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Both platforms put AI agents to work in your business. What they mean by that is very different. One is a finished product for business teams; one is developer infrastructure.
$100 in free credits
No card required.Relevance AI gives technical teams the infrastructure to build AI agent workflows. Appy.ai gives business teams a named team of AI specialists, ready on day one.
Appy.AIRelevance AIAppy.AIRelevance AINamed team of AI specialists led by Violet, ready to work immediately.
Developer platform for building custom AI agent workflows from scratch.
Named team of AI specialists led by Violet, ready to work immediately.
Developer platform for building custom AI agent workflows from scratch.
Business teams in marketing, sales, ops, and finance who want results without building.
Engineers and technical builders who want to design their own agent infrastructure.
Business teams in marketing, sales, ops, and finance who want results without building.
Engineers and technical builders who want to design their own agent infrastructure.
Connect Slack or Teams and start delegating. No build phase required.
Build workflows, define agents, configure tools, and iterate before getting value.
Connect Slack or Teams and start delegating. No build phase required.
Build workflows, define agents, configure tools, and iterate before getting value.
None. Non-technical founders and department heads can start the same day.
Engineering resources required to design, build, and maintain agent systems.
None. Non-technical founders and department heads can start the same day.
Engineering resources required to design, build, and maintain agent systems.
Named agents per function: Marcus, Maven, Paige, Scout, Piper, Sage, and more.
You define agent roles, capabilities, tools, and memory systems yourself.
Named agents per function: Marcus, Maven, Paige, Scout, Piper, Sage, and more.
You define agent roles, capabilities, tools, and memory systems yourself.
Slack and Teams natively. AI colleagues that live where your team already works.
APIs, custom interfaces, and embedded apps you build and maintain.
Slack and Teams natively. AI colleagues that live where your team already works.
APIs, custom interfaces, and embedded apps you build and maintain.
Agents share memory across your business. Context builds automatically over time.
Developer-configured memory systems you design and maintain.
Agents share memory across your business. Context builds automatically over time.
Developer-configured memory systems you design and maintain.
Violet routes work to the right specialist automatically.
Build your own orchestration logic to coordinate agents and handoffs.
Violet routes work to the right specialist automatically.
Build your own orchestration logic to coordinate agents and handoffs.
Deliverables ready to review: reports, decks, spreadsheets, drafted content.
Depends entirely on how you build and configure your agent system.
Deliverables ready to review: reports, decks, spreadsheets, drafted content.
Depends entirely on how you build and configure your agent system.
Teams that want AI specialists working now, not after a build cycle.
Teams building custom internal AI systems with specific, non-standard requirements.
Teams that want AI specialists working now, not after a build cycle.
Teams building custom internal AI systems with specific, non-standard requirements.
Appy.ai's model is simple: your team gets named AI specialists — each with a defined role, domain expertise, and memory of your business — who work inside the tools you're already in.
Marcus handles market strategy. Maven runs social media. Paige owns content. Scout does competitive research. Piper handles business development. Sage analyzes your financials. Violet coordinates all of them.
You don't configure these agents. You don't write prompts or define tools or design workflows. You ask for work and they do it. The deliverable comes back ready to review, not as a conversation you have to interpret.
Relevance AI is a powerful platform for companies that want to design their own AI agent infrastructure. You define what your agents are, what tools they can use, how they store memory, how they hand off work to each other.
If you have an engineering team and a specific workflow you want to build, Relevance AI gives you the components to do it. The tradeoff: building a working agent system takes engineering time, iteration, and ongoing maintenance.
With appy.ai, there's no build phase. Connect to Slack, and your specialists are already defined and ready. A non-technical founder or department head can have AI colleagues working on their business the same day.
With Relevance AI, the build phase is the product. You're not buying a finished AI team — you're buying the ability to create one. If you have engineers who want that control, it's a strong choice. If you don't, or if you want results faster than a build cycle, it's the wrong tool.
Both run on frontier AI models. The real question is whether you want a partner to think with or a team to hand the work to.
Choose Appy.ai if...
Choose Relevance AI if...
Honest answer: the question isn't which is better. It's which fits your team and what you're trying to do. If you're a business team that wants AI specialists working in your Slack today, appy.ai is built for you. If you're an engineering team that wants to build custom AI agent systems, Relevance AI gives you the infrastructure to do it.
Teams don't switch to Appy.AI because they want to build infrastructure — they switch because they want work done. Here's what they say after Violet becomes part of how they operate.
"I love the product. I can't not use it now... something like this needs to be part of my daily communication."
"Because it's embedded where I work, it feels conversational, like I'm interacting with my team"
"Appy is helping me go from AI chat, to AI co-creator to the next level of AI co-founder"
"I love the product. I can't not use it now... something like this needs to be part of my daily communication."
"Because it's embedded where I work, it feels conversational, like I'm interacting with my team"
"Appy is helping me go from AI chat, to AI co-creator to the next level of AI co-founder"
"I would say Open Claw is like this promising intern - Appy seems like a team of professionals. Delivering PPT without asking was slick!"
"With the ability to create new agents and new workflows and triggers, you've basically created OpenClaw. But it's in Slack and it's secure. I can't get myself in trouble."
"When the specialists go to work, it's going to be better than Chat. It's closer to client ready."
"We're a small business, resources are limited. The opportunity here is that Appy can make our impact larger and more efficient."
"I have a new hire starting next week and I'm going out of the office. It was so easy to take my onboarding program in Notion and just ask Appy to send out Slack messages daily in the new hire's channel..."
"ChatGPT is more of a generalist. Working with Appy I didn't have to do that usual, 'you are an expert and these are your domains of expertise...' It was nice to just be able to give it something and it automatically handed it off to the agent to best handle it."
The questions teams ask most when weighing Appy.ai against Relevance AI. Can't find what you're looking for? Contact us.
Appy.ai is a finished product: a named team of AI specialists you delegate to in plain language, working inside Slack or Teams on day one. Relevance AI is developer infrastructure: a platform for engineering teams to build their own AI agent workflows from scratch.
No. Appy.ai is designed for non-technical business teams. A founder, department head, or team lead can connect to Slack and have their AI specialists working the same day — no build phase, no engineering support required.
Yes. Appy.ai supports creating custom agents and skills on top of the pre-built team. The difference from Relevance AI is that building is optional — you get immediate value from the existing specialists without having to build anything first.
Relevance AI is designed for engineering teams and technical builders who want precise control over how their AI agents work, what tools they access, and how they coordinate. It's a strong choice when you have a specific, non-standard use case and the engineering resources to build it out.
Appy.ai has no build phase — connect to Slack, and your specialist team is ready. Relevance AI requires engineering time to define agents, configure tools, and build workflows before you get working output. For teams that want results quickly, that gap matters.
Appy.ai lives inside Slack and Microsoft Teams as a native presence — you @mention your specialists or DM them like any other colleague. It also works via email, webhooks, and event triggers. Relevance AI works through APIs and custom interfaces you build yourself.
