Category managers are flying blind because the data they need lives in the wrong places. Mining it is manual, slow, and incomplete.
External intelligence is scattered across analyst reports, trade publications, consulting briefs, and social channels. Mining it is manual, slow, and incomplete.
By the time research is read, summarized, and turned into a slide, it's stale and it still hasn't been connected to what's actually happening in your own scan data.
Your proprietary POS data sits in a warehouse. It tells you what sold last week but doesn't tell you why the category is moving, where it's going, or what signals to watch.
Business Impact
The research takes so long to gather, synthesize and distill, by the time it's done it's stale and results in action that comes too late, or inaction.
You're missing out on trends and keyword movement signals that could represent revenue opportunies.
Teams enter buyer meetings, innovation reviews, and brand planning cycles without a current, defensible view of their categories.
The Kana Solution
Kana's Category Intelligence Hub ingests and distills external category signals and your proprietary scan data in a single conversational interface to help Category Managers answer questions in plain English and get grounded, sourced answers in seconds, not days.
How It Works
The system pulls from analyst reports, trade publications, search trends, and social signals on an automated schedule.
An LLM extraction pipeline transforms raw content into structured, queryable intelligence: named trend signals, directional sentiment, forward-looking predictions, and category driver attributions, each with a verbatim evidence quote for auditability.
Those signals are then automatically joined to your internal scan data, so every answer tells you not just what the market is saying, but whether your own numbers confirm it, contradict it, or are inconclusive.
How You Benefit
Every external signal is automatically cross-referenced against your proprietary scan data. Contradictions are flagged immediately.
Search trend data is ingested and analyzed weekly. Spike detection flags category keyword movements weeks before they show up in POS data so your team can act before competitors.
Every signal in the system carries a verbatim excerpt from its source document. Every numerical claim in a response traces to a specific row in a table.
Signals are clustered automatically. Responses surface how many independent sources corroborate a claim, giving category managers a way to distinguish a confirmed industry theme from a single analyst's opinion.
Category managers can ask questions the way they think. The system reasons over the available data, queries the right sources, and returns a structured answer with citations.
Learn About
Kana’s Features
Kana FAQ
Frequently asked questions and answers about agentic AI, agentic marketing, Kana's agentic AI marketing platform, integrations, and enterprise workflows.
This is where different specialized agents, one for analytics, one for audience building, one for media buying, etc. collaborate. Kana's symphony of interoperable agents are highly aligned, and loosely coupled so they share data and goals, ensuring your strategy is executed consistently across all channels.
Traditional dashboards tell you what happened last week (Retroactive). Kana’s agents tell you what is happening now and what will happen next (Predictive/Actionable), allowing you to intervene while the campaign is still live.
Kana provides strict oversight of AI model usage through multiple layers of control: data guardrails that ensure LLM prompts contain only relevant, factual context from the customer's own data; structured output validation that rejects malformed responses; multi-pass generation pipelines where outputs are iteratively refined and checked for consistency; and an AI-as-a-judge evaluation pattern where a separate model instance evaluates outputs against defined accuracy and quality rubrics before they are presented to users. These controls substantially mitigate the hallucination risks typically associated with unconstrained chatbot-style LLM usage.