Sprinklr has launched LLM Insights, a new capability within its Sprinklr Insights product designed to help brands monitor and manage how they are represented across AI-generated search results.
The issue that Sprinklr is aiming to solve is companies having almost no visibility into what large language models are telling customers about them.
Indeed, generative AI platforms are increasingly the first port of call for customers researching products and services.
Rather than clicking through search results, users are getting synthesized answers – and those answers may be incomplete, outdated, or just plain wrong.
For customer experience teams, that creates a scenario where brand perception is being shaped by a channel they can’t monitor or correct.
In discussing the news, Sprinklr’s Chief Product and Corporate Strategy Officer, Karthik Suri, said:
“Customers increasingly move from a single prompt to a synthesized recommendation often without visiting brand websites or owned channels. Representation in these platforms is a critical driver of awareness and consideration.”
“LLM Insights gives organizations the ability to understand that conversation, act on it, and be part of the answers that matter.”
The CX Problem Hidden Inside AI Search
For CX leaders, if a customer asks an AI assistant which contact center platform to use, or which brand offers the best support experience, and your company is missing from the answer – or worse, described inaccurately – you’ve lost that customer before they ever reach your website or your agents.
The beta results back this up. Early customers using the tool this spring found that AI-generated answers were actively misrepresenting their brands at critical decision points.
Competitors were being surfaced more prominently, their own products were being positioned as higher-cost alternatives, and third-party domains were reinforcing inaccurate pricing and brand narratives – all without the brands having any idea it was happening.
Using Sprinklr’s unified architecture, those teams were able to trace how social and digital signals were shaping those outcomes, giving them a way to detect emerging distortions early and take action before they spread.
Real Customer Conversations, Not Synthetic Prompts
What sets LLM Insights apart from similar tools, according to Sprinklr, is where it gets its queries from.
Rather than relying on keyword lists or synthetic prompts, the platform generates questions from real customer conversations pulled from across its unified environment, including social media, reviews, communities, and customer care interactions.
The idea is that these reflect the kinds of questions actual customers are asking AI platforms, giving brands a more accurate picture of how they are appearing in the answers that matter.
From a contact center angle, that integration with care data is particularly relevant. Customer service interactions are a rich signal for understanding the language real users use when researching or comparing brands.
Feeding that into LLM visibility analysis creates a tighter loop between what customers are asking agents and what AI platforms are telling prospective customers before they even make contact.
Insights That Connect Directly to Action
The tool also connects directly into content, knowledge, and engagement workflows, so teams can act on what they find without jumping between platforms.
Sprinklr is tracking metrics including AI mention rate, share of voice, and sentiment, and says the tool can link LLM visibility to downstream outcomes like traffic, conversions, and customer experience performance.
For organizations already using Sprinklr for brand monitoring or omnichannel service management, the pitch is that LLM Insights slots in without friction – up and running in minutes within the same environment they already rely on.
That kind of attribution will be important for making the business case internally, particularly as CX and marketing teams start to ask how AI search is affecting inbound volume and customer quality.
LLM Insights is currently in limited preview, with general availability expected in Q3 2026.
For contact center and CX leaders, the core takeaway is that if your brand is being described inaccurately in AI-generated answers, you’re already behind.
The question is whether or not you know about it.
