Seven analysis types, 38 AI agents, multi-provider LLM routing, and a synthetic data pipeline. Every feature built to work across any industry.
Every transcript can be analyzed across seven complementary dimensions.
Generates a structured executive summary with key findings, methodology notes, and confidence indicators.
Identifies emergent themes, clusters related topics, and tracks prevalence across a corpus of transcripts.
Extracts key verbatim quotes tagged by theme, persona, sentiment, and certainty with full timestamp context.
Maps emotional tone across the full conversation timeline, detecting inflection points and their triggers.
Identifies distinct speakers, assigns roles, resolves mislabeled tags, and calculates participation metrics.
Classifies participants into behavioral personas with confidence scores and source evidence from the transcript.
Scores interview quality across 5 dimensions: question quality, engagement, information extraction, professional skills, and objective achievement.
Purpose-built Rust tools that power every analysis.
Multi-agent runtime in Rust. Stateless compute with SSE streaming, model routing, prompt caching, error recovery, and per-run abort. Each analysis runs as an independent flow.
DAG-based workflow engine in Rust with 96 built-in nodes. Handles complex multi-step pipelines — document ingestion, data transformation, API calls, AI processing — with parallel execution and retry logic.
Rust CLI for privacy-first speech-to-text. Speaker diarization, VAD segmentation, 4 providers (whisper.cpp, sherpa-onnx, OpenAI, Azure). Audio never leaves your machine.
Cognition-aware extraction engine in Rust. Transforms PDF, DOCX, PPTX, and images into structured Markdown using local ONNX layout models and vision-language models. Provides document context for transcript analysis and generation.
8 implemented, 1 partial, 29 planned across 8 categories.
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).
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 (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).
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.
Bring your own keys. Route each purpose to the best model.
OpenAI, Anthropic, and any OpenAI-compatible endpoint (Azure, Together, Ollama, LM Studio). Configure credentials per provider with AES-256-GCM encryption at rest.
Each purpose can route to a different model: Analysis (deep reasoning), Generation (creative output), Embedding (vector search), and Validation (quality checks).
Store API keys securely in the platform. Each key is encrypted with AES-256-GCM and scoped to your account. Test connectivity before saving.
Save model configurations as reusable presets. Set temperature, max tokens, and system prompts per use case. Switch between presets without reconfiguring.
Generate realistic interview data for testing, training, and simulation.
Define behavioral segments for any domain. The generator creates persona profiles with demographic markers, behavioral traits, communication patterns, and decision-making styles.
Segments drive respondent behavior in VTT generation. Generate segments first, then configure transcript scenarios that produce authentic dialogue.
Configure transcript generation with domain, persona segment, focus asset, duration, setting, and challenges. The pipeline produces properly timestamped VTT files with natural dialogue flow.
13 seed configurations included for common scenarios. Create custom configs for any domain or research context.
Request a demo to walk through every analysis type, agent flow, and generation pipeline on your own data.