Decagon
Verified Oct 15, 2026 ActiveEnterprise customer-service agents for chat, email and voice, configured in plain-language procedures
Listed on EffectHub ·
Pricing
- Pricing model
- Contact sales
No public price: there is no pricing page (the URL returns 404) and the only path is a get-a-demo form. Contracts are enterprise agreements negotiated with sales; no self-serve tier or trial is advertised.
Use tool ↗About Decagon
Decagon builds AI agents that handle customer-service conversations over chat, email and voice for large consumer brands, with Agent Operating Procedures that let operations teams write the agent's logic and escalation rules in natural language instead of a configuration language. Around the agent sit live A/B experiments, simulation-based testing, a Watchtower module for always-on QA, reporting, knowledge suggestions and pre-built connectors to support stacks, and a newer Duet product positions the agent as a partner that improves itself from outcomes. Named customers include Deutsche Telekom, American Airlines, Square, Duolingo, Notion and Chime. Founded in San Francisco in 2023 by Jesse Zhang and Ashwin Sreenivas, the company raised a $250 million Series D in January 2026 at a reported $4.5 billion valuation; pricing is by sales conversation only.
- Platforms
- Web · API
- API
- Yes
- Last verified
- Oct 15, 2026
Verified sources
- Home page: AI concierge agents across chat, voice and email; Agent Operating Procedures in natural language; experiments, testing and QA, Watchtower always-on QA, insights and pre-built connectors; Duet product; customers including Deutsche Telekom, American Airlines, Square, Notion, Duolingo and Chime; get-a-demo only, no prices (15 October 2026)decagon.ai ↗
- The pricing URL returns HTTP 404; no pricing link exists in the site navigation (15 October 2026)decagon.ai ↗
- About page: co-founders Jesse Zhang (CEO) and Ashwin Sreenivas (President); investors a16z, Accel, Bain Capital Ventures, Coatue and Index Ventures (15 October 2026)decagon.ai ↗
- Privacy policy effective 6 May 2025 names Decagon AI, Inc. with a mailing address at 2261 Market Street, San Francisco, California (15 October 2026)decagon.ai ↗
- Founded August 2023 in San Francisco by Jesse Zhang and Ashwin Sreenivas; $250 million Series D in January 2026 led by Coatue and Index Ventures at a $4.5 billion valuationen.wikipedia.org ↗
Is Decagon legit?
We don’t label tools safe or unsafe. Below are the records we checked ourselves, each with the date we read it.
- Domain registered
- Nov 17, 2022 (3 years ago)Registry record (RDAP), registrar GoDaddy.com, LLC, looked up Sep 6, 2026
- TLS certificate
- Issued by Let's Encrypt, valid until Dec 14, 2026Seen in our own connection to the site on Oct 15, 2026
- Last checked
- Oct 15, 2026The site answered
- On the market since
- 2023The vendor's own material, read by us
- Operated by
- Decagon AI, Inc., United StatesOur research, card verified Oct 15, 2026
Pros & cons of Decagon
One of the two or three vendors a large consumer brand should shortlist for a support agent that must handle refunds, bookings and account actions rather than deflect FAQs, and the plain-language procedures plus built-in QA are what make it operable by a support team instead of engineers. It is an enterprise purchase with a sales cycle and a price that depends on your volume, so a team under a few hundred thousand conversations a year is not the customer.
Pros
- Agent logic written as natural-language operating procedures rather than flow builders
- Experiments, simulation testing and always-on QA ship with the agent
- Chat, email and voice from one vendor with pre-built connectors
- Reference customers at the scale of Deutsche Telekom, American Airlines and Square
Cons
- No public pricing or trial; everything goes through a demo and an enterprise contract
- Built for high-volume brands, so smaller teams are unlikely to clear the commercial threshold
- Rapid growth and new products such as Duet mean the feature set is moving quickly
- Governing law and a registered office are not stated in the public legal pages, only a San Francisco mailing address
Best for
- Consumer brands with high ticket volume across chat, email and voice that need the agent to take actions in backend systems
- Support operations teams that want to own agent behaviour through written procedures and A/B tests
- Enterprises that need simulation testing and continuous QA before letting an agent speak to customers
Not for
- Small businesses or startups looking for a priced, self-serve chatbot
- Teams whose support stack has no API for the agent to act through
Watch out: Pricing is volume-based and negotiated, so model your conversation counts before the demo; the procedures approach also means someone on your team must own and maintain them like policy documents.
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