Context
Kore.ai is an enterprise-grade no-code and agentic AI platform adopted across industries such as banking, healthcare, and telecom, and by Fortune 500 organizations including UnitedHealth, Citi, and AT&T. Model-agnostic and multi-channel, it integrates seamlessly with existing technology stacks like Salesforce Service Cloud and ServiceNow, with a focus on end-to-end orchestration and measurable ROI. Recognized consistently as a Leader in analyst reports — including Forrester Wave™ (Cognitive Search) and the Gartner® Magic Quadrant™ (Conversational AI Platforms) — Kore.ai positions itself as both a user- and builder-centric solutions platform.
Kore.ai’s pre-built conversational solutions were created to help enterprises launch intelligent virtual assistants quickly, each tailored to the unique workflows of industries like, but not limited to, Banking, HR and Healthcare. My work focused on refining these flows into cohesive, ready-to-deploy experiences that improved usability and reduced time to launch, from high-level industry templates to industry agnostic agent flows for everyday usage.
Outcome
The refinement and expansion of Kore.ai’s pre-built conversational flows improved usability along with speed to launch across multiple industry verticals. By streamlining builder interactions, clarifying flow logic, and standardizing end-user experience, we reduced configuration hurdles and enabled enterprises to go live with agents much faster. Real User Feedback (G2 Revews ) underscores the value of Kore.ai’s low-code/no-code design, emphasizing templates that “save time and make setup quick and efficient.” With an average implementation time of roughly two months (G2), Kore is very competitive against the typical 2–4 month (V. Yellow.ai / G2 and Genesys / G2) or atypical 3-6 month (V. Quokka Labs) enterprise conversational AI deployment. In part because of that, Kore enjoys both favorable retention rates, and user satisfaction scores.
Rapid platform implementation with Kore.ai’s template-driven builds delivers an average 2–5x faster launches, with potential gains of up to 10x, over bespoke solutions (via internal build testing).
75+ Pre-Built Solutions for users that span verticals and workflows. Each provides logic and integrations that reduce build effort, speed deployment, and ensure consistent user experiences at scale.
High satisfaction rating (4.6/5). Gartner Peer Insights shows Kore.ai holds an average rating of 4.6/5 from 128 verified enterprise reviews, reinforcing strong real-world satisfaction for both CX and EX use cases.
Given the rapid acceleration of AI platforms, this study is a snapshot in time and as such, deployment speed and other metrics will likely age to a point that atypical results in this study are typical. As this study also covers multiple design systems and brand alignments, which is not hte focus, there will be a range of designs that speak to elements referenced.
Problem
Kore.ai’s pre-built solutions covered several key industries and common use cases, but there was room to expand their reach and adaptability. As the platform evolved, enterprise clients began seeking more nuanced flows that reflected emerging verticals and specialized processes. While the existing templates performed reliably, they sometimes left gaps where industry-specific logic or conversational depth could be strengthened. Accessibility and minor error-handling inconsistencies also emerged as the library grew, highlighting opportunities to make interactions clearer and more inclusive. The challenge was to extend the framework so it could support more complex workflows without increasing setup time or sacrificing the clarity that made the templates effective in the first place.
Process/Research
The project began with research focused on how builders and end-users interacted with the pre-built templates in real scenarios. Through feedback reviews and observation of configuration sessions, the team identified points where users hesitated, had accessibility issues or needed additional clarity to complete builds. Conversations with clients revealed a growing interest in broader and more specialized template options to better match evolving industry needs. Patterns in user behavior also pointed to areas where the organization of templates and the presentation of configuration steps could be clearer.
Process/Design
New templates were created to address emerging industry needs, while existing ones were refined to streamline conversation paths and reduce redundant logic. Prototypes explored new ways to visualize branching, entry points, and configuration steps, adding clearer flow mapping and inline cues that helped builders see how each template connected to larger processes. Consistent structure, clearer naming, and guided editing patterns were enforced in templates to reduce points of confusion and complexity. These updates established a more scalable framework for future template creation.
Process/Validation
To understand how the updates performed against insights from earlier research, the new templates were compared to previously observed patterns of builder behavior and flow clarity. For templates without existing benchmarks, observation and direct feedback were gathered at multiple stages of the project to evaluate comprehension and usefulness. Pilot launches then validated these directions in live environments, showing how the updates performed once these templates were being utilized by the user base. Having an active and invested community allows a quick pulse of feedback that perhaps, other companies may or may not benefit from. That being said, one of the key metrics for success for any project, big or small, is the continued recognition of analysts like Forrester and Gartner. Every right step, across all departments at whole, contribute to that support.
Wrap-Up
This project showed that scaling pre-built conversational solutions is less about quantity and more about clarity, consistency, and adaptability. The effort was driven by close collaboration between customer experience and product teams to ensure each template aligned with both technical feasibility and real user needs. Because much of this work involved proprietary tools and internal systems, collaboration and continuous feedback were key features of this particular project.