The Bottom Line: Relevance AI isn’t another single-purpose AI agent. It’s the platform where your ops team — not your engineers — builds, deploys, and manages a full AI workforce. Canva, Databricks, KPMG, Lightspeed, and Autodesk are already running production AI agents on it. 40,000 new agents were created on the platform in January alone.
Why This One Matters
There are roughly 500 AI agent startups right now. Most are single-function tools: an AI SDR here, an AI support bot there. Fine. But that’s not how real companies actually work.
Real companies need AI agents across sales, marketing, support, research, and ops — all connected, all using the same knowledge base, all managed by the people who actually understand the workflows. Not by an engineering team that’s already buried in product work.
That’s the gap Relevance AI fills. It’s a visual, no-code platform where subject-matter experts build the agents themselves. Your head of sales builds the BDR agent. Your marketing lead builds the lifecycle agent. Your support manager builds the ticket-routing agent. Then they wire them together into multi-agent teams that collaborate — like actual employees — to complete complex end-to-end processes.
This isn’t theoretical. Canva is using it to redesign how their entire go-to-market org works. KPMG is turning AI agents into an operating model. Lightspeed Commerce built an AI-powered GTM motion on it.
The Founder Story
Relevance AI was founded in 2020 in Sydney by Daniel Vassilev and Jacky Koh (co-CEOs) along with co-founder Daniel Palmer. They were early — building AI agent infrastructure before “agentic AI” was even a term VCs used in pitch decks.
They started as a data analytics platform, then pivoted hard into AI agents as the opportunity became clear. The bet paid off. Series A in December 2023 ($15M, led by King River Capital with Insight Partners and Peak XV). Then a $24M Series B in May 2025 led by Bessemer Venture Partners, bringing total funding to $37M+.
The company has scaled from 19 employees in 2023 to 80+ across Sydney and San Francisco. Vassilev relocated to SF to build the US go-to-market team — the kind of move that tells you they’re serious about enterprise adoption in the US market.
What I respect about this team: they were building agent infrastructure before the hype cycle. CB Insights named them a “Leading Enterprise Agent Vendor.” Everest Group called them an “Agentic AI Luminary.” Forbes Asia put them on their 100 to Watch list. They earned those labels through actual customer deployments, not demo videos.
What Actually Makes It Different
Here’s the core problem with most AI agent tools today: they require engineering resources to build and maintain. Which means they become another project in the engineering backlog. Which means they don’t get built, or they get built wrong because the people coding them don’t understand the actual workflow.
Relevance AI flips this. Here’s how:
“Invent” — Text-to-Agent Generation: Describe what you want an agent to do in plain English. The platform generates a specialized agent in minutes. Not a toy demo. A production-ready agent with tools, triggers, and knowledge connections. This is the kind of thing that actually gets ops leaders to build rather than spec out requirements for engineers who are 6 weeks behind.
Multi-Agent “Workforce” Builder: This is the real unlock. A visual, no-code system where you design teams of specialized agents that collaborate. Think of it like an org chart, except every role is an AI agent. One agent does prospect research. Another qualifies leads. A third writes personalized outreach. A fourth manages the inbox. A manager agent orchestrates the whole thing. This mirrors how actual teams work — and it’s the first platform I’ve seen that nails this at the ops level without requiring dev resources.
Model Agnostic: Relevance AI works across OpenAI, Anthropic, Google, and Meta models. You’re not locked into one vendor’s ecosystem. Swap models per agent depending on the task. Use Claude for complex reasoning, GPT for content generation, Gemini for something else. Your choice. This matters at enterprise scale.
Deep Integration Layer: CRM connections, API integrations, custom code execution, knowledge bases, scheduling, approvals, escalation workflows. The stuff that separates production agents from science projects. SOC 2 Type II compliant. Enterprise-grade security. The things Canva and KPMG care about before deploying anything.
The Use Cases That Matter for B2B
Here’s where Relevance AI really shines for SaaS and B2B companies:
AI BDR Agent (Bosh): Engages leads, does prospect research, writes personalized outreach, manages follow-ups, books meetings, updates CRM — all 24/7. Not a “spray and pray” email blaster. An agent that adapts to your specific sales motion and playbook.
Account Research Agent (Apla): Human-quality account research on autopilot. Every call is fully prepped with the right insights. This alone saves your AEs hours per week. MongoDB reported 3,000% ROI using Relevance AI’s platform.
Lifecycle Marketing Agent (Lima): Messages every customer like they’re your only customer. Personalized sequences across the full customer journey. The kind of thing a team of 3 marketers would do for your top 50 accounts — now running for every account.
CRM Enrichment, Inbound Qualification, Customer Support, SEO — they have pre-built agent templates across all of these. Clone a template, customize it with your knowledge and workflows, deploy in hours instead of months.
The Numbers
- $37M+ total funding (Bessemer, Insight Partners, Peak XV, King River Capital)
- 80+ employees across San Francisco and Sydney
- 40,000 AI agents created on the platform in January alone
- Enterprise customers including Canva, Databricks, Confluent, Autodesk, KPMG, Lightspeed, Rakuten, Qualified, Freshworks, Activision, Airwallex
- 4.5 stars on G2
- SOC 2 Type II compliant
- Pricing starts free — Pro at $19/mo, Team at $199/mo, Business at $599/mo, Enterprise custom
What This Means for B2B Founders
We’re past the point where deploying AI agents is optional. Shopify, Box, Duolingo — the CEOs running the best companies are issuing AI-first mandates. The question isn’t whether you’ll build an AI workforce. It’s whether you’ll build it with a platform designed for it, or cobble together 15 different point solutions that don’t talk to each other.
Relevance AI is one of the few platforms I’ve seen that actually treats AI agents as first-class team members rather than glorified automations. The multi-agent system builder, the approvals and escalation workflows, the scheduling controls, the version control for agents — this is the infrastructure you need to go from “we have a cool demo” to “we have AI agents running production workflows across the company.”
The Verisoul case study nails it: their 2-person growth team started operating like a team of 20 after deploying Relevance AI. That’s the kind of leverage every SaaS founder should be thinking about right now.
Try it: relevanceai.com
Meet them: SaaStr AI 2026 | May 12-14 | SF Bay Area
SaaStr AI App of the Week is a weekly series highlighting the most interesting AI tools actually being used in production by B2B companies. Not demos. Not pilots. Actually deployed, actually working, actually generating ROI.
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