Pharmaceutical & Biotech
Transform HCP and patient interviews into structured behavioral intelligence. From adverse event detection to KOL influence mapping, TranscriptIntel automates the analysis that pharma teams need at regulatory-grade accuracy.
Pharmaceutical interview analysis demands precision at scale — and current workflows can't deliver both.
Manual coding of healthcare professional interviews is time-consuming, error-prone, and expensive — requiring specialized analysts who are in short supply.
Regulatory obligations require identifying adverse events and safety signals in every patient and HCP interview. Manual review risks missing critical mentions buried in long conversations.
Key opinion leader references are scattered across hundreds of interviews. Mapping who influences prescribing behavior requires cross-referencing statements that manual processes can't sustain.
AI-generated personas suffer from halo effect contamination — positive sentiment on one dimension bleeds into others, producing unrealistically consistent synthetic respondents.
Purpose-built capabilities for pharmaceutical conversation intelligence.
Classify HCPs into behavioral archetypes based on prescribing behavior, evidence evaluation style, and decision-making patterns — with confidence scores and source evidence.
Original research-backed approach that evaluates each dimension independently, injects counterfactual evidence, and flags when persona outputs show artificial consistency.
Identify speakers, classify roles, track sentiment progression, and profile behavioral patterns across multi-stakeholder interviews including HCPs, patients, and payers.
Automatically identify emergent themes, cluster related topics, track prevalence, and surface unexpected low-frequency signals that manual reviewers consistently miss.
AI agent crews spanning core analysis, behavioral intelligence, and pharma-specific workflows.
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.
Tags transcript segments by evaluation dimension (efficacy perception, safety, prescribing behavior, formulary, competitive landscape, etc.) and produces a Coverage Map showing which dimensions have direct evidence and which are gaps.
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 (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).
Reviews persona outputs and analysis reports against regulatory guardrails: flags claims not supported by transcript evidence, identifies language that could be misinterpreted as real HCP testimony, and checks that confidence labels are attached.
Scans HCP and patient transcripts for mentions of adverse events, side effects, safety concerns, near-misses, and off-label consequences — tagging each by severity, causality language, and attribution.
Extracts the specific factors an HCP weighs when choosing a treatment — efficacy data thresholds, safety tolerability, dosing convenience, formulary status, patient profile fit, prior authorization burden — and ranks them by decisiveness.
Identifies every mention of formulary hurdles, prior authorization friction, step-therapy requirements, payer pushback, reimbursement challenges, and patient cost burden — and links each to the specific payer type or access pathway discussed.
Detects when an HCP references specific colleagues, thought leaders, society guidelines, conference presentations, or institutional protocols that influenced their opinion — building a map of who and what shapes prescribing behavior.
Processes patient interview transcripts to extract the lived experience: symptom burden, emotional journey, caregiver dynamics, treatment expectations vs. reality, adherence patterns, and language the patient actually uses.
Extracts adherence-specific intelligence from patient transcripts: self-reported adherence, reasons for missing doses, practical/emotional/financial barriers, coping strategies, support systems, and medication beliefs.
Processes patient interviews to score trial participation readiness across logistical, clinical, and psychological dimensions — surfacing motivations, concerns, deal-breakers, and information needs.
Evaluates how an HCP received a specific message or value proposition — tracking engagement signals, objection triggers, areas where the message landed vs. fell flat, and the HCP's restatement in their own words.
Auto-detects the therapeutic context of a conversation — disease state, patient population, treatment line, relevant biomarkers — and maps conversational cues to current treatment guidelines and society recommendations.
Catalogs every objection an HCP raises about a product, classifies each by type and severity, and cross-references with the HCP's Behavioral DNA to predict which objection-handling approach is most likely to work.
Extracts the HCP's actual prescribing patterns from interview evidence: what they prescribe first-line vs. second-line, what triggers a switch, what makes them loyal to a brand, and their comfort level with different drug classes.
Processes payer/formulary committee interview transcripts — extracting coverage criteria, cost-effectiveness thresholds, HEOR evidence requirements, preferred step-therapy pathways, and the specific objections payers raise against new entries.
Takes a panel of generated personas and evaluates whether the panel has sufficient diversity, genuine independence between personas, and realistic disagreement potential — flagging halo contamination, missing perspectives, and gaps in Behavioral DNA coverage.
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.
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