Gedank RayzeTranscriptIntel
AI-Powered Transcript Analysis

Conversation Intelligence for Any Industry

Upload interview transcripts, run multi-agent analysis powered by IronCrew, and generate synthetic conversation data. Built for forensics, pharma, market research, and beyond.

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Platform Capabilities

Three core pillars that power the platform.

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Upload & Parse

Ingest VTT, SRT, and plain text transcripts up to 50MB. Automatic format detection, speaker extraction, and normalization to a canonical schema.

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AI Analysis

Run 9 analysis types through IronCrew's multi-agent runtime: summary, topics, quotes, sentiment, speakers, behavioral DNA, interviewer scorecard, emotion arc, and decision journey.

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Synthetic Generation

Generate realistic interview transcripts from behavioral segment profiles. Configure persona, domain, duration, and scenario for authentic VTT output.

Use Cases

Domain-agnostic by design. Here are three verticals already in use.

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Forensics

Analyze witness interviews, suspect interrogations, and field reports. Detect behavioral patterns, extract key quotes with timestamps, and score interviewer technique.

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Pharma

Process HCP and patient interviews. Map prescribing behavior, detect adverse event signals, and build synthetic personas for advisory board simulations.

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Market Research

Extract competitive intelligence, map decision journeys, and surface unmet needs from consumer and B2B interviews at scale.

Technology

The infrastructure behind the analysis.

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IronCrew

Multi-agent orchestration runtime in Rust. Stateless compute with SSE streaming, model routing, prompt caching, and per-run abort.

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IronFlow

DAG-based workflow engine in Rust with 96 built-in nodes. Runs complex multi-step pipelines with parallel execution, retry logic, and conditional routing.

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transcribeit

Local-first transcription CLI in Rust. Speaker diarization, VAD segmentation, 4 providers. Audio never leaves your machine.

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Cognitive-OCR

Document extraction engine in Rust. Transforms PDF, DOCX, PPTX, and images into structured Markdown using local ONNX models and vision-language models.

Ready to See It in Action?

Schedule a walkthrough with our team. We will show you the full pipeline on your own transcripts.

Request Demo