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A no-code AI platform that puts a shared company Knowledge Base at the centre of every assistant and workflow — strong for ops, CS, sales, HR and marketing teams, with a free entry tier but quote-based paid pricing that buyers should weigh carefully.
Each score reflects our own editorial assessment against the six-dimension framework on our methodology page. Cassidy loses most ground on pricing transparency: the vendor has moved paid plans to a sales-led, quote-only model, which is harder for buyers to evaluate than fully public pricing. It scores best on ease of use and on the way its Knowledge Base ties assistants and workflows together.
Every agent on AI Agent Square is assessed independently by our editorial team across six dimensions: features and capabilities, pricing transparency, ease of onboarding, support quality, integration breadth, and real-world fit. We draw pricing and feature claims from the vendor's own current documentation and corroborating third-party sources, and we mark anything we cannot confirm as qualitative rather than stating it as fact. Scores are our editorial opinion and are updated when vendors ship material changes.
Cassidy uses an AI-credit model: every plan includes a monthly credit allocation, and credits are consumed as assistants and workflows run tasks. As of July 2026 the vendor's pricing page publishes a free Starter plan and moves paid Business and Enterprise plans to quote-based, sales-led pricing — it no longer lists fixed monthly dollar figures for paid tiers.
| Plan | Price (Jul 2026) | AI credits / mo | Seats | Key inclusions | Best for |
|---|---|---|---|---|---|
| Starter | Free | 10,000 | 3 | 1 workspace, large Knowledge Base allowance, 5 agents, 5 workflows, standard integrations, 24-hour syncing, limited meeting recorder | Pilots, small teams, evaluation |
| Business | Custom (quote) | Custom | Custom | All platform features, premium onboarding & support, advanced integrations, instant syncing, custom event triggers | Growing ops / RevOps teams |
| Enterprise | Custom (quote) | Custom | Custom | SSO, advanced RBAC, dedicated CSM, white-glove onboarding, all deployment options, live syncing | Larger, security-conscious orgs |
A note on older price figures. Several third-party directories still cite fixed Cassidy tiers such as $49, $149 or $499 per month. Those figures do not match the vendor's current pricing page, which as of July 2026 shows a free Starter plan and quote-based Business and Enterprise plans. We have removed the fixed-dollar paid tiers from this review rather than repeat numbers we cannot verify against the live pricing page. If you need a hard budget, request a written quote from Cassidy and confirm the credit allocation, seat count and Knowledge Base capacity in the order form.
Credits are Cassidy's universal unit of consumption. Each month your plan grants a set number of credits, and they are deducted as assistants and workflows run tasks. Per Cassidy's own credits explainer, an agent chat typically consumes on the order of 1–30 credits and a workflow on the order of 1–100 credits, scaling with prompt size, response length, Knowledge Base access and bulk-processing volume. Model choice matters too: Cassidy notes that premium-tier models consume roughly 5x more credits than standard-tier models. The free Starter plan includes 10,000 credits per month; paid plans allocate more, and teams that run low can request additional credits from Cassidy rather than buying top-ups through a public self-serve page.
For buyers, the practical implication is the same as with Gumloop, Relevance AI and most 2026 AI-agent platforms: your real monthly cost is a function of usage complexity, not just seat count. A team running many long-context workflows against a large Knowledge Base on premium models will burn credits far faster than a team using short standard-model agent chats. Before committing, model your expected volume on the free tier, watch the credit meter, and use that as the basis for a Business-tier quote.
Cassidy AI is a no-code workflow automation platform that builds AI assistants and multi-step workflows on top of a shared company Knowledge Base. Rather than positioning itself as a single chatbot or a generic automation router, Cassidy structures the whole product around a simple idea: give AI real, current context about your business, then let anyone on the team point that context at repetitive work. Per Cassidy's own positioning, the platform is organised around four core objects — a Knowledge Base, Assistants, Workflows and Integrations — that combine so business teams can automate work without writing code.
The company is a New York–based startup led by co-founder and CEO Justin Fineberg, and it raised seed funding to build automations powered by internal company data. Its market is squarely non-developer business teams: operations and RevOps, customer success and support, sales, HR and people operations, and marketing. The pitch to those buyers is that they can replace a scatter of disconnected AI experiments — one team using a chatbot here, another pasting documents into a general model there — with a single platform where every assistant and workflow draws from the same verified source of truth. Cassidy also emphasises security and its use of enterprise cloud AI infrastructure, which matters to the mid-market and enterprise buyers it targets.
What distinguishes Cassidy from simpler tools is that the Knowledge Base is not an afterthought bolted onto a chatbot. It is the spine of the product. Assistants answer from it. Workflows read and write against it. And because it syncs with the systems where your documents actually live, the answers reflect the latest versions of your files rather than a stale snapshot uploaded months ago. That design decision is the single most important thing to understand about Cassidy, and it is what the rest of this review keeps returning to.
Cassidy's Knowledge Base centralises company knowledge across dozens of tools to give AI automations real-time context on the business. According to Cassidy's Knowledge Base product page, it connects internal data sources — documents, websites, meetings and tools — and keeps them in sync so the AI always has current context. Native connections cover the systems most companies already store knowledge in: Google Drive, SharePoint, OneDrive, Notion and Confluence, among others. Instead of manually curating a corpus, teams connect the sources they trust and let Cassidy handle the ingestion and refresh.
Two design details make this more than a vector-database-with-a-nice-name. First is syncing cadence. The Knowledge Base refreshes against external sources so it reflects the latest versions of files — on the free tier this is periodic (Cassidy cites 24-hour syncing at Starter), and higher tiers move to instant or live syncing. For a customer-success team answering questions from a product wiki that changes weekly, the difference between a day-old sync and a live one is the difference between a right answer and a confidently wrong one. Second is Document Verification: teams can flag potentially outdated content so assistants only rely on information a human has confirmed is trustworthy. That is a meaningful trust primitive, because the failure mode everyone fears with internal-knowledge AI is an assistant citing a deprecated policy or an old price list with total confidence.
In our assessment, this is the cleanest implementation of "shared organisational memory for AI" in the current crop of business automation platforms. It is the reason Cassidy feels less like a workflow toy and more like a system of record for how a team's AI understands the company. The trade-off is that the Knowledge Base is only as good as the sources you connect and the discipline you bring to verification — connect a messy, contradictory set of documents and the assistants will faithfully reflect that mess. Cassidy gives you the tools to keep the corpus clean; keeping it clean is still organisational work.
Assistants are the interface most users touch first. Per Cassidy's assistants product page, you build personalised AI assistants trained for specific tasks across your team — answering RFPs, resolving customer tickets, fielding internal HR questions, or handling sales discovery. Each assistant can pull from the Knowledge Base, call connected tools, and respond inside the surfaces people already use. The build experience is deliberately approachable: you describe what the assistant should do, point it at the relevant knowledge and tools, and refine its behaviour through natural-language instructions rather than code.
The strength of the assistant model is specificity. Rather than one generic company bot that does everything adequately, Cassidy encourages many narrow assistants that each do one job well — an RFP assistant that knows your security posture and product capabilities, a support assistant that knows your help centre and past tickets, an onboarding assistant that knows your HR policies. Narrow assistants are easier to trust, easier to evaluate, and easier to hand to a specific team. The limitation is that building good narrow assistants takes thought about scope and knowledge boundaries; teams that spin up a dozen overlapping assistants without clear ownership can end up with sprawl. This is a governance question more than a product one, but it is worth planning for before you scale.
Where assistants handle conversational, on-demand tasks, workflows handle structured, multi-step processes. Per Cassidy's workflows page, the Workflow Copilot helps users build automations step by step — structuring the logic, connecting the tools, and refining the details — with no coding required. Cassidy ships a large library of pre-built actions: generate content, search the Knowledge Base, send emails, update CRM records, branch on conditions, loop over lists, call external APIs. Multi-step workflows can therefore handle genuinely complex processes — enriching and routing inbound leads, drafting and logging outreach, triaging support tickets, assembling first-draft RFP responses — without pulling in an engineer.
The Workflow Copilot is the feature that most reduces the intimidation factor of automation. Building a branching, tool-calling workflow from a blank canvas is daunting for a non-technical operator; describing the outcome you want and letting the Copilot scaffold the steps is far more approachable. In practice, the Copilot gets you most of the way and you refine from there, which is exactly the right division of labour. The honest caveat is that the most bespoke or brittle workflows — ones with unusual branching, tight error-handling requirements, or deep custom-system logic — can still hit the ceiling of a no-code tool, at which point you reach for webhooks, the API, or an engineer. Cassidy provides those escape hatches, but if your automation ambitions are heavily custom, evaluate them against a code-first tool like n8n before committing.
The single most consequential UX decision in Cassidy is that assistants deploy where work actually happens. Slack and Microsoft Teams are first-class surfaces; Chrome, Word, Excel and Outlook extensions cover the rest of the typical knowledge-worker stack. This matters more than it sounds. The reliable adoption pattern for internal AI is "ask the assistant inside the tool you already have open," not "remember to visit a separate web app." A support agent who can query the assistant without leaving Slack, or a salesperson who can trigger a workflow from Outlook, will actually use it; the same person told to open a fifth browser tab often will not. By meeting users inside Slack, Teams and the Office suite, Cassidy removes the largest source of adoption drop-off in this category.
Cassidy's integrations split into two categories. Knowledge Sources feed the Knowledge Base — Google Drive, SharePoint, OneDrive, Notion, Confluence and similar systems. Workflow Actions let assistants and workflows do things in other tools — Salesforce, HubSpot, Slack, Microsoft Teams, Intercom, Pipedrive and many more. Per Cassidy's integrations documentation, the platform offers native connections across both categories plus webhooks and an API for anything not covered out of the box. In everyday terms, that means a single workflow can read from your document store, enrich a record, update your CRM and post to a Slack channel without leaving Cassidy.
For the mid-market SaaS stack — a CRM, a support desk, a document store, a chat platform — Cassidy's native coverage is strong and the pairing of Knowledge Sources with Workflow Actions is coherent. The gaps show up at the heavy end of the enterprise: deep integrations with systems like SAP or Oracle are lighter than the mainstream SaaS connectors, so organisations whose critical processes run through those systems should confirm exactly what is supported before assuming Cassidy can automate against them. The webhook-and-API escape hatch means most gaps are bridgeable with engineering effort, but "bridgeable with engineering" is a different proposition from "native and no-code," and buyers should price that distinction into their evaluation.
Cassidy's onboarding is among the lightest in the category, which is a direct benefit of the free Starter tier and the no-code build experience. A typical path is: sign up, connect your first knowledge sources (Drive, SharePoint or Notion), build a first assistant, and deploy it to Slack or Teams. Because the free tier includes real credits and meaningful Knowledge Base capacity, teams can stand up a working production-grade assistant and validate it against real questions before any procurement conversation. From there, the Workflow Copilot smooths the step up from single assistants to multi-step workflows.
The harder work is not technical, it is behavioural. The platform's value compounds only when team members build the habit of asking the assistant first, contribute to and maintain the Knowledge Base, and flag outdated content through Document Verification. Cassidy's design supports these habits — deployment inside Slack and Teams lowers the friction, and Document Verification gives people a concrete action — but the cultural change is on the customer. Organisations that assign clear ownership for the Knowledge Base and for each assistant tend to see adoption stick; those that launch assistants without an owner tend to see them decay. Budget for that ownership as part of the rollout, not as an afterthought.
Cassidy operates as a SaaS platform and markets enterprise-grade security, with governance controls concentrated in its paid tiers. Single sign-on (SSO) and advanced role-based access control (RBAC) are Enterprise-tier capabilities, and live syncing and the fuller set of deployment and administration options also sit at the top of the range. For regulated industries — healthcare, finance, legal — the right approach is to review Cassidy's published security documentation, data-processing terms and any relevant certifications directly against your specific obligations, and to confirm in writing which controls are included in the tier you are quoted. If you handle protected health information, confirm the availability of a Business Associate Agreement before contracting. None of this is unusual for the category, but because Cassidy's paid pricing is quote-based, it is especially important to get the security and compliance inclusions itemised in the proposal rather than assumed.
Cassidy is covered favourably across independent tool directories. Listings on Futurepedia, ColdIQ and Coda One position it as a workflow automation platform for operations and customer-facing teams, and Cassidy's own materials cite adoption across thousands of teams including larger enterprises. We report those directory positions and vendor adoption claims as attributed context rather than as our own verified metrics — buyers should weight independent, hands-on evaluation (which the free tier makes easy) above any single directory listing or vendor headline. The recurring themes in third-party coverage align with our own read: fast time-to-first-assistant, strong Knowledge Base ergonomics, useful Slack and Teams deployment, and a no-code Workflow Copilot on the positive side; credit-cost unpredictability on heavy months and lighter deep-enterprise integrations on the cautionary side.
Support and CS teams build assistants that answer tickets, draft RFP responses and surface product knowledge from the Knowledge Base — cutting response times while keeping answers grounded in verified, current documentation rather than a rep's memory.
Sales teams use assistants and workflows to qualify inbound leads, draft personalised outreach, answer discovery questions from a shared knowledge base, and update CRM records in Salesforce or HubSpot without leaving Slack or Outlook.
People teams stand up onboarding and policy assistants that field employee questions from an always-current HR knowledge base, reducing repetitive Q&A while Document Verification keeps answers aligned to the latest approved policies.
Marketing teams run content-research, brief-generation and repurposing workflows that draw on brand guidelines and prior assets in the Knowledge Base, keeping tone and facts consistent across a growing body of work.
Also worth a look depending on your priorities: Lindy for email and meeting automation, MindStudio for building custom no-code AI apps, Notion AI if your knowledge already lives in Notion, and Microsoft Copilot Studio for organisations standardised on Microsoft 365. For a broader map of the space, browse the full workflow automation category.
Cassidy AI is a well-designed choice for business operations, customer-success, sales, HR and marketing teams that want AI assistants and workflows built on a genuinely shared, always-current Knowledge Base. Treating the Knowledge Base as a first-class object — syncing Drive, SharePoint, OneDrive, Notion and Confluence, with Document Verification to keep answers trustworthy — is the differentiator that sets it apart from Zapier-style flow tools and generic chatbots. The no-code Workflow Copilot and native deployment inside Slack, Teams and the Office suite mean teams actually adopt it, which is where most internal-AI projects quietly fail. A usable free Starter tier lets you prove value before you spend.
The one real caveat is pricing transparency. Cassidy has moved paid Business and Enterprise plans to quote-based, sales-led pricing, so there is no public dollar figure to plan against, and the AI-credit model means your true cost scales with usage complexity rather than seats alone. Older third-party listings that still show fixed $49–$499 tiers no longer reflect the vendor's current page, and we have removed those figures rather than repeat unverified numbers. For teams that fit its shape, Cassidy earns a strong recommendation — start on the free tier, model your credit consumption against real work, and take those numbers into a quote conversation. For code-first developer teams, simple-connector use cases, or buyers who require fully public pricing, look at n8n or a more transparent alternative instead.
The most reliable way to price Cassidy is to build a real assistant on the free Starter plan, watch your credit consumption against actual work, and take those numbers into a Business or Enterprise quote conversation. Compare it against the rest of the category before you commit.