Digital Forensics
Scale investigative interview analysis without sacrificing depth. Built by a team with roots in digital forensics tooling — including engineering experience at Cellebrite — TranscriptIntel brings behavioral intelligence to the interviews that matter most.
Investigative interview analysis hasn't kept pace with the volume of digital evidence.
Analysts spend hours per transcript reviewing witness and suspect interviews. Fatigue leads to missed details, inconsistent coding, and delayed case progression.
Patterns of deception, evasion, and contradiction are easily missed by fatigued analysts working through long interviews under time pressure.
Complex investigations involve hundreds of interviews across multiple subjects. Manually cross-referencing statements and extracting actionable intelligence is unsustainable.
TranscriptIntel maps behavioral patterns, extracts evidence, and profiles subjects automatically.
Automatically identify distinct speakers, assign roles (interviewer, suspect, witness, counsel), and resolve ambiguous or mislabeled speaker tags across interview recordings.
Map emotional tone across the full timeline of an interview — detecting shifts, inflection points, and the triggers that caused behavioral changes in subjects.
Classify interview subjects into behavioral archetypes with confidence scores and source evidence, revealing patterns across multiple interviews with the same subject.
Extract key verbatim statements with timestamps, tag them by theme and behavioral profile, and score sentiment and certainty for rapid case review.
Specialized AI agents that power forensic interview intelligence.
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).
Identifies emergent themes, clusters related topics, tracks topic prevalence across a corpus, and surfaces unexpected or low-frequency topics that manual reviewers often miss.
Scores an interview across the weighted framework: question quality (25%), engagement & rapport (20%), information extraction (25%), professional skills (20%), objective achievement (10%).
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).
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