Decagon
Decagon handles support conversations and connects them to business workflows.
Overview
- Transparency
- 4 / 10
- Trust
- 99 / 100
Trains on your data
- Free
- No
- Paid
- No
- Opt-out
- Not captured
Incidents
00
Assessed SEP 21, 2026 · Checked by hand SEP 21, 2026
Decagon asserts SOC 2, ISO 27001, GDPR, HIPAA and PCI DSS, and its DPA security annex prohibits training and confirms provider ZDR and deletion on request.
Trust breakdown
99 / 100
Overall trust score
Data and privacy
Trains on your data
No training on user data
Storage regions
Sub-processors
Data retention
Third-party model providers operate under Zero Data Retention (ZDR) agreements that contractually prohibit the retention, logging, or use of Customer Personal Data for model training, finetuning, or any purpose beyond real-time inference.
Deletion: Decagon will delete Customer Personal Data upon the Customer’s written request, within the timeframe specified in this DPA and in accordance with Applicable Privacy Laws.
Powered by
Decagon uses a multi-model AI stack. They leverage OpenAI models (GPT-3.5, 4, 4o, 4 Turbo, and o1-mini) for various tasks, including complex decision-making and query rewriting after fine-tuning. They also train and fine-tune their own proprietary open-source LLMs, specifically on enterprise customer support conversations, with approximately 80% of inference traffic running on these in-house models as of March 2026. They use Together AI's inference engine for production inference and Modal for training and serving their real-time voice AI models, including custom draft models for speculative decoding. Microsoft Azure is used to host both off-the-shelf and fine-tuned models.
Integrations and access
Integrations
Available on
Pricing
Enterprise
Custom
Decagon does not publicly list its pricing. It offers two pricing models: per-conversation (a fixed rate for every incoming conversation, with flexible pricing for higher volumes) and per-resolution (a higher fixed rate for each fully resolved conversation, with no charge for escalations). Pricing is custom-quoted based on factors like ticket volume, channel mix, integration complexity, workflow depth, and SLA requirements. Third-party estimates for annual contracts range from approximately $100,000 to $580,000, with a median around $432,750. There is also a reported annual platform fee of $50,000 before any usage charges. There is no free trial or self-serve option; all accounts require a demo and custom quote.
Prices as listed on SEP 21, 2026; check the vendor's page.
Moat and openness
Moat
- Proprietary Model
- Proprietary Data
- Network Effects
- Switching Costmoderate
- Unique UX
- Distributionmoderate
Decagon exhibits some defensibility through its proprietary fine-tuned LLMs, trained on enterprise customer support conversations, and its specialized Agent Operating Procedures (AOPs) for defining AI agent workflows. The extensive list of notable enterprise customers and integrations suggests a moderate switching cost once deeply embedded. However, the reliance on underlying OpenAI models and the competitive landscape in AI customer service prevent a stronger moat.
Openness
- Open model weights
- Published research
- Open source contributions
- Transparency reports
- Public safety evals
Decagon emphasizes transparency in its AI products, particularly for enterprise adoption. They aim to provide clear visibility into how their AI agents make decisions, referencing data sources, and drawing conclusions. Their tools, like 'Trace View', offer granular insights into agent behavior and reasoning. They also provide user-friendly feedback systems and proactive improvement suggestions. However, there is no indication of open-sourced models, published research, or public safety evaluations.
Timeline
- 2026-07Decagon reached $100M in annualized revenue.
- 2026-03Decagon completed its first employee tender offer at a $4.5 billion valuation.
- 2026-01Decagon was valued at $4.5 billion following a $250 million Series D funding round.
- 2025-09Decagon launched Voice 2.0, an upgrade to its enterprise-grade AI phone support platform, Decagon Voice, in partnership with ElevenLabs. This version reduced latency by 65% and added cross-channel memory, outbound calling, SMS integration, and finer brand controls.
- 2025-06Decagon raised a $131 million Series C funding round at a $1.5 billion valuation.
- 2024-10Decagon completed a $65 million Series B funding round, valuing the company at $650 million.
- 2024-05Decagon completed a $35 million Series A funding round.
- 2023-08Decagon was founded by Jesse Zhang and Ashwin Sreenivas.
Company
Decagon
- Founded
- 2023
- HQ
- San Francisco, California, United States
- Site
- decagon.ai
Popularity
Not disclosed
monthly visits
Notable customers
Decagon operates in the rapidly growing AI-powered customer service automation market, providing conversational AI agents for enterprise customer support across various channels.
Value and ROI
What it should move: It shortens resolution time and handles more requests without staff intervention.
Best fit
It fits teams that need one configurable agent across several support channels.
The catch
Decagon needs support content and system access. Test policy exceptions, handoffs, and actions that affect customer accounts.
- Duolingo: Uses Decagon to handle customer chat questions for the Duolingo English Test.[1]
- Substack: Uses Decagon to automate support workflows for readers and writers.[2]
In our stacks for