Relevance AI
Relevance AI is an enterprise platform for building, running and governing specialist AI agents across sales, support, marketing, HR and operations, pairing a no-code builder with evals, multi-model routing, full tracing and role-based governance.
What is Relevance AI?
Relevance AI is an enterprise platform for building, running and governing AI agents, published by OnSearch Pty Ltd, an Australian company founded in 2020 and trading under the Relevance AI name. Its product thesis is narrow on purpose: a specialist agent owns one task, delivers reliable quality on every run, executes on the cheapest model that passes, and runs on its own — the opposite, the site argues, of generalist copilots built for exploration, which need constant steering and pay frontier pricing on every task. A maturity ladder runs from L1 assisted to L4 self-driving, with real business impact placed at L3 and L4.
Three paths lead to the same runtime. Business users compose agents on a visual drag-and-drop canvas; Invent, a text-to-agent generator, writes the system prompt, builds tools from your documents and drafts evals from past runs; engineers drive the same platform over MCP from Claude Code, Codex or Cursor, or through the JavaScript SDK and API. Every path is gated by the same evaluation suites.
The architectural argument is consolidation. Most teams bolt together a router, a queue, an eval tool and a tracer just to keep agents in production; Relevance ships triggers, a shared context layer, an MCP gateway, an LLM router, evals, agent orchestration, a job queue and full tracing as one system. Agents carry long-term project and user memory, hybrid RAG with reranking, sandboxed Python and JavaScript, per-tool approval rules and autonomy step limits. A managed queue adds autoscaling workers, concurrency caps, retries with backoff and a dead-letter queue.
Quality control is the distinguishing mechanism: a version that fails its evals is blocked from publishing, and production runs keep being sampled so drift is flagged before customers see it. Governance follows — role-based access control, SSO and SAML, audit logs, PII masking, approval gates, cost visibility and OTEL export.
Showcased use cases sit mostly in go-to-market: research and enrichment, pre-meeting briefs, post-call actions, meeting scheduling, outbound prospecting, forecast roll-ups, deal review and proposal building. Named customers include Canva, Autodesk, Lightspeed Commerce, Rakuten Advertising, Qualified, Send Payments and Zembl.
What it does
- Build a specialist agent by drag and drop, by describing it in plain language, or over MCP from Claude Code, Codex or Cursor
- Orchestrate several agents as a node-and-edge graph with handoffs, sub-agent calls and parallel branching runs
- Trigger agents from a schedule, an app event, a webhook, an API call or a custom signal, with a managed queue pacing the load
- Run evaluation suites that block a failing version from publishing and keep scoring a sample of production runs
- Route each task to the cheapest model that clears your quality bar, measured across providers
- Gate high-stakes actions behind human approval, mask PII and trace every run, cost and outcome
- Publish an agent as an embeddable widget, a chat bubble, a shareable link or a marketplace listing
When to use Relevance AI / When not to
A quick filter to help you decide if Relevance AI is the right fit.
When to use Relevance AI
- Enterprise go-to-market teams handing repetitive, high-volume work to agents — the platform demo shows 1.24 million tasks run per month at an average of $0.09 each
- Organisations that need governance before autonomy, with role-based access control, SSO and SAML, audit logs, PII masking and a 99.9% uptime SLA advertised as built in
- Domain experts rather than engineers: Relevance AI states plainly that business teams, not developers, should own agent quality and performance
- Teams currently stitching together a model router, a job queue, an eval tool and a tracer, who would rather run one system than five
- Solo GTM operators and small squads, served by the Pro and Team tiers without an enterprise contract
When not to use Relevance AI
- Open-ended exploratory work: the site itself contrasts its narrow specialist agents with tools built for exploration, naming Cowork and Codex
- Anyone hoping to run production workloads on the free plan, which caps at 200 Actions per month, one user, one project, one workforce and 30 days of task history
- Buyers who require single-tenant hosting: the architecture is multi-tenant with logical separation, and single-tenant options are described as still in the works
- Organisations that might need to move data region later, since the region is chosen at signup and cannot be changed once the organisation exists
- Customers expecting human phone support outside an enterprise contract: calls are reserved for Enterprise, and lower tiers get an AI support agent explicitly in beta
How to use Relevance AI
A typical end-to-end flow, from setup to results.
- Create an account at app.relevanceai.com — the free plan requires no credit card
- Choose your data region at signup between US (N. Virginia), EU (London) and AU (Sydney), knowing the choice is final
- Note that the subscription applies at organisation level, not per user or per project, so everyone shares Actions, credits and feature access
- Start an agent one of three ways: the drag-and-drop canvas, a plain-language description handed to Invent, or MCP from Claude Code, Codex or Cursor — or clone one from the marketplace
- Connect your sources and tools: native app connectors, MCP servers, sub-agents, sandboxed Python and JavaScript, and your own model API keys on paid tiers
- Define your tone of voice, business context and knowledge once in the shared context layer instead of repeating it in every agent
- Set triggers: recurring schedules, webhooks, pre-built app events, custom integration crons, bulk runs across datasets or a direct API call
- Write the evaluation set and its thresholds before publishing, so a failing version is blocked automatically
- Tune autonomy: step limits, ask-approval or terminate on limit, conditional approval rules, cost-based pauses and forced retry on error
- Monitor in production through run sampling, drift alerts, full tracing, cost visibility and email or Slack escalations, and use Relevance Chat on the web or the Android and iOS apps
Pros & Cons
Pros
- One stack instead of five: router, queue, evals, tracing, MCP gateway and orchestration ship as a single system rather than an integration project
- Evals are not a side module — they block a failing version from publishing and keep scoring live traffic, which is the hard part of keeping agents in production
- Cost control is explicit and measurable, with routing to the cheapest passing model and Vendor Credits billed at wholesale with no markup
- Genuinely open to non-engineers without shutting engineers out, thanks to MCP, a JavaScript SDK and API integration
- Security posture is documented in detail: SOC 2 Type II, GDPR, three data regions, TLS 1.2+ and AES-256, tenant isolation and a published sub-processor list
- No training on customer data is stated without hedging and repeated across the site and the documentation
- A permanent free plan with no credit card, and a full four-tier price comparison published in the documentation
Cons
- The public pricing page shows only the Enterprise tier: actual prices live in the product documentation, which most visitors will never open
- The governance features enterprises actually shop for — SSO, RBAC, audit logs, agent evaluations, work-hour controls, multi-org — are Enterprise-only, and that tier is quote-only
- No postal address is published anywhere on the site, despite a company name and an ABN being displayed
- The data region is fixed at organisation creation and cannot be changed afterwards without contacting support
- Human support is thin: first-response SLAs run from one to three business days by tier, none at all on free, and hours are 9am to 5pm Sydney time on weekdays only
- Unused Actions and Vendor Credits expire immediately on cancellation or termination and are not refundable
- Terms clause 8.3(a) licences Client Data to improve the Solution, wording broader than the no-training promise, and clause 6.3 guarantees neither data availability nor a usable backup after a system failure
Pricing & Plans
Relevance AI offers a permanent free plan at 0 USD per month, which requires no credit card and includes 200 Actions per month. The lowest paid entry point is the Pro tier at 19.00 USD per month on annual billing, or 29.00 USD per month billed monthly. Usage is metered in two units: Actions, counted whenever an agent runs a tool, and Vendor Credits, which cover third-party model and tool usage at wholesale with no markup.
- 200 Actions per month
- a one-time allocation of 1
- 000 Vendor Credits (a 2 USD bonus)
- unlimited agents and tools
- 1 workforce
- 1 user
- 1 project
- 30-day task history
- everything in Free plus 30
- 000 Actions per year (2
- 500 per month)
- 240 USD of Vendor Credits per year
- unlimited workforces
- 2 build users
- scheduled tasks
- chat mode
- everything in Pro plus 84
- 000 Actions per year (7
- 000 per month)
- 840 USD of Vendor Credits per year
- credit rollover
- 5 build users and 45 end users
- 5 shared projects
- calling and meeting agents
- everything in Team plus custom Actions and Vendor Credits
- unlimited users and projects
- enterprise triggers for Salesforce
- Snowflake and Zendesk
- agent evaluations
- work hour controls
- multi-org management
- SSO
Data, GDPR & hosting
A consolidated view of how Relevance AI handles your data.
GDPR overview
GDPR compliance is claimed explicitly and repeatedly: a GDPR badge appears on the home, product, enterprise and agents pages, every pricing tier lists SOC 2 and GDPR compliance, and the security documentation devotes a section to it. Section 13 of the privacy notice sets out EEA and UK data subject rights — access, rectification, erasure, objection, restriction, portability, withdrawal of consent and complaint to a supervisory authority — and a table maps each processing purpose to its lawful basis. Transfers outside the EEA and UK rely on adequacy decisions under Article 45 or on safeguards under Article 46(2), namely Standard Contractual Clauses; transfers to California are named. Sub-processors operate in the United Kingdom, the EEA, the United States and Australia. The single contact point is privacy@relevanceai.com. No Article 27 EU representative and no data protection officer are named.
Who owns the data?
Under the terms, you keep ownership of what you upload: the security documentation states that all data uploaded to Relevance AI remains your property, and you can export it as CSV, Excel or JSON at any time. In exchange, clause 8.3(a) grants OnSearch Pty Ltd and its personnel a non-exclusive, royalty-free, non-transferable, worldwide and irrevocable licence to use Client Data as reasonably required to provide and to improve the Solution. Relevance AI retains ownership of its own Software Content, including the software, text, graphics and logos. Account deletion is processed within 60 days, but clause 4(b) ends any obligation to retain user data once the billing cycle following cancellation closes.
Reuse rights
You may reuse your own data freely, without asking permission: it stays your property, export in CSV, Excel or JSON is available at any time, and knowledge bases, agent logs and files remain under your control for retention and deletion. What the vendor may do with it is narrower than the licence text suggests at first reading. Relevance AI states it does not use customer data to train its models or improve its services unless a specific partnership agreement exists, and repeats that it never trains models on customer data; metadata may improve features such as search, never training. Tool inputs and outputs are not logged. Default third-party models are GPT from OpenAI and Claude from Anthropic, both listed as not logging and not training, with a DPA in place. Against that, clause 8.3(a) licences Client Data to provide and improve the Solution, and clause 8.3(c) lets the vendor remove Client Data it judges inappropriate or illegal.
Data retention & training
Hosting summary
Data is stored in one of three regions chosen at signup: US (N. Virginia), EU (London) or AU (Sydney). The choice is permanent — it cannot be changed after the organisation is created. Agent conversations and Knowledge data stay in the selected region. Infrastructure runs on a cloud IaaS provider, and Relevance AI additionally self-hosts OpenAI and open-source models such as LLaMA and Fireworks inside its own multi-region AWS and Azure environments. The architecture is multi-tenant with logical separation; Enterprise customers with fine-grained access control get a separate service and database, and single-tenant options are described as still in development. Encryption is TLS 1.2 or above in transit and AES-256 at rest, with automatic encrypted backups across multiple availability zones and network isolation through custom VPCs and private subnets. Terms clause 6.3(a) allows cloud storage outside Australia. Sub-processors and operations sit in the United Kingdom, the EEA, the United States and Australia, and the sub-processor list is published on the trust centre.
Things to keep in mind
Risks and trade-offs to weigh before adopting Relevance AI.
- Delegating judgement, not just busywork: the platform is designed to move work from copilot to autopilot, and teams that stop reviewing what agents decide will lose the tacit knowledge that let them spot a bad output in the first place
- The eval score is a proxy, not the truth — a 96% pass rate reads as reassurance, and it is easy to forget that the remaining 4% reaches real customers unreviewed
- Cost optimisation pulls toward the cheapest passing model, a quiet pressure to lower the bar rather than raise the budget when a task starts failing
- Data ownership fine print: clause 8.3(a) licences Client Data to provide and improve the Solution, wording broader than the no-training promise, and clause 8.3(c) lets the vendor remove Client Data at its discretion
- Continuity risk: clause 4(b) ends any obligation to retain user data after the billing cycle following cancellation, clause 6.3(c) guarantees no usable backup after a system failure, and liability is capped at the fees paid in the preceding three months
- Lock-in by geography: the data region is chosen at signup and cannot be changed, so a compliance requirement discovered later becomes a migration problem
- Recourse is hard: no postal address is published, disputes are governed by New South Wales law with mandatory prior mediation, and the vendor may suspend or cancel an account at its absolute discretion
Setup & Integrations
Technical difficulty
Low to moderate for a first agent, higher for production. Signup needs no credit card, agents can be cloned from the marketplace, and two of the three build paths require no code — a drag-and-drop canvas, or a plain-language description handed to Invent, which writes the system prompt, builds tools from your documents and drafts evals from past runs. Engineers get MCP, a JavaScript SDK, API integration and sandboxed code. The real effort sits downstream: defining evaluation sets, autonomy limits, cost thresholds and approval gates. Relevance AI sells a six-week embedded deployment programme for exactly that reason.
Deployment
Apps stores
Integrations
Behind Relevance AI
Fundraising
Social
Resources
All the official URLs gathered for verification and reference.
Alternatives
Tools that compete with or complement Relevance AI.
Frequently asked questions
Is there a free plan, and does it require a credit card?
How much does the first paid tier cost?
What are Actions and Vendor Credits?
Is my data used to train AI models?
Where is my data hosted, and can I choose?
What certifications and compliance does Relevance AI hold?
Is there an API, an SDK and a mobile app?
How many integrations are available?
What support can I expect, and how long is my data kept?
Should you pick Relevance AI?
Relevance AI is one of the more coherent answers to the question that follows a successful agent prototype: how do you keep it working? Its bet is that the hard part is not building an agent but proving it still behaves next month, and the product is shaped accordingly. Evaluation suites gate every publication and keep scoring sampled production traffic; a model router picks the cheapest option that clears your quality bar; a managed queue absorbs load spikes; tracing shows what each task actually cost. Bundling triggers, context, evals, routing, orchestration and observability into one system spares teams an integration project they would otherwise assemble from half a dozen vendors.
The maturity signals are real. SOC 2 Type II, GDPR, three selectable data regions, a published sub-processor list, an unusually blunt no-training commitment, named enterprise customers and roughly 37 million raised across three rounds all point to a platform past its experimental phase.
Two reservations deserve weight. The first is commercial opacity: the public pricing page shows only the Enterprise tier, and the genuinely enterprise features — SSO, RBAC, audit logs, agent evaluations, multi-org — sit behind a quote. The real grid exists, but you have to find it in the documentation. The second is corporate opacity: no postal address appears anywhere on the site despite a company name and an ABN being displayed, human support is limited to Sydney business hours, and no Article 27 EU representative is named even though European customers are courted.
For a go-to-market or operations team with volume, compliance obligations and an appetite to own agent quality in-house, this is a serious platform. For a small team wanting a cheap, self-serve automation tool, the free plan is a sandbox rather than a workspace, and the useful tier starts at 19 USD per month.
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