Customer Intelligence Hub: Every Conversation Compounds
Stop losing 90% of your research insights. Build a searchable intelligence system that gets smarter with every study.
What Is a Customer Intelligence Hub?
A customer intelligence hub transforms every customer conversation into searchable, compounding knowledge. Unlike project-based research tools where insights disappear into slide decks, a customer intelligence hub builds institutional memory that gets smarter with every study, survives team changes, and feeds directly into decision-making.
How Is This Different from a Research Repository?
A research repository stores files. A customer intelligence hub structures, connects, and compounds knowledge from every conversation. It translates messy human narratives into a standard ontology, enables cross-study pattern recognition, and makes intelligence queryable by anyone on the team — not just the researcher who ran the original study.
Does it just store transcripts?
No. Every conversation is processed through a structured ontology — emotions, motivations, competitive mentions, and jobs-to-be-done are extracted and indexed. You search structured intelligence, not raw text.
Can I search across all past studies?
Yes. Query the hub conversationally across every study, every segment, every time period. Ask 'What do enterprise buyers say about our pricing vs. Competitor X?' and get answers grounded in real verbatim.
How does it compound over time?
Each new conversation enriches the system. Patterns emerge across studies. New hires access years of insights on day one. Teams validate new findings against historical data. The dataset becomes a proprietary moat.
The $40B Problem: Insights That Disappear
Organizations spend billions on customer research every year. Over 90% of that knowledge vanishes within 90 days.
Trapped in Slide Decks
Research findings get filed in shared drives and forgotten. The insight from Q2's churn study is sitting in a PDF that nobody will open again — while the product team re-asks the same question.
Walks Out the Door
When a senior researcher leaves, years of contextual knowledge leave with them. New hires start from zero, missing patterns that the organization already paid to discover.
Project Silos
Each study starts from scratch. The win-loss team doesn't see churn interview patterns. The UX team doesn't know what consumers said about the same feature in brand tracking. Cross-study intelligence doesn't exist.
Expensive Re-runs
Teams re-run $50K studies quarter after quarter because there's no way to query what past research already revealed. The organization pays for the same insight twice — or three times — because nothing compounds.
Representative Outcomes
What teams measure after switching to AI-moderated research.
New hires access years of customer intelligence on day one
Historical queries replace redundant research projects
Every insight traced to real verbatim — no hallucinations
Intelligence gets smarter with every conversation
What the Intelligence Hub Includes
Conversational Querying
Ask questions in plain language across all historical research. Get answers grounded in real participant verbatim, not model-generated summaries.
Structured Consumer Ontology
Every conversation is processed into structured, machine-readable insight: emotions, motivations, competitive mentions, jobs-to-be-done. Making insights comparable across studies and time.
Cross-Study Pattern Recognition
Surface patterns that no single study could reveal. See how churn drivers correlate with win-loss themes, or how UX friction maps to shopper behavior.
Evidence Trails & Citations
Every finding traces back to specific verbatim quotes from real participants. No hallucinated personas. No model-remixed training data. Explainable, auditable, commercially defensible.
Team-Wide Access
Product, marketing, sales, and leadership all access the same intelligence system. Insights routed via Slack, email, and integrated workflows.
MCP & API Integration
Feed customer intelligence directly into ChatGPT, Claude, and other AI tools via MCP. Connect to data warehouses, CRMs, and automation platforms.
How Customer Intelligence Compounds
From raw conversation to searchable, compounding intelligence.
Every Conversation Is Processed
Each interview passes through a multi-stage pipeline: intent extraction, emotional scoring, competitive detection, jobs-to-be-done mapping, and evidence-based synthesis.
Intelligence Compounds
New conversations are indexed against the entire history. Cross-study patterns surface automatically. The system gets smarter with every study you run.
Query and Act on Intelligence
Ask questions across all historical research in plain language. Get answers grounded in real verbatim. Route insights to teams via Slack, email, or API.
Customer Intelligence Hub vs.
Research Repos vs. CRM Intelligence
| Dimension | Customer Intelligence Hub (User Intuition) | Research Repositories | CRM Intelligence |
|---|---|---|---|
| Data type | Structured qualitative intelligence | Unstructured files (PDFs, decks) | Transactional and behavioral data |
| Queryability | Conversational, cross-study | File search, keyword-based | SQL/dashboard queries |
| Depth of insight | Emotional motivations, root causes | Whatever the researcher wrote up | What happened, not why |
| Compounds over time | Yes — each study enriches the system | No — each study is a separate file | Partially — data accumulates, no synthesis |
| Survives team changes | Yes — knowledge is in the system | Partially — depends on documentation | Yes for data, no for context |
| Evidence quality | Real verbatim, cited and auditable | Depends on report quality | Metrics without customer voice |
| Cross-functional access | Any team member, plain language | Research team gatekeepers | Requires analytics skills |
Grounded in Conversations, Not Hallucinations
Digital twins and synthetic personas fail because they remix training data, amplifying demographic skews and brand-familiarity biases. User Intuition grounds every insight in verified customer conversations.
How the Ontology Works
- Multi-stage processing: intent, emotion, competition, JTBD
- Structured, machine-readable output from unstructured conversations
- Standard ontology makes insights comparable across studies and time
- Every insight indexed for cross-study pattern recognition
- Evidence trails link findings to specific verbatim quotes
- Proprietary truth that no LLM benchmark can replicate
Why This Matters
- 3% of devices complete 19% of all surveys — quantitative data is compromised
- LLMs hallucinate consumer insights — our data is grounded in real conversations
- Research insights have a 90-day half-life — our system makes them permanent
- New hires access years of customer knowledge on day one
- Cross-study patterns reveal trends no single study could surface
- Intelligence that is explainable, auditable, and commercially defensible
Every answer comes with citations to real participants, not model-hallucinated personas.
Frequently Asked Questions
Stop Losing 90% of Your Research Insights
See how the customer intelligence hub compounds knowledge from every conversation.
Every conversation makes your intelligence more valuable.