Cignara vs Novus

Cignara and Novus are both business tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Cignara

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.

Novus

Novus scans your codebase, auto-instruments product analytics without requiring engineers to tag events by hand, and monitors user flows for regressions — flagging broken interactions before they reach production. The agentic layer goes further: it reviews pull requests for UX issues, proposes fixes, and can open its own PRs with remediation code, though a human signs off before anything merges. That approval gate is a deliberate design choice, not a limitation. Where the system strains is on the monitoring side: the scraped page content available does not confirm depth of support for complex branching flows or highly customized event schemas, so teams with mature, bespoke analytics stacks will need to validate fit before migrating.

Attribute	Cignara	Novus
Pricing	Paid	Paid
Free trial	No	No
Open source	No	No
Has API	No	Yes
Self-hosted option	No	No
Platforms	Cloud-based SaaS; phone and chat channels	Web (SaaS); integrates with GitHub
Released	2022	2026-03-25
Pros	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.	Automatic codebase instrumentation without manual event tagging, so engineers stop losing sprint time to analytics upkeep every time a feature ships. Regression detection before production, which means broken user flows surface in review — not in a customer support ticket three days after release. PR-level UX review with generated fix proposals, so code moving fast through AI-assisted development gets a behavioral sanity check that manual review at speed cannot reliably provide. Unified monitoring of both human and agent-driven user flows, so product teams running AI features do not have to stitch together separate observability tools to see the full picture. Human approval required before any proposed code change merges, so the agentic layer accelerates without removing accountability from the team shipping the product.
Cons	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.	No self-hosted deployment option is available, which means teams with data residency requirements or air-gapped environments cannot use Novus at all — those teams evaluate on-premises analytics platforms instead. Open beta status means the pricing model is not fixed; teams building production dependencies on Novus are accepting the risk of a cost structure change mid-roadmap, and teams with tight budget predictability requirements are better served by a tool with announced pricing. The automated instrumentation model assumes Novus can adequately represent your event taxonomy — teams with mature, deeply customized analytics schemas tied to external data warehouses or BI pipelines will hit a compatibility ceiling and either maintain a parallel manual instrumentation layer or migrate to a purpose-built pipeline tool.

Bottom line

Only Novus exposes a public API. Choose based on which difference matters most for your workflow.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.