Private credit teams evaluate thousands of opportunities with incomplete information and tight timelines. 

Each new deal involves hundreds of pages of diligence materials, credit agreements, and financial statements, plus precedent knowledge hidden in past Investment Committee (IC) memos. The challenge isn’t in finding documents, but extracting the right terms, benchmarks, and comps fast, while maintaining defensibility in front of the investment committee, sponsor, and borrower.

Fortunately, Hebbia is purpose-built for the intricacies of finance. It helps private credit firms automate the heavy lift of screening, benchmarking, and precedent analysis so teams can focus on judgment, analysis, and negotiation.

Here are four ways private credit teams are using Hebbia:

  • Building IC Memo Library
  • More Efficient New deal screening
  • Surfacing Hidden Opportunities
  • Credit agreement analysis

1. Building IC Memo Library

Past IC memos contain valuable analyses on credit quality of previously analyzed companies

Problem: IC memos are scattered across folders, rarely standardized, and difficult to filter by criteria such as deal type, size, sponsor, or industry. Analysts rely on institutional memory, which creates inconsistency, slows benchmarking, and often prevents it altogether at the junior level.

How Hebbia Helps: Hebbia centralizes past IC memos into a queryable library. Using Matrix, credit teams can instantly filter by key attributes and surface the most relevant precedents. Analysts can issue queries like:

  • “Show me pricing from previous deals >$50m EBITDA with leverage above 6x”
  • “Summarize key risks flagged in sponsor-backed healthcare deals in the last 24 months”

Matrix outputs structured, filterable, source-linked results in clean tables or sentences, so analysts can compare precedent terms directly in memos or use them in live negotiations.

2. More Efficient New Deal Screening & Initial Short-Form Memo

Generate a defensible first-pass memo from CIMs or decks in minutes instead of hours.

Problem: Drafting a first-pass memo using initial diligence materials can take up to 1–2 days. Analysts extract business fundamentals and key metrics from lengthy, often fluffy, decks, and seek answers to basic diligence items.

How Hebbia Helps: Hebbia generates a draft memo focusing only on the elements the analyst prescribes – cutting through the chaff and laying out a solid initial thesis on highlights and risks. Analysts can ask:

  • “Draft a section on how this company makes money – explain it simply in layman’s terms”
  • “How is customer concentration, and how sticky are their key customers really?”

Matrix produces a structured summary—company overview, main segments, recent performance, competitive positioning—each tied to the original source, that can be married with a cross-check vs precedent, comps, or the web. What once took days is now done in hours, with memos built on verifiable, defensible inputs. 

3. Surfacing Hidden Opportunities

Surface hidden opportunities by scanning filings, transcripts, and news for refinancing signals.

Problem: Finding opportunities entails surfacing lenders with rumored strategic alternatives, refinancing needs, liquidity issues, acquisitions, or other sponsor dynamics at play. The return-on-time invested into scouring for these deals is often low. Reading through hundreds of files to find one or two opportunities is hardly worth it. Thus, opportunities are often missed, and origination efforts depend heavily on memory and personal networks or relationships – which becomes increasingly challenging in a growing space. 

How Hebbia Helps: With Browse and Matrix, teams query across filings, transcripts, and thousands of emails to identify upcoming capital needs, and focus efforts down to those their firm has the most appetite for. Example queries include:

  • “List all borrowers mentioning 2026 debt maturities in earnings transcripts or filings”
  • “Which sponsor portfolios have companies that discussed covenant relief in the past 2 quarters?”

Matrix then surfaces the most relevant opportunities, with propensity to align to your firm’s strong suits, and points you where to devote your time. Origination teams build robust, living pipelines to grow their firm’s top of funnel.

4. Credit Agreement Analysis

Extract and benchmark covenant terms, baskets, and carve-outs across agreements in minutes.

Problem: Knowing where protections are strong or weak is critical to negotiations, and ongoing portfolio health.Teams rely heavily on external counsel for reviews. Portfolio-wide benchmarking is slow and requires line-by-line reading of each agreement. Amendments and LME risks in a pinch force repeated re-reviews.

How Hebbia Helps: Matrix enables cross-document covenant comparisons at scale. Analysts can query:

  • “Which Stonerock-backed deals had high Serta risk and why?”
  • “Compare standard EBITDA add-back caps for the last 10 deals we’ve reviewed and compare to this one”

Results are delivered in structured, source-cited tables that highlight where to focus in the docs. This allows private credit teams to reduce reliance on counsel for first-pass reviews and approach negotiations armed with precedent.

Why Private Credit Teams Choose Hebbia

Private credit demands speed, accuracy, and defensibility. Hebbia delivers all the above by combining:

  • Matrix for structured, source-cited benchmarking and relative analysis.
  • Browse for precise source finding and organization across vast data sets.
  • Iterative Source Decomposition (ISD) to preserve full context and structure, ensuring outputs are defensible in ICs and negotiations.

Hebbia doesn’t replace judgment but it automates the extraction and structuring of deal content, so private credit teams can focus on the investment call itself.

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.