CORE CAPABILTIES
LATEST MEANINGFUL FEATURES
THREADS
Mechanism for explicit, hypothesis-driven analysis.
Test strategic ideas, assumptions, and concepts.
OBSERVABILITY
Framework for evaluating the quality, integrity, and reliability of reasoning in synthesis and analysis.
STORY
Personal workspace for organizing research insights. Customize outputs to tell your story.
Reach out for more information about Meaningful's latest innovations, including Meaningful Agency
Qualitative
Meaningful
Qualitative research sits at the core of Meaningful’s architecture.
Across most organizations, qualitative insight is simultaneously the most valuable and the most fragile form of intelligence. It captures nuance, motivation, emotion, and meaning—but it is difficult to scale, slow to synthesize, and often discarded once a project ends.
Meaningful is designed to change this dynamic by treating qualitative research not as a one-off method, but as a continuous signal stream that feeds institutional memory and long-term understanding.
Conversations is an AI moderated conversational tool that bridges the qualitative/quantitative divide. It goes beyond surveys to gather more in-depth data from human respondents at an accelerated scale and speed but at a fraction of the cost of qualitative solutions.
Features
Templates
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Customer segmentation
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Brand perception and positioning
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Concept and message testing
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Needs discovery and journey exploration
AI Moderator
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Uses natural, conversational language rather than survey-style prompts
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Maintains context across the entire interaction
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Adapts phrasing to respondent communication styles
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Employs progressive exploration, asking targeted follow-ups where clarity or depth is required
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Operates within strict constraints to prevent hallucination or leading responses
Participants engage via text or voice and experience the interaction as a guided conversation rather than a questionnaire.
Research design and templates
Analysis and outputs
As interviews are completed, Meaningful performs real-time, multi-dimensional analysis, including:
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Thematic analysis
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Sentiment and emotional analysis
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Narrative and language pattern analysis
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Patterns are identified across interviews while preserving:
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Individual voice and nuance
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Segment and demographic variation
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Emotional and motivational drivers
Insights generated through AI-moderated conversations are automatically connected to:
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Prior qualitative research
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Social and cultural signals
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Secondary and market intelligence
AI Moderated Conversation
Designed to amplify human expertise where it matters most, Meaningful does not attempt to replace human interactions. Instead, it removes the analytical bottlenecks that typically follow them.
Features
Interview ingestion
Researchers can upload:
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Video or audio recordings
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Virtual or in-person interviews
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Focus groups and expert sessions
Meaningful performs advanced transcription that preserves:
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Conversational flow and emphasis
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Emotional nuance
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Industry-specific terminology
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Culturally relevant expressions
Research design and templates
Analytical framework
Each interview is processed through a structured, five-layer analytical framework:
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Key narratives: The foundational truths emerging across conversations
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Supporting insights: Conditions, variations, and mechanisms that explain how narratives manifest
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Evidence layer: Representative and contrasting quotes, behaviors, and language patterns
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Contradictions and tensions: Inconsistencies, unmet needs, and paradoxes that often signal opportunity
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Strategic implications: Concrete actions, priorities, and decision guidance
Meaningful is designed to integrate with existing survey platforms, not replace them.
Organizations and agencies continue to design surveys using their preferred tools, manage sampling and fieldwork externally, and conduct initial statistical analysis within the survey.
Meaningful focuses on what happens after survey data is collected.
Features
Supported survey integrations
Meaningful can connect to widely used quantitative survey platforms, including:
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PG Forsta (Decipher)
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Qualtrics (coming soon)
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SurveyMonkey (coming soon)
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Typeform (coming soon)
Researchers can upload:
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Survey responses are imported automatically once fieldwork is complete.
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The goal is not to re-run quantitative analysis inside Meaningful, but to bring structured survey outputs into the broader intelligence context.
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Video or audio recordings
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Virtual or in-person interviews
Focus groups and expert sessions
Meaningful performs advanced transcription that preserves:
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Conversational flow and emphasis
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Emotional nuance
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Industry-specific terminology
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Culturally relevant expressions
Analytical framework
Once ingested, quantitative survey data becomes part of the unified intelligence layer.
This allows teams to:
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Connect quantitative findings to qualitative narratives and language
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Validate qualitative insights against scaled responses
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Track how themes identified in interviews manifest across larger populations
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Revisit survey results as new qualitative or social data emerges
Survey data is treated as a living input, not a static deliverable.
Complementary, not competitive
Meaningful does not attempt to replicate the advanced statistical tooling of survey platforms. Instead, it complements them by:
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Preserving survey results alongside other research inputs
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Providing context that traditional survey dashboards lack
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Enabling longitudinal interpretation across studies and methods
This respects existing workflows while dramatically increasing the downstream value of quantitative research.
Secondary research is typically treated as a periodic, manual task. Analysts conduct desk research at the beginning of a project, summarize findings into slides, and then move on. Once delivered, this context quickly becomes outdated and disconnected from future work.
Meaningful is designed to operate secondary research as continuous strategic intelligence, integrated directly into the same system as primary research and analysis.
Features
From desk research to living context
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Continuously ingests relevant external information
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Structures it around defined strategic questions
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Preserves sources, reasoning, and assumptions
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Connects new developments to existing insight
Typical investigation areas include:
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Market structure and dynamics
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Competitive positioning and behavior
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Industry trends and innovation signals
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Regulatory, policy, or economic factors
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Cultural and societal context
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Multi-level scope
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Local or regional context
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National market dynamics
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Global trends and forces
Evidence and traceability
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Source attribution
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Supporting evidence
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Clear differentiation between fact, interpretation, and implication
Integration with primary research
Secondary research in Meaningful is continuously connected to:
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Qualitative narratives and emotional drivers
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Quantitative findings and measurement
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Social and cultural signals
Secondary research often determines whether insight is situational or strategic.
By integrating secondary intelligence directly into the same system as primary research, Meaningful ensures that customer understanding is always interpreted in context—and that context is never lost.
Meaningful treats social content as a continuous qualitative signal stream that complements primary research and grounds insight in real-world language and behavior.
Features
Authentic voice over abstract metrics
Meaningful prioritizes how people actually speak, not how platforms classify them. The system captures:
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The language people use to describe problems and experiences
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Emotional expressions tied to specific moments and decisions
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Comparisons between options, brands, or alternatives
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Implicit expectations revealed through complaints, questions, and advice-seeking
Source-aware collection
Meaningful ingests social and cultural data from a range of sources, including:
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Community forums and discussion platforms
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Reviews and feedback channels
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Professional and industry discourse
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Open social platforms
Intelligent segmentation and pattern detection
Social data in Meaningful is analyzed using intelligent segmentation, not simple keyword tracking.
Longitudinal insight and early signal detection
Because social data is ingested continuously, Meaningful enables longitudinal analysis rather than snapshot reporting.
Analysts can:
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Track how narratives evolve over time
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Detect weak signals before they become obvious trends
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Observe how external events influence perception and behavior
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Compare emerging social narratives to historical qualitative and survey findings
Integration with other intelligence streams
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Qualitative interview narratives
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Quantitative survey findings
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Secondary and market intelligence
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Historical research and institutional memory
The most powerful insight systems are not built solely on research outputs. They emerge when proprietary data, external signals, and expert interpretation are brought together in a single analytical context.
Meaningful is designed to support this through a flexible system of external data connectors and document ingestion, allowing organizations and agencies to unify all relevant sources of intelligence—without displacing existing tools or compromising governance.
This capability is foundational to advanced Insights as a Service (IaaS) models.
Features
From research inputs to integrated intelligence
Most organizations already possess significant volumes of valuable data. Meaningful enables them to be combined in real time, interpreted together, and preserved as part of a continuously evolving intelligence layer.
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Customer and account data in CRM systems
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Behavioral and transactional data in internal databases
- Quantitative survey results held in third-party platforms
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Historical research, reports, and analysis stored in documents
External system connectors
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CRM platforms
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Databases and data warehouses
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Survey platforms
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Analytics and business intelligence tools
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Internal reporting and knowledge systems
Privacy, governance, and control
Meaningful is designed to support enterprise-grade privacy and governance requirements.
For example:
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Data can be ingested using unique identifiers (UIDs) rather than personally identifiable information (PII)
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Sensitive fields can be excluded, abstracted, or hashed prior to ingestion
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Client and agency data can remain logically separated while still being analyzed together
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Access can be scoped by role, project, or organization
This allows agencies and brands to collaborate deeply without compromising ownership, confidentiality, or compliance.
External document ingestion and enrichment
In addition to live system connectors, Meaningful supports the ingestion of external files and documents, including:
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PDFs and research reports
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PowerPoint presentations
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Excel and CSV files
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Text, markdown, and structured data formats
These materials are often where critical context lives: past studies, strategy documents, benchmarks, or working analyses that never make it into formal systems.
Rather than treating documents as static attachments, Meaningful:
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Parses and indexes their content
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Integrates them into live synthesis
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Preserves them as part of institutional memory
This allows analysts to enrich ongoing research with historical and contextual knowledge, ensuring that synthesis is cumulative rather than repetitive.
Agency-led intelligence ecosystems
This capability becomes particularly powerful in an agency context. An agency can:
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Connect a client’s proprietary data
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Integrate its own proprietary benchmarks or historical research
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Combine both with external market, social, and cultural signals
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Apply expert interpretation and strategic framing
The result is a shared intelligence ecosystem that neither the client nor the agency could create alone.
Data Connectors
Collecting data is not the hard part of insight work. The hard part is making sense of it across time, sources, and uncertainty. Live synthesis is Meaningful’s answer to this challenge.
Rather than producing static summaries or one-off reports, Meaningful continuously synthesizes all available intelligence—qualitative, quantitative, social, secondary, and proprietary—into a coherent, evolving understanding of the problem space.
This synthesis is always grounded in evidence, transparent in its reasoning, and designed to support expert interpretation.
Features
From aggregation to synthesis
Most research systems aggregate information. They display findings side by side and leave the burden of connection entirely to the analyst.
Meaningful is designed to go further. Live synthesis actively:
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Identifies patterns that recur across sources
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Connects narratives, behaviors, and structural forces
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Highlights changes over time rather than isolated moments
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Surfaces contradictions and tensions as signals, not errors
The goal is not to replace human synthesis, but to make synthesis continuous and scalable.
Cross-source pattern recognition
Live synthesis operates across all intelligence streams simultaneously, including:
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Primary qualitative interviews
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Quantitative survey findings
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Social and cultural signals
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Secondary and market intelligence
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Client and agency proprietary data
Patterns are identified where themes, behaviors, or narratives appear consistently across sources and contexts.
Evidence triangulation and confidence assessment
A core principle of Meaningful’s synthesis process is explicit triangulation.
Each synthesized insight is evaluated based on:
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The number of independent sources supporting it
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The methodological strength of those sources
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The consistency or tension between them
Analysts can:
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Track how themes evolve over time
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See when narratives strengthen, weaken, or fracture
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Identify early indicators of change
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Revisit prior conclusions as new evidence emerges
This prevents outdated assumptions from persisting simply because they were once true.
Analyst-controlled interrogation
Live synthesis is interactive, not fixed.
Analysts can:
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Ask new questions of the existing intelligence base
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Explore alternative explanations
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Drill into evidence supporting or contradicting a conclusion
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Reframe synthesis around new strategic priorities
This ensures that synthesis remains a dialogue between system and expert, not a static output
Meaningful transforms
how research is conducted, interpreted, and applied
Contact us
Meaningful transforms research into a living, evolving intelligence engine — one that captures real human nuance and compounds over time. By scaling depth, not just data, Meaningful empowers brands, agencies, and researchers to move beyond surface-level answers and deliver strategic clarity and insight their clients can’t get anywhere else.
thinqinsights was an early adopter of Meaningful and it has become an indispensable part of our research process. Partnering with Meaningful, we not only thinqinsights, we ignite insights.
For more information about Meaningful products, pricing or to request a demo, or just to chat about Meaningful, contact us.
