For years, the experience of interacting with a major brand has felt like a game of telephone. A customer tweets a complaint, a social media manager moves it to a support ticket, and by the time a specialist joins the conversation, the customer is asked to repeat their story for the third time. This fragmentation isn’t a failure of will, but a failure of architecture.
Khoros is attempting to solve this “context gap” with the launch of Iris AI, a system designed to serve as a unified messenger across all brand channels. Unveiled on April 11, 2026, the platform integrates social listening, publishing, engagement, and analytics into a single intelligent ecosystem, aiming to replace the disjointed toolsets that have defined brand management for over a decade.
The launch comes less than a year after Khoros was acquired by IgniteTech in May 2025. Since the acquisition, the company has undergone a fundamental structural shift, moving away from treating artificial intelligence as a plugin and instead rebuilding its entire software foundation on an AI-native codebase.
Closing the Context Gap in Brand Care
The primary friction point in modern customer engagement is the loss of data during handoffs. When a query migrates from a public social stream to a private support queue, the nuance of the original interaction often vanishes. Iris AI is designed to eliminate this by ensuring that the context of a conversation follows the user from the first point of contact to the final resolution.
According to Eric Vaughan, CEO of IgniteTech and Khoros, most existing brand care platforms are essentially patchwork systems. He noted that many companies use social listening tools that cannot communicate with their processing queues, resulting in analytics that arrive weeks after an event has passed. Iris AI is positioned as a “single source of truth” to prevent this lag.
The platform’s AI engine handles “intelligent triage,” analyzing incoming requests based on sentiment, urgency, and topic. It then routes the interaction to the agent best equipped to handle it. If the AI can resolve the issue independently, it does so; if a human is required, the agent receives the full history of the interaction, removing the need for the customer to repeat themselves.
From Legacy Code to AI-DNA
From a technical perspective, the transition to Iris AI represents a massive cleanup of “technical debt.” For a company formed from the merger of Lithium Technologies and Spredfast, the software environment was a complex layer of legacy systems accumulated over 15 years. IgniteTech claims to have rewritten this entire codebase into a unified, AI-generated system in less than a year.
This architectural overhaul has already shown internal results. Since the May 2025 acquisition, IgniteTech reports that its internal support resolution rate climbed from 5% to 60%, while platform downtime was reduced by 97% and the support backlog dropped by 82%.
The speed of development has also accelerated. Vaughan highlighted the creation of a production-ready management platform for X (formerly Twitter) channels in just ten days, arguing that this velocity is only possible when AI is the foundation of the build rather than a feature added to the end of a roadmap.
Core Capabilities of the Iris AI Ecosystem
- Cross-Channel Publishing: AI-assisted creation and scheduling of content across platforms including Facebook, Instagram, LinkedIn, TikTok, and YouTube.
- Global Social Listening: Monitoring of billions of sources across 187 different languages.
- Real-Time Risk Mitigation: AI-powered moderation that flags brand risks and sentiment shifts before they escalate into crises.
- Zero-Migration Integration: The system is designed to be fully data-compatible, allowing existing Khoros customers to move to the fresh platform without traditional data migration.
The Broader Customer Engagement Strategy
Iris AI does not operate in isolation. It’s one half of a larger AI-native strategy that includes Aurora AI, a specialized product for community management. Together, they bridge the gap between public social media streams and private community forums.
This dual approach is a strategic move to challenge the dominance of other public companies in the space. Vaughan specifically pointed to competitors like Sprout Social and Sprinklr, suggesting that their lack of integrated community-and-social intelligence at the data level has led to losses in the enterprise sector.
| Feature | Legacy Toolsets | Iris + Aurora AI |
|---|---|---|
| Context Flow | Fragmented/Lost during handoff | Unified across all touchpoints |
| Development Cycle | Quarterly Roadmap updates | Rapid AI-prototyping iterations |
| Language Support | Variable/Plugin-based | Native support for 187 languages |
| Architecture | Layered legacy code | AI-native codebase |
A Shift in Software Development
The development of Iris AI also signals a departure from traditional enterprise software sales. Rather than presenting slide decks and theoretical roadmaps, Khoros utilized AI-powered prototyping to create functional previews for customers. This allowed the users themselves to validate features and prioritize the build in real-time, effectively turning customers into co-designers.
This transformation is further symbolized by the move to khoros.ai. The company describes the domain change not as a simple rebranding, but as a signal that the organization has been fundamentally restructured around AI.
The next phase for the platform involves the deeper integration of the “intelligence layer” between Aurora and Iris, which is intended to learn a brand’s specific voice and customer behavior over time to further automate complex interactions.
Do you reckon AI-native architecture will finally end the frustration of repeating yourself to customer support? Share your thoughts in the comments below.
