Equity research is about extracting clarity from complexity. Analysts work across massive volumes of unstructured information, such as regulatory filings, transcripts, investor presentations, internal models, and call notes. The challenge isn’t just finding what matters, but turning it into structured, defensible insight that stands up to internal and client scrutiny.
Hebbia is built for this. Matrix lets analysts upload (or pull from integrated sources like S&P, CapIQ, PitchBook, SEC filings, and more) aggregating thousands of documents, models, transcripts, CRM exports, and memos into a single workspace. Analysts run spreadsheet-style queries and get structured, citation-linked outputs ready to plug into models, drafts, and decks.
This is powered by Hebbia’s information retrieval engine and Iterative Source Decomposition (ISD), which preserve context, formatting, and structure in ways RAG systems cannot. Top research teams are using Hebbia to scale output and generate high-quality insight faster.
Here are the top five ways leading equity research teams like XYZ are using Hebbia today:
Ramp on companies and industries faster by analyzing multiple filings, transcripts, and decks. Extract growth drivers, risk factors, and segment disclosures all at once.
Context: When analysts take on new names whether it’s an IPO, adjacent sector, or different geography, they need to quickly understand company structure, segment reporting, risk factors, and strategy across years of documents.
Problem: With coverage lists expanding and research timelines shortening, the ability to analyze thoroughly and quickly is increasingly constrained.
How Hebbia Helps: Matrix empowers analysts to ingest the full document set for each company, across filings, transcripts, decks, and notes, and issue targeted queries such as:
This accelerates time to coverage while producing stronger, evidence-backed narratives from day one. Results are returned in a structured table with rows grouped by company, each citation linked to its original source, allowing them to cover more names without sacrificing depth.
This is possible only with Hebbia’s information retrieval engine and Iterative Source Decomposition (ISD) architecture, technology no other platform offers. ISD preserves layout, speaker attribution, and context across every document, enabling precise, source-linked querying at scale. Combined with Hebbia’s infinite context window, analysts can extract institutional-grade insights from hundreds of filings, transcripts, and notes, without sampling, summarization, or loss of fidelity.
Trace every KPI or assumption to its original source. Strengthen model accuracy with historical context and peer benchmarks.
Context: Financial models drive valuation, scenario analysis, and the investment case, forming the foundation of most research outputs. But as models evolve, the context behind key inputs, whether historical KPIs or forward assumptions, is often missing, unclear, or forgotten.
Problem: Without proper documentation, especially of non-recurring versus recurring items or changing KPIs over time, models become opaque and difficult to defend during internal reviews or client discussions. Moreover, forecasts become less robust.
How Hebbia Helps: Hebbia helps analysts strengthen their financial models by connecting key metrics to their underlying sources. By uploading filings, transcripts, investor materials, and research notes, analysts can use Matrix to run targeted enrichment queries such as:
Matrix returns fully sourced, citation-linked excerpts that clarify where a metric originated and how management framed it, allowing analysts to incorporate this information into model comment boxes. This results in models that are easier to audit, explain, and maintain and it improves the quality of forecasts.
Matrix is uniquely suited to this task because it treats every source document, regardless of format, as a fully indexed, interactive object. Hebbia’s ISD architecture enables analysts to surface detailed historical context, comparable metrics, and source-level clarity across multi-decade timeframes, without losing granularity.
Analyze tone and language across years of calls. Surface sentiment shifts and strategy changes by speaker, quarter, and company.
Context: Earnings calls can reveal signals, like shifts in tone, priorities, or operational momentum. Changes in language can provide early insight into strategy, sentiment, or risk.
Problem: While analysts review transcripts closely, spotting consistent themes across time and companies is hard to scale. Identifying tone shifts or evolving narratives is time-consuming, and tracing past management responses to macro shifts or other changing variables is difficult to scale across multi-year periods or large coverage lists.
How Hebbia Helps: With Matrix, analysts can upload earnings transcripts across their coverage universe and query for recurring themes, tone shifts, and strategic signals. Common use cases include:
Matrix returns structured excerpts and analysis by company, quarter, and speaker, with linked citations. Analysts generate clean, time-ordered outputs that show not just what was said, but how it was said, supporting deeper previews, reviews, and thematic analysis.
This capability is powered by Hebbia’s infinite context window and ISD architecture, which preserve speaker attribution, chronology, and formatting across transcripts in ways traditional RAG systems cannot.
Turn past disclosures into targeted meeting questions. Spot gaps, follow-ups, and changes in guidance with source-backed prompts.
Context: Before meeting with management teams, attending industry conferences, or hosting reverse inquiries, analysts are expected to show up with thoughtful, company-specific questions. The best questions draw on deep familiarity with prior disclosures and identify gaps, shifts, or inconsistencies that require clarification.
Problem: Surfacing the right questions means referencing filings, transcripts, and materials to spot changes or gaps, which is work that becomes hard to scale across a broad coverage list.
How Hebbia Helps: By centralizing and indexing all relevant documents, Matrix allows analysts to identify high-value question prompts with minimal effort. Sample queries include:
The results are structured, fully sourced, and directly usable as question lists. Analysts can generate targeted discussion points such as: “In Q1’23 you emphasized international expansion as a priority, but it has not been mentioned in the last two calls. Can you provide an update?” The Matrix returns granular, actionable language that reflects the exact words and tone used by management along with deep analysis on the company, helping analysts deliver more informed and differentiated conversations.
Hebbia’s ability to link all disclosures, including filings and transcripts in platform, and internal notes, into one context-aware workspace makes it ideal for high-quality prep.
Upload notes, transcripts, and internal docs into one workspace. Create a persistent, searchable memory across the entire research team.
Context: Research teams accumulate valuable insights over time with meeting notes, sector takeaways, call recaps, and internal memos, but these are often scattered across inboxes, CRMs, and personal folders.
Problem: Without a centralized, searchable system, firms lose institutional memory. Analysts miss context, repeat work, and lose knowledge when team members leave.
How Hebbia Helps: Matrix allows research teams to upload all internal documents, including meeting notes, call recaps, emails, internal memos, and CRM exports, into a single, private research workspace. This workspace is ever growing (with our integrations with Microsoft and Box allowing for seamless uploading and indexing) and changing, allowing for not only a space to see all documents, but constantly analyze upon them in the background. Analysts can then query across this using targeted prompts such as:
This transforms fragmented notes into a living, queryable knowledge bank that compounds in value over time. The entire team now benefits from centralized institutional memory that is structured, source-linked, and instantly accessible.
Matrix is not just a document search engine. It is a purpose-built research platform designed to meet the demands of analysts, where precision, speed, and source integrity are non-negotiable.
By transforming unstructured content into structured intelligence, Hebbia empowers teams to move faster, go deeper, and deliver higher-quality insight under tighter timelines. It integrates seamlessly into existing research workflows and enhances output without compromising rigor.
Today, top equity research teams across the industry rely on Hebbia to stay ahead of expanding coverage demands, rising client expectations, and increasingly complex information environments. It is becoming the core infrastructure for how modern research gets done.
