Leveraged finance (“LevFin”) is about speed to insight and execution through complex documents. These documents take the form of credit agreements, niche legal documents, or massive volumes of unstructured private company information. The real challenge isn’t just finding information, it’s structuring it in a reliable, defensible way so LLMs can reason over it and bankers can deliver expert insight that drives better client decisions.
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.
Behind this is Hebbia’s information retrieval engine and Iterative Source Decomposition (ISD) architecture, which preserves context, structure, and formatting across documents in ways retrieval-augmented generation (RAG) cannot. Unlike RAG, which relies on retrieving and summarizing chunks of text that can lose nuance and source fidelity, ISD maintains full document integrity and precise source-linking for more accurate, defensible insights. Top LevFin teams are using Hebbia to scale output and generate high-quality insight faster.
Here are a few of the ways leading LevFin teams are using Hebbia today:
Quickly analyze key clauses and metrics from a series of credit agreements.
Context: Leveraged finance associates regularly review credit agreements to extract key terms, assess risks, and support deal execution. They benchmark provisions across similar deals, prepare internal summaries, and ensure terms align with market standards. This process is critical but often slow and manual due to the complexity and length of the documents.
Problem: Extracting key terms and data points from credit agreements is a time-consuming and manual process for bankers and legal teams. These documents are highly complex, filled with dense legal language, and vary widely across deals and counterparties. Comparing terms across multiple agreements—especially to determine whether specific provisions align with "market" norms in a particular industry or region—is even more difficult. This slows down deal execution, increases the risk of oversight, and hinders strategic decision-making.
How Hebbia Helps: Hebbia transforms the review process for credit agreements by using advanced AI to automatically extract key terms, clauses, and obligations—such as covenants, interest rate structures, maturity terms, and collateral requirements. This allows bankers and legal professionals to rapidly identify the most relevant components without manually combing through lengthy documents.
This capability is powered by Hebbia’s infinite context window and ISD architecture, which preserve clauses, chronology, and formatting across documents in ways traditional RAG systems cannot.
Beyond extraction, Hebbia enables users to benchmark terms against a curated library of precedent agreements. These can be filtered by industry, geography, revenue scale, and other deal attributes, making it easy to assess how a given agreement compares to market standards. Users can also leverage insights from past deals to help draft new agreements more efficiently and with greater confidence. This not only reduces manual effort but also improves negotiation leverage and decision-making speed.
Strengthen pitch materials with better market maps, company positioning, and sector insights.
Context: Pitching is one of the most repetitive yet high-stakes workflows in banking. Bankers are responsible for assembling decks that combine industry dynamics, buyer and comp profiles, market maps, and strategic rationale tailored to each client. In many cases, they also prepare Private Information Books (PIBs), which are longer-form, internal documents used to brief deal teams or potential buyers. PIBs often include detailed financials, prior management commentary, buyer universe mapping, past outreach history, and deal rationale.
Problem: Compiling this content is labor-intensive. Bankers have to manually pull data from past decks, press releases, websites, and analyst notes, while ensuring accuracy and strategic coherence. Repetitive formatting, outdated language, and inconsistent positioning can kill credibility fast, especially if the narrative doesn’t align with how the client sees themselves.
How Hebbia Helps: Matrix supports bankers throughout the entire pitch workflow, from ideation to delivery. Bankers use it to:
Matrix cuts hours from deck creation and ensures PIBs are built on verified, source-linked inputs. Hebbia’s information retrieval engine and Iterative Source Decomposition (ISD) preserve layout, context, and chronology across documents. This enables multi-source queries combining investor presentations, internal notes, and CRM exports in one step, capabilities no RAG-based tool can match.
Strengthen assumptions and justify inputs with source-linked data from filings, call notes, and investor materials.
Context: Bankers build and update models to support valuation work, marketing narratives, and buyer discussions. These models rely on input assumptions sourced from company materials, CRM notes, proprietary diligence, and market comps, often across dozens of private and public companies. But as assumptions change and models are handed off between teams, the original source material behind key metrics often becomes unclear.
Problem: Tracing the origin of a margin assumption, customer count, or unit economics input can take hours. Source material is often buried in PDFs, CIM drafts, call summaries, or old slides. Without clear sourcing, models become hard to defend in buyer discussions or internal reviews, especially when assumptions are challenged or need to be refreshed in a live deal.
How Hebbia Helps: Bankers use Matrix to pull in company materials, including VDR content, internal notes, call recaps, press releases, CIMs, and benchmarking data, and issue targeted queries like:
Matrix returns clean, citation-linked excerpts that show the original language, the number, and its surrounding context, all tied to the exact slide, memo, or paragraph it came from. This makes assumptions easier to validate, update, and explain.
Hebbia is uniquely equipped for this because of ISD, which treats unstructured materials like PDFs, decks, and internal notes as structured, queryable sources. Unlike traditional RAG-based tools, Hebbia doesn’t summarize or sample. It returns line-level answers with full context, even when the material spans dozens of private companies or non-standard formats.
