Quickchat AI

What it is

Quickchat AI is a chatbot and AI agent builder aimed at customer support, sales and ecommerce teams. Its own FAQ describes it as "a custom AI Agent platform for customer support, sales, and ecommerce" that builds agents which ground their answers in your docs, take actions through APIs, and deploy across your website, helpdesk, WhatsApp, and Slack. The reader it fits best is a support or growth team that wants a conversational agent working from its own help content, plus an operations lead who wants to try the same agent on internal questions such as HR.

The pages do not say where the company is based, so this entry does not either.

What you build and how

The site frames the product as a "complete AI-first customer service platform": it says Quickchat AI "covers everything from building your AI Agent to deploying it across channels, supporting customers, and analyzing every conversation." In practice that gives you four stages to work through.

Describe the agent. The first block on the homepage is titled "Describe your AI". You set a personality, guidelines and a prompt, so the agent speaks the way your team does and stays inside the rules you give it. There is no flow chart to draw. The agent is defined by instructions and by what you connect to it, which is closer to briefing a new hire than to wiring a decision tree.

Ground it in your content. The agent answers from your docs. The homepage's own example shows a customer asking about a return window, and the answer cites a returns policy page and a help center article. That is the pattern to expect: you supply the policies, pricing pages and help articles, and the agent is expected to stay close to them.

Let it act. Quickchat AI agents "take actions through APIs". The homepage demo shows a customer who reports that order #4821 is 5 days late. The agent checks the refund policy, finds the order, sees it shipped late, and processes the refund through an Order API call. If you are building a refund, return or order-lookup assistant, that example is the shape of what you would assemble. The company's own buyer guide also lists "Custom APIs" next to HubSpot and Intercom as key integrations.

Deploy and watch. Channels named on the site are your website, your helpdesk, WhatsApp and Slack. After launch, the analytics side is meant to show what the agent is doing. The page says "Analyze how every AI answer was generated" and promises you can "track the exact sources, logic, and actions behind every answer". The demo panel labeled "Why AI said that" breaks an answer into reasoning, knowledge sources and the action taken. For a team that must explain a wrong answer to a manager or a customer, that audit view is one of the more concrete selling points on the page. Its blog describes the platform as strong at "natural conversations, handling large Knowledge Bases, and AI observability analytics" and as having multilingual support.

The four use cases on the homepage

The site says "One platform. Four high-impact use cases" and suggests starting with your highest-volume conversations, then expanding from customer-facing workflows to internal operations.

• Support Agent. Resolves requests automatically, so your team can focus on edge cases and higher-value work.
• Marketing and ecommerce. Aimed at shoppers who cannot find answers: the agent responds before they leave and keeps purchase intent moving.
• Sales Assistant. Qualifies leads, captures context and guides prospects through the buying journey. The demo has the agent answer a plan question, ask which helpdesk the visitor uses, and pass the details to a sales team.
• HR Agent. An internal assistant, shown answering a question about how many vacation days an employee has left.

Since this directory also files tools by what you can build, note that Quickchat AI is a hosted agent builder. Nothing on the pages mentions a visual canvas, a template library, code export or self-hosting, so none of those is claimed here.

The evidence from the pages

Pricing. The FAQ says the company "offers a free plan and paid plans at $9, $29, $99, $299, and $999 per month." The company's comparison with Sierra names four of the tiers and shows the annual discount: Basic at $29 a month ($24 a month billed annually), Essential at $99 ($83 annually), Professional at $299 ($249 annually) and Business at $999 ($833 annually). The same page lists the $9 tier at $8 a month billed annually. Enterprise customers are billed per resolved conversation, starting at $0.50 per resolution, with volume discounts, and only conversations the AI Agent resolves without human handoff are billed.

The free plan is positioned as the way to evaluate: the company says it "lets teams evaluate the platform on a real knowledge base before any procurement conversation." Its blog also says the free tier covers evaluation without procurement.

Speed and results. The company says it deploys in 1 to 7 days. It reports a resolution rate above 80 percent on customer data and one customer, Maybe Tech, that handles 600+ daily inquiries with 93 percent AI-resolved. The homepage carries a ">80%" resolved-automatically figure. Treat these as the vendor's own numbers.

Price comparison. The homepage claims it is "75% cheaper than Agentforce". A footnote says the pricing is based on publicly listed competitor rates and that the resolution rate was measured across real customer deployments against published industry benchmarks, as of June 2026.

Privacy and compliance. The homepage's privacy block reads "Audited & EU-ready": ISO "In progress", SOC 2 "In progress", GDPR "EU-ready". It adds "No training on your data" and describes the product as "Built for strict enterprise requirements from day one", with "No LLM training on customer data, no data sharing with external parties." Neither ISO nor SOC 2 is stated as complete, so a security review should ask for the current status.

Integrations. The pages name HubSpot, Intercom, custom APIs, WhatsApp and Slack. The buyer guide lists multilingual support.

Support and onboarding. The pages shown do not describe support channels or a template count.

Who it suits and who it does not

Quickchat AI suits a support lead who already has a help center and wants an agent that answers from it, with the option to let it process simple actions such as refunds through an API. It also suits a revenue team that wants an assistant qualifying inbound visitors, and a team that has to justify an AI answer to a stakeholder and values the sources-and-actions trail.

The pricing structure is easy to model. A small team can start on the free plan, move to a $29 or $99 tier, and larger deployments can choose per-resolution billing at $0.50. Because you pay only for conversations resolved without handoff at that level, the bill follows outcomes rather than seats.

There are caveats. The ISO and SOC 2 items are in progress, which matters to a regulated buyer. The agent depends on the quality of the docs you give it: gaps in your help content will show up as gaps in answers. At the enterprise level, cost scales with the number of resolved conversations, so a team with very high volume should work out the monthly figure before committing. Finally, this is a managed agent platform. A developer who wants a drag-and-drop canvas, exported code or a self-hosted runtime will not find those described on the pages.