Hebbia, the AI platform that enables faster workflows and deeper diligence, is helping Leveraged Finance (LevFin) teams scale output and generate high-quality insight, faster. 

LevFin is all about navigating complex documents as accurately as possible. One of the most pressing challenges is how to structure information from hundreds of documents in a way that’s (1) digestible by Large Language Models (LLMs) and (2) empowers bankers to make better client decisions.

Fortunately, Hebbia is purpose-built for the intricacies of finance. It enables analysis at scale across the most comprehensive set of financial data sources—including proprietary documents—and transforms analysis into polished presentations and financial models.

Here are the ways LevFin teams are taking advantage of Hebbia’s proprietary AI technology for:

  • Analyzing credit agreements more efficiently
  • Improving pitch deck and Private Information Book (PIB) development
  • Updating model inputs and deal assumptions

1. Analyzing Credit Agreements More Efficiently

Credit agreement analysis is essential for deal execution. The process is critical but often slow and manual.

Problem: The manual process of analyzing credit agreements has potential to slow down deal execution, hinder strategic decision-making, or increase the risk of a catastrophic oversight.

Credit agreements are dense, highly-complicated documents that vary widely across deals and counterparties. Extracting key terms and data points is a time-consuming process for LevFin and legal teams. Comparing terms across multiple agreements—especially to determine whether specific provisions align with "market" norms—is even more difficult.

How Hebbia Helps: Hebbia transforms the review process for credit agreements—without compromising accuracy— in two ways:

1.  Uses advanced AI to automatically extract key terms, clauses, and obligations. This allows LevFin teams and legal professionals to rapidly identify the most relevant components (covenants, interest rate structures, maturity terms, collateral requirements, etc.) without manually combing through lengthy documents. This capability is powered by Hebbia’s infinite context window and Iterative Source Decomposition (ISD) architecture, which preserve clauses, chronology, and formatting across documents in ways traditional RAG systems cannot.

2. 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.

2. Improving Pitch Deck and Private Information Book (PIB) Development

Pitch deck and PIB development is one of the most repetitive yet high-stakes workflows in banking.

Problem: It is very labor-intensive to compile all the content needed for pitch decks and PIBs: buyer and comp profiles, market maps, strategic rationale, detailed financials, prior management commentary, buyer universe mapping, to name a few. 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 cuts hours from deck creation and ensures PIBs are built on verified, source-linked inputs. Hebbia’s information retrieval engine and 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.Matrix supports bankers throughout the entire pitch workflow, from ideation to delivery. LevTeams use it to:

  • Pull KPIs, submarket sizing, and competitive intelligence from leading consulting and advisory reports or filings
  • Generate up-to-date strip profiles with product details, founder information, and ownership
  • Construct market maps and surface sector themes across decks and transcripts
  • Customize strategic rationale pages based on how a company positions itself publicly
  • Maintain a live, searchable knowledge base of prior pitch content for reuse and refinement
  • Seamlessly generate formatted pitch decks and PIBs within the platform, reducing manual slide creation

3. Updating Model Inputs and Deal Assumptions

Strengthen assumptions and justify inputs with source-linked data from filings, call notes, and investor materials.

Problem:  Tracing the origin of a margin assumption, customer count, or unit economics input can take hours. Source material is often buried in PDFs, Confidential Investment Memo (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:LevFin teams use Hebbia to pull in company data, including publicly disclosed financials, VDR content, internal notes, call recaps, press releases, CIMs, and benchmarking data, and issue targeted queries like:

  • What were the drivers of gross margin or CAC efficiency discussed in diligence notes or management calls?
  • What are the latest disclosed customer counts or growth KPIs across the peer set?
  • Where have assumptions changed across model versions, and what was the source?

The Hebbia Difference for LevFin

Hebbia helps you navigate the complexities of LevFin workflows. With the industry’s most comprehensive financial services data integrations, Hebbia enables you to conduct all aspects of diligence within a single platform. Leveraging Matrix, users can obtain highly accurate, line-level answers across hundreds of documents—complete with full context—and seamlessly convert those insights into polished presentations and financial models.

That’s why leading investment banks and over 40% of the largest asset managers by AUM use Hebbia to power their most important work.

Hebbia is uniquely built for financial diligence. In addition to Chat and Deep Research, Matrix—Hebbia’s flagship product—lets you aggregate proprietary documents, models, transcripts, CRM exports, memos, and market data into a single workspace. 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.