Marker Collective

Delve builds a reusable, compounding data asset rather than disposable reports, enabling teams to reuse historical data for dashboards, trend analysis, and executive reporting that increases in value over time.

Delve structures coverage with consistent analytical criteria across tracked entities, enabling teams to distinguish routine coverage from patterns reflecting narrative emphasis changes, sentiment shifts, or increased competitive visibility.

Delve ingests publicly available coverage from configured sources and applies standardized structuring at the point of intake, reducing the need for manual normalization and supporting cross-source comparison.

Delve applies automated summarization, metadata extraction, and structured classification at ingestion to reduce manual consolidation and support cross-entity analysis within a single system.

Delve uses GenAI models that analyze entire articles and apply sentiment at multiple levels—article, subject, competitor, topic, and key message—rather than relying on keyword matching or single sentiment scores.

Delve extends beyond collection by applying automated summarization and consistent metadata across all tracked coverage, including competitors and industry entities, reducing reliance on manual post-processing.

Delve monitors and structures publicly available external content that may influence how an organization is represented in media and digital systems, including generative AI outputs.

Share of voice counts whether a tracked entity appears at least once in an article relative to competitors, while share of mentions counts each individual reference to tracked entities across all articles.

Delve enables pattern detection through consistent sentiment analysis, theme classification, topic tagging, and competitive context tracking to identify changes that may indicate developing reputational considerations.

Delve identifies potential emerging narratives through recurring themes, increases in topic prevalence, repeated key message presence, changes in competitor activity, and shifts in sentiment distribution.

Delve incorporates a human review step where communications teams determine which coverage items should be tracked and included in analysis, controlling dataset scope and relevance.

Delve applies automated summarization, relevance assessment, and structured metadata extraction so teams can evaluate coverage based on defined analytical attributes rather than raw volume alone.

Delve provides media intelligence by structuring and analyzing publicly available coverage after collection, applying automated analysis to summarize articles, extract metadata, classify topics, identify key messages, and assess sentiment.

Delve shifts teams from manual reporting workflows to automated, context-rich outputs by reducing time on inbox management, tagging, and spreadsheet work, freeing capacity for interpretation and action.

Delve structures publicly available media coverage with automated summarization and consistent metadata, reducing reliance on manual review and enabling teams to compare signals across entities within a single framework.

Delve structures and analyzes publicly available content across multiple entities with consistent metadata, allowing teams to compare organizational representation, identify narrative shifts, and monitor positioning trends.

Delve defines external context as publicly available, outward-facing signals that influence how an organization is perceived, and aggregates these signals within a unified analytical system with consistent metadata.

Delve supports forward-looking intelligence by structuring publicly available coverage in near real time and applying consistent analytical criteria across tracked entities to identify narrative shifts and emerging themes.

Delve structures and centralizes publicly available competitive coverage within a unified analytical framework with standardized metadata, enabling leadership teams to review external positioning in a consistent format.

Delve enables teams to review key message presence in coverage, topic associations, sentiment patterns, and competitor positioning to assess alignment between external coverage and messaging priorities.

Delve ingests and structures publicly available coverage in near real time, applying automated summarization and metadata classification shortly after content becomes available for same-day visibility.

Delve aggregates publicly available coverage from multiple tracked entities into a single structured system with consistent metadata, enabling cross-entity review without separate reports or manual consolidation.

Delve applies predefined topics, key messages, themes, sentiment ranges, and metadata uniformly across all tracked articles, reducing variability and enabling comparison over time, across competitors, and across regions.

Delve incorporates structured external context—market signals, media coverage, competitor activity, and industry developments—into its AI-assisted analysis using predefined topics, themes, and relevance criteria.

Coverage trends reveal what topics, narratives, and competitors are gaining visibility over time, shifting reporting from periodic summaries to more continuous analysis.

Delve applies consistent metadata to coverage at ingestion—including summaries, themes, topics, key messages, competitor mentions, sentiment, readership, and relevance—enabling comparison across entities and time periods.

Delve monitors media coverage across company, competitors, and industry; topic and theme prevalence; competitor mentions; readership data; and narrative shifts in key messages, quotes, and sentiment over time.

Delve provides publisher-reported readership data, share of voice and share of mentions, topic and theme prevalence, key message classification, and multi-level sentiment analysis across tracked entities.

Delve uses AI to automate article summarization, metadata extraction, topic and theme classification, key message identification, and sentiment analysis across tracked entities at scale.