Market Research
Automate the tedious parts of qualitative analysis without losing the nuance. TranscriptIntel extracts themes, mines quotes, and segments respondents by behavior — not just demographics.
Qualitative research teams are drowning in transcript data while clients demand faster turnarounds.
Coding themes across hundreds of qualitative interviews is slow, subjective, and inconsistent between analysts — introducing bias at the foundation of your research.
Finding the right quotes to support findings means re-reading entire transcripts. Attribution, context, and sentiment are lost in copy-paste workflows.
Demographics alone don't explain behavior. Teams need behavioral segmentation but lack the tools to derive it systematically from interview data.
Validating research methodologies requires test data. Generating realistic synthetic transcripts that reflect genuine behavioral patterns is beyond manual authoring.
AI-powered tools designed for the workflows qualitative researchers actually use.
Identify emergent themes, cluster related topics, track prevalence across your corpus, and surface unexpected low-frequency signals that manual coding misses.
Extract key verbatim quotes tagged by theme, behavioral persona, sentiment, and certainty — with timestamps and surrounding context for immediate use in reports.
Move beyond demographics. Classify respondents into behavioral archetypes based on how they think, decide, and act — with confidence scores and source evidence.
Generate realistic interview transcripts for any domain with configurable personas, scenarios, and behavioral patterns — perfect for methodology testing and training.
AI agent crews that power market research intelligence at scale.
Accepts VTT, SRT, DOCX, PDF, plain text, and Excel uploads; detects format, validates structure, normalizes to a canonical internal schema with speaker labels and timestamps.
Identifies distinct speakers, assigns roles (interviewer vs. participant, lead vs. support), and resolves ambiguous or mislabeled speaker tags.
Classifies incoming transcripts as formal interviews, meetings, panel discussions, or other conversation types; flags completeness issues (missing intro/conclusion, truncated recordings).
Reads a transcript and classifies the participant into one (or a hybrid blend) of the six Behavioral DNA personas — Trailblazer, Evidence Harmonizer, Risk Sentinel, Support Navigator, Protocol Guardian, Operational Pragmatist — with confidence scores and source evidence.
Profiles the interviewer across the five archetypes (Explorer, Facilitator, Strategist, Connector, Analyst) using question style, follow-up patterns, rapport signals, and time management behavior.
Identifies when a participant shifts between Behavioral DNA segments during a conversation and maps the triggers for each shift.
Extracts key verbatim quotes, tags them by theme and Behavioral DNA persona, scores sentiment and certainty, and links each quote to its timestamp and surrounding context.
Maps emotional tone across the full timeline of a conversation — detecting shifts, inflection points, and the triggers that caused them (a question, a topic change, a competitor mention).
Detects mentions of competitors, products, brands, and alternatives; captures positioning comparisons, switching triggers, preference rationale, and pricing/access commentary.
Identifies emergent themes, clusters related topics, tracks topic prevalence across a corpus, and surfaces unexpected or low-frequency topics that manual reviewers often miss.
Identifies pain points, frustrations, workarounds, and wishlist items; scores each by urgency, frequency across interviews, and feasibility signals.
Reconstructs the participant's decision-making process step by step — who influenced them, what information mattered, where they got stuck.
Scores an interview across the weighted framework: question quality (25%), engagement & rapport (20%), information extraction (25%), professional skills (15%), objective achievement (15%).
Takes the upcoming interview context (topic, participant profile, objectives) and generates a tailored preparation brief: suggested question flow, persona-aware probing strategies, potential pitfalls based on the interviewer's archetype weaknesses.
Analyzes a completed interview against the prep brief and the interviewer's archetype profile; identifies excellent moments with timestamps, missed opportunities, and generates targeted micro-learning recommendations.
Takes a dimensionally-tagged transcript and builds a structured persona profile: metadata, behavioral markers, prompt directives, segment classification, and source evidence — following the modular, dimension-by-dimension approach to minimize halo contamination.
Takes a generated persona and injects contradictory evidence on one dimension, then evaluates whether the persona's other dimensions shift appropriately or collapse in lockstep.
Takes a generator config (domain, persona segment, focus asset, duration, setting, challenges) and produces a realistic VTT transcript with proper timestamps, natural dialogue flow, and behavioral authenticity.
Interactively helps users construct generator configs by asking about their domain, target persona, scenario goals, and constraints; validates the config against the schema and suggests realistic parameter combinations.
Runs across a batch of analyzed transcripts and surfaces cross-interview patterns: shifting sentiment over time, emerging themes, persona distribution skews, competitive positioning trends.
Identifies who influences the participant's decisions — named individuals, roles, organizations, peer networks — and maps the influence topology from conversational evidence.
Takes analysis outputs from transcripts across different domains and identifies structural parallels — similar decision patterns, shared behavioral archetypes operating under different terminology, transferable insights.
Scans transcripts for personally identifiable information, protected health information, and other sensitive data; flags or auto-redacts based on configurable policies (GDPR, HIPAA, CJIS).
Generates a structured executive summary with configurable depth and target audience — key findings, methodology notes, and confidence indicators drawn directly from transcript evidence.
Maps utterance-level emotion, speaker stance, and valence-arousal-dominance (VAD) signals across the conversation, producing a smoothed emotional trajectory linked to topics and inflection points.
Generates behavioral DNA persona segments for any domain — demographic markers, behavioral traits, and decision-making styles — that drive synthetic respondent behavior in transcript generation.
Synthesizes a thematic analysis report from cross-transcript evidence, clustering findings by theme with supporting quotes and confidence scoring across a corpus of interviews.
Request a live demo to see how TranscriptIntel scales qualitative research without sacrificing depth.
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