Decagon
The AI concierge for every customer: build, optimize and scale AI agents across chat, voice and email.
By Decagon AI, Inc. · 4.2/5 Value-Position score (estimate)
Positioning guardrails
Best for
- Enterprise B2B and consumer brands with high-volume chat, voice or email support queues
- CX and support operations teams that want to author and iterate agent behaviour without engineering sprints
- Companies that need one agent across chat, voice, email and SMS with shared cross-channel context
- Leaders building a business case on deflection rate, CSAT and cost per contact
Ideal size: 50–1,000 support & CX staff people · Mid-market to enterprise with a dedicated CX operations function
Not for
- SMBs or startups seeking transparent list pricing or a self-serve free tier
- Teams that want a pre-built template bot rather than configuring workflows themselves
- Buyers requiring on-premises, self-hosted or air-gapped deployment
- Businesses needing merchandising, ERP or back-office automation rather than CX conversations
Value metrics scorecard
Time-to-Value
About a month (estimated)
~30 days to first production value
Total Cost of Ownership
On request
Quote-based enterprise pricing; no public list price and no self-serve plan — the site routes every buyer to a demo request
Implementation Friction
3/5
Engineering + admin effort required
Value-Position score
out of 5 · model estimate
Full cost breakdown
Mandatory implementation fee
None
Seat tiers
Not listed
Add-on costs
- None
Company & support
Who is behind Decagon, and how your team gets help once it is live.
Market position
Where Decagon sits against its closest alternatives. Pick any two of cost, speed, friction and buyer score, and up to 9 companies to compare.
Quadrant view
Typical annual cost × Time-to-value
The lines cross at the median of the solutions shown, so about half sit on each side of each line. A dashed ring marks an outlier pinned to the edge; hover for its value.
Companies on the chart 6 / 10
- Decagon
- Order.co
- ChefTec
- EnergyCAP
- MyQ
- TransferMate
Add or change companies
Up to 10 companies including Decagon. Listed closest first.
Stack fit signal
Compatibility with standard B2B ecosystems.
No supported MCP path today, so it cannot be driven from an AI client.
AI & MCP readiness
What Decagon ships in AI, and what it asks of your ecosystem.
AI features shipped
Decagon's core product is AI agents that resolve customer conversations across chat, voice, email and SMS. Its security page documents governance controls: a supervisor model that detects hallucinations before sending, bad-actor detection for adversarial prompts, a 'Watchtower' QA layer reviewing every conversation, and tamper-protected audit logs.
Your data & models
- Trains on your data
- Never trains on your data
- Runs on
- OpenAI, Anthropic
- AI pricing
- Not recorded
In your ecosystem
- AI connection
- Not supported
- Model key
- Not recorded
- AI usage audit
- Basic visibility
Compliance attestations
* IAPP AIGP certifies individuals, not products. It means named staff hold the credential — not that the platform does.
Bottom line
Decagon sells an enterprise AI concierge that resolves customer conversations across chat, voice, email and SMS. Agent Operating Procedures let CX teams define agent behavior in natural language, with testing, observability and analytics built in. Reference customers include Chime, Duolingo, Notion, Rippling, Hertz and Mercado Libre, reporting deflection rates of 32–90% and large cost reductions. Security includes RBAC, SSO, AES-256/TLS 1.2+, PII redaction, hallucination supervision and audit logs. Pricing is quote-based and not published.
Frequently asked questions
How is Decagon priced?
Decagon does not publish list pricing on its website; every pricing path routes to a 'Get a demo' request, so expect a custom enterprise quote. Before signing, ask how the commercial model works (per conversation, per resolution or platform fee), what the minimum term is, and which channels and integrations are included versus charged as add-ons. Compare the quoted cost against your current cost per contact.
How quickly can we go live?
No implementation timeline is published. Public customer quotes describe fast deployment — one operations manager says the previous vendor took half a week of maintenance while Decagon was a 'night-and-day difference', and another reports 10x more deflection at launch than expected. Treat those as directional: scope a pilot on one channel and one intent set first, and ask the vendor for a reference implementation plan before committing to a wider rollout.
Which channels can the agents handle?
Decagon unifies chat, voice and email within a single intelligence layer, and its about page lists voice, chat, email and SMS among the channels it supports. Agents share cross-channel memory, so context from a chat carries into a voice call, and voice agents support brand customisation and end-user authentication. Confirm which channels are enabled in your contract, since pricing is quote-based.
Is Decagon compliant enough for regulated industries?
The security page documents RBAC, SSO with Okta and Microsoft Entra, just-in-time API tokens, AES-256 encryption at rest, TLS 1.2+ in transit, PII redaction via Google's DLP service, multi-region infrastructure and platform uptime SLAs. However, the pages reviewed do not state SOC 2, ISO 27001, HIPAA or FedRAMP certifications — the site points to a separate Trust Center. Request current attestation reports and a BAA or DPA as part of diligence.
Does Decagon train models on our conversations?
No. Decagon's security page states it enforces zero-data retention with all AI providers, naming OpenAI and Anthropic, 'ensuring no conversation data is stored or used for training'. Sensitive fields are also redacted using Google's DLP service shortly after a conversation ends, and access to audit logs is restricted to senior engineering leadership. Buyers in regulated verticals should still confirm the same terms in the contract.
Do we need engineers to maintain the agent?
Decagon positions Agent Operating Procedures as the alternative to complex configuration languages: workflows are defined in natural language, so CX teams can refine behaviour without an engineering sprint or a vendor ticket. The platform adds testing, observability and experimentation so changes can be validated before rollout. One operation leader reports that half of his week previously went to maintaining another vendor's system; a pilot is still the fastest way to test the claim.