2026 Decagon AI Pricing Model and Plans

In 2026, Decagon has emerged as a leading enterprise AI platform for high-volume support and sales teams, offering scalable AI agents that manage complex workflows. Designed for operationally complex enterprises, it replaces manual processes while maintaining compliance and consistent outcomes. Its pricing and implementation strategies enable CIOs, CROs, and BPO leaders to forecast costs and measure ROI effectively.

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For mid-size to large companies drowning in documents or handling thousands of daily interactions, Decagon pricing provides flexibility between usage-based and outcome-based models. Retail, FinTech, insurance, real estate, and BPO organizations utilize the platform to automate ticket resolution, lead qualification, and internal workflows, reducing handle time and operational costs. 

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This article explores Decagon’s core features, pricing models, and plans, detailing how enterprises can optimize AI-driven automation while aligning costs to business outcomes.

Key Takeaways

• Per-Conversation and Per-Resolution pricing let enterprises align AI costs with either usage volume or successful outcomes.
• Ticket volume, workflow complexity, and integrations are major factors driving total Decagon costs.
• Clearly defining what counts as a “resolved” interaction prevents unexpected charges in outcome-based pricing.
• Onboarding, SLAs, premium support, and multi-region setups affect pricing and operational ROI.
• Retail, FinTech, insurance, real estate, and BPO teams can optimize AI adoption by selecting the model aligned with business goals.

What is Decagon?

Decagon is an enterprise AI platform that functions as a digital workforce for sales, support, and operational teams. It uses AI agents to handle conversations end-to-end, escalating only when necessary. Designed for high-volume query environments, Decagon enables enterprises to automate repetitive workflows while maintaining compliance and operational oversight.

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For operationally complex companies, retailers, insurers, real estate firms, and BPO providers, Decagon reduces manual handoffs, accelerates resolution, and ensures consistent outcomes across multi-channel interactions. 

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CIOs and CTOs utilize it to replace fragmented workflows, while CROs and revenue ops teams track AI-driven impact on conversion and deflection. Its architecture supports scalability, multi-region implementations, and integration with enterprise systems, making it ideal for fast-scaling companies managing thousands of interactions daily.

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Next, let’s explore why enterprises rely on Decagon’s capabilities for high-volume workflows.

9 Core Features of Decagon

Decagon’s platform offers a suite of enterprise-grade tools designed for high-volume, complex workflows across support, sales, and internal operations. Each feature enhances automation, compliance, and operational visibility while ensuring measurable outcomes.

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1. AI Agents: Purpose-built AI agents act as digital teammates, handling conversations end-to-end and escalating only when necessary. Retail teams depend on these agents to manage thousands of tickets or sales interactions daily, reducing manual workload and increasing resolution speed.
2. Agent Operating Procedures (AOPs): Structured playbooks guide agents to operate within a business context. AOPs ensure consistent, compliant responses, essential for regulated industries and complex enterprise environments. Directors of Support and operational leaders rely on AOPs to maintain quality at scale.
3. AOP Copilot: This design companion allows teams to create, test, and refine AOPs efficiently. Fast-scaling companies replacing manual workflows use it to accelerate agent set up, reducing setup time and ensuring predictable automation.
4. Watchtower: Watchtower provides real-time monitoring and observability of agent behavior. Enterprises gain visibility into decision-making processes, enabling trust, oversight, and actionable insights for CROs, revenue ops, and CIOs.
5. Agent Assist: When AI agents cannot fully resolve an issue, they act as copilots for human teammates, suggesting responses, surfacing knowledge, and reducing handle times. BPO leaders and high-volume support teams benefit from faster ticket resolution.
6. Experiments: Workflow testing and optimization tools enable continuous measurement of resolution rates. Operationally complex enterprises implement experiments to refine automation and improve overall performance.
7. Integrations: Prebuilt connections to helpdesks (Zendesk, Freshdesk), CRMs (Salesforce), e-commerce platforms (Shopify), and ITSM tools (Jira, ServiceNow) ensure seamless adoption and data flow for multi-system enterprises.
8. Analytics & Reporting: Provides metrics on resolution rate, escalation rate, deflection, and CSAT. Enterprise leaders use these insights to track ROI, operational efficiency, and automation impact.
9. Enterprise-Grade Compliance & Implementation: Decagon supports SOC 2, ISO 27001, and GDPR compliance, multi-region setup, and dedicated enterprise support, essential for regulated industries like insurance, FinTech, and BPO operations.

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See how Nurix AI helps enterprises qualify and convert more open-enrollment leads using AI voice agents that personalize outreach and route sales-ready prospects instantly.

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To implement these features safely at scale, governance becomes the next essential stage.

Compliance and Governance Model Used by Decagon AI

Decagon AI approaches compliance as a system design problem, not a post-processing layer. Its platform embeds regulatory controls directly into conversational workflows to meet enterprise and industry requirements.

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• Consent-First Interaction Design: Decagon enforces explicit consent capture at the start of voice and chat interactions, with opt-in and opt-out states logged automatically.
• Conversation-Level Audit Trails: Every interaction is recorded, timestamped, and tied to workflow metadata, enabling full traceability for audits and dispute resolution.
• Policy-Grounded Responses: Agents operate within predefined policy boundaries, reducing the risk of hallucinations, unauthorized disclosures, or off-script responses.
• Data Security and Privacy Controls: Decagon supports encryption in transit and at rest, role-based access controls, and data retention policies aligned with GDPR, CCPA, and enterprise security standards.
• Risk and Anomaly Detection: The system flags abnormal conversational patterns, escalation spikes, or sensitive-topic deviations for compliance and QA review.

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These controls ensure agents remain compliant as volumes increase. Measuring whether they deliver business value requires an equally structured metrics framework.

Metrics Decagon AI Uses to Measure Performance and ROI

Decagon AI focuses on metrics that reflect operational impact, reliability, and business outcomes, rather than surface-level engagement statistics.

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1. Automation Rate: Percentage of conversations fully resolved by AI without human intervention, used to quantify workload deflection.
2. Resolution Accuracy: Measures successful task completion against predefined intent and outcome criteria, not just response generation.
3. Latency and Turn Completion Time: Tracks response speed and end-to-end workflow execution time, especially for multi-step conversations.
4. Escalation and Handoff Quality: Monitors how often conversations escalate and whether contextual data is preserved during human handoff.
5. Compliance Adherence Rate: Tracks flagged violations, policy deviations, and corrective actions over time.
6. Cost-to-Resolution: Calculates cost per automated interaction versus human-handled benchmarks to demonstrate margin impact.

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These metrics align closely with how enterprise leaders evaluate conversational AI systems: reliability at scale, measurable efficiency gains, and controlled risk exposure.

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Now let’s examine how these features shape enterprise pricing strategies.

Decagon Pricing Models

Decagon offers two primary pricing models, tailored for enterprises handling high-volume interactions. Per-Conversation Pricing provides predictable costs based on usage, while Per-Resolution Pricing aligns cost with successful outcomes. 

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Each model serves different operational priorities for retail, FinTech, insurance, real estate, home services, and BPO organizations.

Per-Conversation Pricing

Per-Conversation Pricing charges a fixed rate for every interaction, regardless of how well AI fully resolves the issue. It offers budget predictability and simplifies forecasting for high-volume teams. Enterprises replacing manual workflows benefit from this model’s simplicity and scale.

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How it works:

• Every conversation handled by AI counts toward billing, even if escalated to a human agent.
• Partial resolutions are included in the billed total, ensuring a simple, usage-based model.
• Example: 10,000 conversations in a month = billed for 10,000 interactions.
• Costs are fixed, enabling enterprise support teams to forecast monthly budgets accurately.
• High-volume sales and support teams can scale without concern for fluctuating costs.

Use Cases:

• High-Volume Retail Support: Automates thousands of daily inquiries while keeping predictable spend.
• BPO Multi-Client Operations: Manages interactions across multiple clients without unexpected billing.
• Insurance & FinTech Standard Queries: AI resolves repetitive account, policy, or billing questions efficiently.
• Real Estate Lead Qualification: AI handles initial prospect interactions and routes leads to agents.
• Home Services Booking & Support: Automates routine appointment scheduling and customer requests.

Benefits:

• Predictable Budgeting at Scale: Fixed per-interaction costs allow finance and operations teams to forecast monthly spend accurately, even during traffic spikes.
• Simple Billing Logic: Every AI-handled conversation is counted uniformly, eliminating disputes over resolution definitions or success criteria.
• Fast Installation for High Volume: Ideal for teams replacing manual workflows quickly without redesigning KPIs around resolution quality.
• Scales Across Channels: Works consistently across voice, chat, SMS, and messaging without pricing complexity.
• Low Operational Overhead: Reduces internal effort spent auditing outcomes, reconciling tickets, or validating AI performance for billing.

Challenges:

• Paying for Partial Resolutions: Enterprises incur costs even when AI cannot fully resolve tickets.
• Overcharging for Simple Interactions: Short or trivial conversations are billed the same as complex ones.
• Limited Performance Incentive: Fixed pricing reduces pressure on AI to maximize successful outcomes.
• High Volume, Low Complexity Risk: Enterprises may pay more when most interactions are simple queries.
• Budget Efficiency Trade-Off: Predictability comes at the cost of paying for unresolved issues.

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Also Read: How Voice AI Helps High-Volume Call Center Teams Stay Ahead

Per-Resolution Pricing

Per-Resolution Pricing charges only for interactions the AI fully resolves, creating cost alignment with actual outcomes. It incentivizes vendors to improve AI performance and enables enterprises to track ROI accurately. This model suits teams focused on successful resolution metrics rather than total usage.

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How it works:

• Only fully resolved interactions are billed; escalated or partially answered tickets are free.
• Example: AI resolves 6,000 out of 10,000 monthly tickets = billed for 6,000 resolutions.
• Monthly costs vary depending on AI performance and resolution volume.
• Enterprises only pay for measurable outcomes, creating clear alignment between cost and value.
• Suitable for teams focused on maximizing ROI from AI adoption while minimizing wasted spend.

Use Cases:

• Outcome-Focused Support Teams: Insurance and FinTech teams pay only for fully resolved tickets.
• BPO Operations: Tracks performance and billing accuracy across multi-client workflows.
• Sales & Lead Qualification: Real estate and home services pay only for leads fully qualified by AI.
• Enterprise Knowledge Management: Companies automating document processing pay only for successful completions.
• High-Volume Customer Engagement: Retail teams automate self-service queries while paying for actual resolutions.

Challenges:

• Defining a Resolution: Ambiguous or partially answered tickets complicate billing.
• Cost Predictability: Resolution volume may fluctuate, making forecasting difficult.
• Ensuring Quality: Vendors might over-report resolved tickets; CSAT and escalation tracking are required.
• Variable ROI: Monthly resolution numbers can create unpredictable efficiency metrics.
• Complex Enterprise Workflows: Multi-step tickets may be partially resolved but still billed inconsistently.

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Also Read: AI Trends in 2026: Key Shifts in Technology and Execution

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Understanding these models highlights the key cost drivers for different business needs.

What Influences Decagon Pricing?

Decagon pricing depends on multiple enterprise-specific factors, including ticket volume, workflow complexity, onboarding requirements, and how “resolution” is defined. These variables impact total cost and ROI.

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1. Ticket Volume: High-volume support and sales teams benefit from economies of scale. Enterprises handling thousands of tickets monthly can negotiate lower per-interaction or resolution rates, while mid-size teams have less impact.
2. Complexity of Workflows: AI tasks requiring multi-step automation, integrations with CRMs, e-commerce platforms, or ITSM tools cost more than simple Q&A or single-step queries. Operationally complex enterprises often face higher quotes.
3. Onboarding and Setup: Implementation, agent training, and professional services may incur one-time costs. Fast-scaling companies replacing manual workflows should include these in ROI calculations, especially when setting up multiple AI agents simultaneously.
4. Resolution Definition: Ambiguity in what constitutes a “resolved” ticket can increase costs under outcome-based models. Clear, predefined resolution criteria are essential for predictable enterprise budgeting.
5. Enterprise-Specific Customization: AOPs, integrations, compliance needs, and multi-region setting requirements affect pricing. Insurance, FinTech, and BPO organizations often pay more to meet regulatory and operational standards.

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To understand how enterprises operationalize agentic AI at scale, see how Aditya Birla Capital used Nurix AI’s voice agents to replace IVR-driven qualification with outcome-driven automation.

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Next, we’ll identify hidden costs and add-ons that can influence total spend.

Hidden Costs and Add-Ons to Look For in Decagon Pricing

Decagon pricing may appear straightforward, but contract nuances and enterprise requirements can significantly affect the final cost. High-volume support, BPO, and sales operations should account for the following:

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• Unresolved Conversations: In Per-Conversation Pricing, every AI interaction counts, even if escalated to a human. High-volume teams must consider this when forecasting monthly spend.
• Resolution Ambiguity: Outcome-based Per-Resolution Pricing requires pre-defined resolution criteria. Loosely defined resolutions can lead to unexpected charges and inflate total cost without added business value.
• Custom Quotes: Decagon does not publish standard list prices. Enterprise clients typically receive bespoke quotes based on ticket volume, workflow complexity, and SLA requirements, creating variability in total spend.
• SLA and Support Tiers: Premium SLAs, uptime guarantees, and priority support may add cost. High-volume, regulated enterprises like insurance, FinTech, and BPO teams should confirm scope and fees before finalizing contracts.

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Also Read: Why Is AI In Customer Experience Now A Strategic Advantage?

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Now that pricing has been made clear, let's examine how Nurix AI can optimize workflows at scale.

How Nurix AI Manages Real Enterprise Workflows Beyond Traditional Conversational AI

Most conversational AI tools stop at answering questions, but Nurix AI goes further by executing real enterprise work end-to-end. Built for operationally complex environments, Nurix AI delivers human-level AI agents that don’t just converse; they reason, act, and complete multi-step workflows across sales, support, and knowledge operations. 

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That’s why high-volume enterprises across retail, FinTech, insurance, real estate, and BPO choose Nurix AI over generic conversational agents to achieve faster resolutions, higher efficiency, and provable ROI.

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How Nurix AI makes a measurable difference:

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• Sales Voice Agents: Unlike scripted bots, Sales Voice Agents conduct natural, context-aware conversations to qualify leads, guide buyers, and automate SDR outreach. They apply business logic in real time, route SQLs directly into CRMs, and help teams close deals faster while reducing customer acquisition costs.
• Support Voice Agents: Support agents resolve tickets end-to-end and handle account, order, and subscription queries instantly; detect intent and sentiment; and escalate only truly complex cases with full context, driving higher CSAT and lower support costs.
• Internal Workflows / Work Assistant: While most AI tools summarize documents, Nurix AI automates the entire lifecycle, reading, extracting, validating, and routing contracts, RFPs, and research outputs. This cuts review cycles, enforces compliance, and removes manual bottlenecks from knowledge-heavy teams.
• NuPlay Platform: NuPlay is the execution layer that competitors lack. It connects AI agents directly to CRM, ERP, and support systems, enabling sub-second, interruption-safe voice, real task completion, brand-controlled personas, and real-time conversation analytics.
• Enterprise Work Assistant: Nurix AI extends conversational AI into deterministic, rules-based internal workflows across HR, IT, finance, procurement, and compliance. CIOs and BPO leaders use it to replace fragmented tools with governed, scalable automation across multiple systems.

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From lead qualification and ticket resolution to document intelligence and internal operations, Nurix AI enables true automation at scale where other conversational AI platforms fall short.

Conclusion

Decagon pricing allows enterprises to align AI costs with priorities, focusing on predictable spending and outcome-based ROI. High-volume support teams and BPO leaders must consider ticket volume, workflow complexity, and resolution definitions. Onboarding, integrations, and premium support tiers also impact total cost. Retail, FinTech, insurance, and real estate teams can optimize Per-Conversation or Per-Resolution models to scale AI agents, improve resolution rates, reduce handle times, and expand margins.

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Optimize your enterprise AI investment with Nurix AI by utilizing Sales Voice Agents and Support Voice Agents to automate high-volume interactions efficiently. These agents accelerate lead qualification, ticket resolution, and document workflows while helping your teams align costs with outcomes and maximize ROI. 

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Schedule a demo and discover how Nurix AI can improve your enterprise operations with human-level intelligence and automation.