Very strong for GTM, sales, marketing, and revenue operations workflows | • Strong focus on GTM; less specialized for general enterprise processes outside Sales/Marketing |
| • Low-/no-code creation of agents and workforces | • According to the provider, many enterprise security documents are only made available to enterprise customers under NDA |
| • Broad integration coverage and API/custom integration path | • Complete official company address not clearly verifiable publicly |
| • Free version available for testing | • Depending on usage, region, and subprocessors, data may be processed internationally, including in Australia, the UK/EEA, and the USA |
| • Enterprise features such as SSO, RBAC, audit logs, data residency, and SOC 2 Type II | • No publicly clearly identifiable customer-related AVV/DPA for EU customers within the scope of the research |
| • No training on customer data according to the provider |
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GDPR-compliant usage possible?
Overall assessment: Conditionally to well suited for GDPR, depending on the plan, region, and integrations. Relevance AI cites GDPR compliance, SOC 2 Type II, encryption in transit and at rest, SSO/MFA via identity providers for enterprise customers, RBAC, fine-grained access control, audit/security documentation, and a list of subprocessors in the Trust Center.
Another positive is that, according to Relevance AI’s security documentation, Relevance AI does not train models on customer data and stores agent conversations as well as knowledge data in the region selected by the customer. For enterprise customers, security questionnaires and additional compliance documentation are offered under NDA; the website also mentions DPA templates in the Trust Center.
The negative is: Relevance AI is operated by OnSearch Pty Ltd, an Australian company; the general privacy policy includes EU/UK-specific rights, but does not make a blanket statement that all processing takes place exclusively in the EU. External integrations and LLM steps may involve third-party providers whose own privacy and processing terms also apply.
Server location: Relevance AI describes multi-region deployment and storage in the selected region; specific default regions or automatically guaranteed EU data residency are not publicly and unambiguously assured for every plan. For GDPR-critical use, the enterprise plan, DPA/data processing agreement, region selection, list of subprocessors, BYO-LLM/LLM provider review, retention settings, and access controls should be verified in a binding manner. Sources: Relevance AI Privacy Policy, Security Overview, Data Security Policy, and Pricing.