Cignara
Summary
Contact centers running high volumes discover fast that generic chatbots collapse the moment a caller wants to reschedule, dispute a charge, and ask a billing question in the same breath — the handoff logic wasn't built for that. Cignara exists for exactly that failure mode.
Cignara deploys AI agents that handle inbound voice and chat support from first contact through resolution, following your SOPs and policy rules without a human stepping in for every edge case. The platform is built for large B2C contact centers where call volumes make per-interaction staffing costs unsustainable. It also surfaces upsell signals mid-conversation, so revenue opportunities that a tired agent would miss at hour six of a shift are captured automatically. The ceiling appears when your workflows require judgment calls that fall outside documented policy — the agent follows rules well, but writes none of its own. Teams with highly variable, exception-heavy interactions report needing significant policy documentation work before the system handles them reliably.
Bottom line: The right fit for an enterprise contact center automating policy-driven, repeatable interactions at scale — the wrong fit if your support team's value is in improvised, relationship-driven conversations that no SOP can fully capture.
Pricing Plans
SubscriptionEnterprise
Custom pricing based on volume, channels, and use cases; requires sales engagement
- Conversational AI agents (voice + chat)
- AI Copilot for human staff
- Enterprise knowledge graph
- Policy-driven guardrails
- SOC 2 compliance
- Multiple LLM provider support
- Custom integrations
View full pricing on cignara.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Agents complete multi-step support interactions — rescheduling, refund processing, billing disputes — autonomously end to end, so your human team handles exceptions rather than volume.
- Policy-driven execution means a compliance or SOP update propagates through agent behavior without rebuilding workflow logic, which prevents the drift between your documented process and what the system actually does.
- Real-time copilot mode feeds live suggestions to human agents mid-call, so the productivity benefit extends to interactions that do require a person rather than stopping at automation.
- Multi-channel coverage across voice and chat from a single platform, so you avoid running separate automation stacks that produce inconsistent customer experiences across contact methods.
- Upsell and cross-sell signal detection runs during live interactions, which means revenue opportunities surface at the moment they are relevant rather than in a post-call analytics report nobody acts on.
Cons
Sign in to edit- The agent follows policy it is given — it does not generate or infer policy for novel situations. Teams with high exception rates or loosely documented SOPs spend significant time on policy engineering before the system handles real call volume reliably; this work is invisible in the demo and surfaces in the first production month.
- There is no self-hosted deployment path and no public pricing or trial access. Enterprises with data residency requirements that rule out vendor-hosted infrastructure have no workaround — this is the condition under which teams move to a self-hostable competitor rather than continuing the sales conversation.
- The platform targets large enterprise contact centers, which means the onboarding and sales process is calibrated for procurement cycles. Teams at mid-market scale or those needing a working proof-of-concept before budget approval are structurally excluded from evaluating it.
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About
- Platforms
- Cloud-based SaaS; phone and chat channels
- API Available
- No
- Self-Hosted
- No
- Last Updated
- 2026-06-04T12:19:18.859Z
Best For
Who it's for
- Large B2C enterprises with high contact center volumes
- Organizations with complex SOPs and policy-driven workflows
- Companies needing multi-channel support automation (voice + chat)
- Enterprises requiring strict compliance and data governance
What it does well
- Automating inbound phone and chat support inquiries
- Handling appointment booking and rescheduling
- Processing refund requests and billing inquiries
- Real-time copilot assistance for human support agents
- Upsell and cross-sell opportunity detection during customer interactions
Integrations
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Frequently Asked Questions
- Is Cignara free?
- Cignara is a paid tool. No permanent free tier is offered.
- Is Cignara open source?
- No — Cignara is a closed-source tool. Source code is not publicly available.
- When was Cignara released?
- Cignara was first released in 2022.
- What platforms does Cignara support?
- Cignara is available on: Cloud-based SaaS; phone and chat channels.
Hours Saved & ROI Stories Community
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Cignara is an enterprise-grade AI agent platform designed to automate inbound support across voice and chat channels. The core workflow has agents receiving a contact, identifying intent, executing the relevant SOP or policy-driven resolution path — refund processing, appointment rescheduling, billing inquiry — and closing the interaction without escalating to a human unless the policy explicitly requires it. The platform also operates as a real-time copilot for human agents, surfacing relevant information and suggested responses during live calls rather than waiting for post-call review.
The differentiating capability the vendor positions most heavily is policy-driven autonomy at scale: the agents are not running from a decision tree you draw by hand but from structured representations of your actual operating procedures. This means that when a policy changes, you update the policy document — not the branching logic of a flowchart. For organizations with compliance requirements or strict data governance mandates, that separation between policy and execution is a meaningful operational control point.
Cignara targets large B2C enterprises with contact centers handling enough volume that per-interaction cost is a line item executives track. It fits well when your support interactions are high-frequency and policy-bounded — the kind of work where the correct answer exists and the problem is executing it consistently at speed. It breaks down when interactions require improvised judgment, cultural nuance, or relationship context that no SOP encodes. There is no self-hosted option and no public pricing — procurement goes through a sales process, which means teams cannot prototype or benchmark without a vendor engagement. Organizations that need to test before committing to a sales cycle will hit that wall immediately.
