The AI Podcast Research Tool Built for Journalists

Modern investigative journalism relies heavily on spoken content. Experts share insights on podcasts long before publishing papers. Executives reveal strategy on earnings calls and YouTube interviews. Politicians make promises in town halls that contradict previous statements. The core problem is that spoken content is nearly impossible to search, verify, or cross-reference at scale using traditional newsroom tools and workflows.

A journalist investigating a pharmaceutical company might need to review 200+ hours of podcast interviews, earnings calls, and conference presentations to track how the company's safety claims evolved over time. Doing this manually would take weeks of full-time effort. With VERIDIVE, the same analysis takes hours, with higher coverage and accuracy.

VERIDIVE was designed to handle exactly this kind of research challenge. By combining automated transcription, entity extraction, speaker attribution, and cross-source verification, the platform transforms unstructured spoken content into a searchable evidence database that journalists can query conversationally through DeepContext.

VERIDIVE's VERIdex indexes transcripts from over 2,000 curated podcast and video sources, making them searchable with full-text and semantic search capabilities. Journalists can search by:

• Speaker name: Find every statement made by a specific person across all indexed sources
• Topic or claim: Search for specific claims, statistics, or arguments across thousands of episodes
• Time period: Filter results by date to track how narratives evolve
• Entity type: Search for mentions of specific companies, products, regulations, or events

The Smart Objects system automatically categorizes extracted information into over 20 entity types, including people, organizations, financial figures, scientific claims, dates, locations, and methodologies. This means journalists do not need to know the exact wording of a claim to find it. Semantic search surfaces relevant results even when different speakers use different terminology to describe the same concept.

For newsroom teams, VERIdex supports shared workspaces where multiple journalists can collaborate on research, share tagged sources, and build collective knowledge bases around ongoing investigations.

One of VERIDIVE's most powerful capabilities for journalists is contradiction detection. The platform automatically flags instances where a speaker's current claims conflict with their previous statements, or where different experts make opposing claims about the same topic.

Here is how contradiction tracking works in practice:

• Speaker timeline: VERIDIVE builds a chronological record of every indexed statement by a specific person, making it easy to spot when someone reverses a position
• Cross-source verification: Claims made on one podcast are automatically compared against related claims from other sources, with contradictions flagged and confidence scores assigned
• Claim evolution mapping: The DeepLink knowledge graph visualizes how specific claims or narratives change over time, showing which experts shifted positions and when

For investigative journalists, this capability is transformative. Instead of manually comparing interview transcripts side by side, the platform surfaces contradictions automatically. A journalist covering corporate accountability can instantly see whether a CEO's public safety assurances align with internal communications revealed in other interviews or panel discussions.

The DeepLink knowledge graph is particularly valuable for long-form investigative journalism. DeepLink maps relationships between people, organizations, events, claims, and concepts, creating a visual network that reveals connections human researchers might miss even after extensive manual review.

A journalist investigating lobbying influence, for example, could use DeepLink to map every instance where a specific lobbyist appeared on podcasts, what talking points they used, which organizations they referenced, and how their messaging aligned with (or diverged from) official policy positions. This kind of network analysis across spoken content was previously impossible without months of dedicated manual work and extensive transcription resources.

DeepLink also supports temporal analysis, allowing journalists to see how relationship networks and narratives evolve over time. This is critical for stories involving coordinated messaging campaigns, where multiple speakers begin using identical talking points within a narrow timeframe, suggesting centralized messaging coordination. Temporal filters let journalists isolate specific date ranges to identify when narrative shifts occurred.

Breaking stories require real-time intelligence. VERIDIVE's DeepWatch module allows journalists to deploy autonomous monitoring agents that track specific YouTube channels, podcast feeds, and video sources for new content matching predefined criteria.

A journalist covering the tech industry might configure DeepWatch agents to:

• Monitor all podcast appearances by specific executives and alert when new interviews are published
• Track mentions of specific companies, products, or regulatory actions across hundreds of sources
• Flag any content where speakers reference ongoing investigations or legal proceedings
• Detect when previously private information surfaces in public spoken content

When a DeepWatch agent detects relevant content, it automatically processes the source, extracts key claims and entities, and sends an alert with a structured summary. This means journalists can respond to breaking developments within minutes, rather than discovering relevant spoken content days or weeks after publication.

For newsrooms covering fast-moving stories, DeepWatch effectively provides a dedicated research assistant that monitors the spoken content landscape 24/7 without fatigue or oversight gaps.