Cassidy

Enterprise AI stalls when the people who understand the work have to wait on IT or engineering to build anything. Cassidy AI's pitch is to let those people build agents themselves, without code, inside IT's guardrails.

It is a no-code platform for building AI agents and workflows grounded in a company knowledge base, deployable inside the tools staff already use, with governance for safe scale. It sits among enterprise agent platforms like Ema, with a focus on business-team self-service rather than engineering.

This review covers how Cassidy builds a safe agent, where it fits a business team, how it is sold, and who will find it more than they need.

Cassidy AI Agent Facts

No-Code AI Agents Grounded In Company Knowledge

Cassidy's premise is that the person closest to a process should be the one automating it, not a queue of engineering tickets. So it lets business users describe an agent or workflow in natural language and build it without code.

What stops that from producing confident nonsense is grounding: agents draw on a company knowledge base, synced, cited and permission-aware, so their outputs reflect how your business actually works rather than a generic model's guess. That grounding is the difference between a demo and something a team will trust.

It is aimed at operationally complex, often regulated industries, insurance, professional services, industrials, healthcare, where the work is document-heavy and accuracy matters, which is also why its governance story is front and centre.

The person closest to a process should be the one automating it, not a queue of engineering tickets. Grounding in company knowledge is what makes the result trustworthy.

How Does Cassidy Build A Safe Agent?

Two things make it enterprise-credible: the context it draws on, and the controls around it.

Knowledge Base, Meetings And Context

You connect documents, meetings, CRM and other company knowledge into a knowledge base that grounds agents, with citations so outputs are traceable, and Cassidy can join and summarise meetings so conversations become searchable context and follow-ups.

That context layer is what separates a grounded agent from a generic chatbot. For the browser-automation corner of getting work done, a tool like Axiom.ai handles a different mechanism; Cassidy's strength is reasoning over your own knowledge.

No-Code Agents With Governance

You build agents and workflows in natural language, deploy them inside Teams, Slack, Chrome and Office, and IT keeps control through permissions, row-level security, usage analytics and model-agnostic routing that picks an appropriate model per task. It states SOC 2, GDPR and HIPAA compliance and no training on your data.

That governance is the real unlock for enterprise self-service, letting non-engineers build without letting things sprawl. A general AI assistant like Cora serves the individual; Cassidy is built for the organisation, with the controls that implies.

Where Cassidy Fits A Business Team

Cassidy fits mid-size to large organisations in document-heavy, exception-heavy fields, insurance underwriting routing, professional-services proposals, industrials knowledge search, where business teams want to automate real processes and IT needs to keep it safe.

It suits functions across sales, support, operations and marketing that have clear, repeatable work to automate and the company knowledge to ground it in. The deploy-where-you-work model matters, because adoption dies when a tool is yet another tab.

It fits less well a solo user or tiny team wanting a personal assistant, where a consumer AI tool is simpler and cheaper, and anyone without organised company knowledge to ground agents in, since the grounding is what makes it worth the governance overhead.

How Cassidy Is Sold To Enterprises

Cassidy is enterprise and demo-led: engagement runs through a get-a-demo path with a solutions team and managed builds, rather than public self-serve pricing. We do not publish prices; there are none listed.

For a buyer, the value question is whether business-team self-service, within governance, genuinely reduces the engineering bottleneck and automates enough real work to justify an enterprise commitment, which a scoped pilot on real processes can answer.

Its adoption and case-study figures (workflows run, employees onboarded) are vendor-stated; treat them as claims and judge the platform on your own processes and your own knowledge base.

A Careful Way To Trial Cassidy

Because the value is grounded, governed automation, trial it on a real process with your own knowledge and IT involved.

1. Pick one real, repeatable process, like routing submissions or drafting a proposal section.
2. Connect the relevant company knowledge and confirm the agent cites it correctly.
3. Have a non-engineer build the agent in natural language to test the no-code claim.
4. Deploy it inside the tool the team actually uses (Teams, Slack or Office) and watch adoption.
5. Have IT verify permissions, data handling and the compliance posture meet your rules.
6. Pass/fail: Cassidy is worth it if business teams build useful, grounded agents without losing IT control.
7. Treat the workflow and onboarding stats as vendor claims, not guarantees.

Cassidy AI, The Agent Brief

• Cassidy AI is a no-code AI-agent platform: business teams build agents and workflows grounded in company knowledge.
• Grounding is the point: a knowledge base feeds agents real context, so outputs reflect how your business works.
• It deploys where you work: inside Teams, Slack, Chrome, Word, Excel and Outlook, not a separate app.
• Governance is built in: permissions, usage analytics and SOC 2, GDPR and HIPAA, for safe enterprise scale.

Cassidy AI Agent Terms

Knowledge base

The connected company documents and data that ground an agent's answers, kept synced, cited and permission-aware.

No-code agent

An AI agent built by describing it in natural language, without writing code or an engineering ticket.

Model-agnostic routing

Automatically choosing an appropriate model per task from several, so you are not locked to one and can swap without rebuilding.

Row-level security

Access control that limits which records a user or agent can see, so grounding respects permissions.