# Decagon vs Sprinklr: who uses which

On usedby, 43 companies use Decagon and 23 use Sprinklr. 2 companies use both, including Deutsche Telekom and Fanatics. Counts include every proof level, job-post signals included. Companies named on both have a confirmed or observed line on each side. Latest check 2026-10-08: 66 of 66 public lines checked.

## Side by side

|  | [Decagon](https://www.usedby.ai/tools/decagon.md) | [Sprinklr](https://www.usedby.ai/tools/sprinklr.md) |
|---|---|---|
| Public customers | 43 | 23 |
| Confirmed | 23 | 15 |
| Observed | 20 | 8 |
| Signals (job posts) | 0 | 0 |
| Top industries of customers | Fintech & Payments (4), DevTools & Infrastructure (3), HealthTech & Digital Health (3) | IT Services & System Integration (2), Airlines & Aviation Services (1), Automotive Manufacturing (1) |
| Category | Customer Service Automation | Customer Service Automation |
| Integrations | Salesforce, Intercom, Zendesk, Confluence, Contentful, Kustomer, Amazon Connect, RingCentral, Zendesk Sunshine ([decagon.ai](https://decagon.ai/product/integrations), checked 2026-10-04) | Adobe Analytics, Adobe Experience Manager, Salesforce, Slack, Zendesk, ServiceNow, MS Teams, Okta, Snowflake, Tableau, Power BI, Google Analytics 4 ([sprinklr.com](https://www.sprinklr.com/products/platform/integrations/), checked 2026-10-08) |
| Security | — | SOC 2 Type II, PCI DSS, ISO 27001, FedRAMP ([sprinklr.com](https://www.sprinklr.com/trust/), checked 2026-10-08) |
| Data residency | — | United States, Europe ([sprinklr.com](https://www.sprinklr.com/trust/), checked 2026-10-08) |
| Data processing terms | — | A data processing agreement is available. ([sprinklr.com](https://www.sprinklr.com/trust/), checked 2026-10-08) |

## Companies named on both

Confirmed or observed on each side.

| Company |
|---|
| [Deutsche Telekom](https://www.usedby.ai/companies/deutsche-telekom.md) |
| [Fanatics](https://www.usedby.ai/companies/fanatics.md) |

## Published results

Reviewed stories only; usedby did not measure the figures.

- Decagon: [NG.CASH](https://www.usedby.ai/case-studies/ng-cash-decagon.md): **35 additional agents**: “Avoided the need to hire over 35 additional agents while managing growing inquiry volumes” (published 2026-07-28)
- Decagon: [Notion](https://www.usedby.ai/case-studies/notion-decagon.md): **34%**: “ticket resolution time improved up to 34%” (published 2026-07-28)
- Sprinklr: [Asharq Network](https://www.usedby.ai/case-studies/asharq-network-sprinklr.md): **USD ~1,000,000**: “resulting in an estimated USD ~1,000,000 in man‑hour savings” (captured by usedby 2026-10-08)
- Sprinklr: [Tawuniya](https://www.usedby.ai/case-studies/tawuniya-insurance-sprinklr.md): **44-minute**: “44-minute reduction in average response times” (captured by usedby 2026-10-08)

---
Source: https://www.usedby.ai/compare/decagon-vs-sprinklr · How we check: https://www.usedby.ai/methodology