Ada vs Relevance AI

Ada is an AI customer service platform from Toronto, founded in 2016 by Mike Murchison and David Hariri. It started as a no-code chatbot builder and has repositioned itself as an agentic customer experience platform: AI agents that answer and resolve customer inquiries end to end across messaging, email and voice, rather than deflecting them to a help article.

The agent orchestrates several large language models, reads your knowledge base, and uses Actions to call your systems so it can look up orders, change bookings or issue refunds. Playbooks encode multi-step standard operating procedures, and the platform includes testing, coaching, analytics and an automated resolution metric to track how many conversations finish without a human. Ada lists SOC 2, HIPAA, GDPR and PCI DSS compliance. There is no self-serve plan: pricing is quote-based after a sales demo.

Ada fits large consumer brands with hundreds of thousands of support conversations a year, such as Pinterest, monday.com, Grab and Life360. Smaller teams usually get faster value from Intercom Fin or Tidio Lyro, which price per resolution and install in an afternoon. Zendesk AI agents make more sense if you want to stay inside one helpdesk vendor, and Decagon and Sierra are the main newer enterprise rivals.

In February 2026 Ada launched a unified Reasoning Engine that powers its agents across all channels from one intelligence layer. When evaluating, ask for a pilot on your own transcripts and compare the automated resolution rate, not deflection, against your current baseline.

Relevance AI is a no-code platform for building AI agents that do business work such as researching prospects, qualifying inbound leads, writing outreach and triaging support requests. The company was founded in Sydney, Australia and also operates from San Francisco, and it markets the product as a way to build an 'AI workforce' of agents that work alongside human teams.

In the builder, teams define an agent's instructions, give it tools assembled from templates or custom steps, connect knowledge and integrations such as CRM, email, web search and spreadsheets, and decide which actions require human approval. Agents can be combined into multi-agent teams where one agent delegates to others, run on triggers and schedules, and be monitored through task logs. Pricing is now sales-led: the public pricing page lists a single Enterprise plan with unlimited agents, tools, users and projects, quoted after a call with the sales team.

Relevance AI fits go-to-market and operations teams that want to experiment with autonomous agents without engineering support. Agent output still needs testing and supervision, usage-based costs can be hard to predict, and the learning curve is real despite the no-code label. Zapier and Make are better for deterministic workflows, n8n gives technical teams more control, and Clay overlaps on prospect research and data enrichment. For sales development pilots, its templates are a practical starting point.