CoSupport AI is an AI platform for customer service scenarios, founded in 2020 and built on a reasoning architecture patented by the United States Patent and Trademark Office (US11823031B1), which the company says has been used by more than 300 brands. Its core idea is to let AI learn from a company's own knowledge base and resolved tickets, and then take over repetitive inquiries in the ticket queue.
[Purpose] CoSupport AI is used to automate and assist customer support. On one hand, it autonomously resolves repetitive L1 tickets end to end; on the other hand, it generates ready-to-send reply drafts for tickets that require human judgment. The company says repetitive inquiries can account for up to 90% of L1 tickets.
[Main capabilities] The platform includes two products. CoSupport AI Agent understands customer intent, performs actions through agentic APIs, and escalates to humans with full context when out of scope, covering scenarios such as billing and refunds, login and password reset, order status and delivery, subscription changes (upgrades, downgrades, cancellations), setup questions already covered in the help center, and routine troubleshooting. CoSupport AI Assistant provides human agents with ready-to-send drafts, summaries, suggested solutions, and translations. Both can automatically detect the customer's language and reply in that language, support more than 40 languages, and cover chat, email, and voice channels. The AI can also read and understand images, screenshots, and videos in attachments.
[How it runs and integrations] CoSupport AI can run within a company's existing help desk, with the company mentioning Zendesk, Freshdesk, Zoho Desk, and others, and can also run as a standalone widget on a website. The company says connection takes about 10 minutes, full deployment takes about 3 days, and it is a no-code integration that does not require a discovery project.
[Performance data (provided by the company)] The company says the average customer AI resolution rate is 74%, teams on version 2.0 report resolution rates of up to 85%, and response accuracy is 99%. Cases include: ProjectFitter's average chat resolution time is about 6 minutes, 93% faster after use; Cocoatech resolved 81% of support requests with AI in the first month (Zendesk); Remedico saves $9,300 per month in support costs and went live on Zoho Desk within two weeks; eCatering diverted 50%; Nordic Knots saves $7,000 per month; Softorino improved from 69% to 82%; SupportYourApp saves about $14,000 per month, with 80% ticket triage.
[Accuracy and Security] Officially, 99% response accuracy is guaranteed by a patented architecture designed to prevent hallucinations and keep answers deterministic: the AI retrieves from the knowledge base and resolved tickets, verifies drafts against these sources, and only then replies. On security, each customer uses a separate server, tickets are encrypted in transit and at rest, customer data is not used to train other models, and it is ISO 27001 certified and compliant with GDPR and CCPA.
[Pricing] Officially, three pricing models are offered, and only one can be chosen for billing: $0.04 per AI response; $0.19 per resolved ticket, with unresolved ones free; and a flat plan at $99 per 1,000 tickets per month, including unlimited AI responses. There is no setup fee and no long-term contract. There is also a 30-day pilot that includes 1,000 AI responses, running on real tickets. Officially, it promises to reach a 60% resolution rate within 60 days, otherwise refunding all fees paid for the days used.
[Applicable Scenarios] Suitable for teams with a high volume of repetitive L1 tickets. The industries officially listed include SaaS and technology, e-commerce and marketplaces, consumer apps and subscription products, BPO outsourcing, financial services, and education.
[Limitations] Regarding unsupported features, the specific integration list, voice channel details, data storage locations, and similar information, the materials do not currently provide them. The pricing and performance data all come from official sources, and actual results may vary depending on ticket types and data quality.
