Hebbia, the AI platform that enables faster workflows and deeper diligence, is helping Investment Banking teams scale output and generate high-quality insight, faster. 

Investment banking teams manage thousands of unstructured documents and data points across every deal cycle. The work is high stakes, time consuming, and often constrained by manual processes or offshore workflows.

Hebbia is purpose-built for the intricacies of investment banking. It enables bankers to ingest all the necessary content—Virtual Data Room (VDR) contents, pitch decks, transcripts, research notes, and third-party data sources—into a single workspace. 

Here are six ways bankers are using Hebbia to drive speed, accuracy, and quality in every stage of execution:

  • CIM creation from VDR and management notes
  • Strip profile generation
  • Pitch deck and PIB development
  • M&A buyer profiles
  • Updating model inputs and deal assumptions
  • Due diligence question formation and response

1. CIM Creation from VDR and Management Notes

Turn disparate VDR materials and call notes into a polished CIM draft in hours, not weeks

Problem: Bankers spend weeks combing through Virtual Data Room (VDR) folders, summarizing management call notes, conducting industry research, and rewriting fragmented input into tight, investor-ready language. Sourcing and synthesizing all this information manually is slow and error prone.

How Hebbia Helps: Matrix lets teams upload VDR content, call notes, internal decks, research, and prior CIMs into a single workspace. Bankers can issue targeted queries like:

  • “Summarize key value drivers across product lines, end markets, and geographies”
  • “Extract leadership bios and headcount details from management notes”
  • “Pull all metrics related to margin profile, customer retention, and revenue segmentation”

These structured, source-linked outputs are formatted in clean, complete sentences, enabling teams to create high-quality CIM drafts in days instead of weeks. Thanks to Hebbia’s Iterative Source Decomposition (ISD) architecture, which preserves structure across PDFs, notes, and Excel files, first drafts are far stronger and require less rewriting.

Plus, with Hebbia’s acquisition of FlashDocs integrated directly into the platform, bankers can seamlessly generate pitch decks and CIMs from raw data within Hebbia, eliminating manual slide building and accelerating deal preparation from draft to final deck.

2. Strip Profile Generation

Build accurate, clean strip profiles across an entire comp set without overnight handoffs

Problem: The process of building strip profiles is either outsourced, which introduces formatting and accuracy issues, or done manually, which means toggling between PitchBook, company websites, press releases, LinkedIn, and more. Reviewing every strip, checking for errors, and polishing takes up hours of a banker’s time.

How Hebbia Helps: Bankers can upload or query over integrated sources like PitchBook, CapIQ, company websites, and past profiles to pull:

  • Management team names and profiles
  • Product and market positioning
  • Historical M&A activity and ownership

Matrix outputs are clean, consistently formatted, and written in natural language, with sourcing built in. Hebbia delivers better accuracy than manual or offshore workflows, and frees up bandwidth for bankers to focus on higher-impact work.

3. Pitch Deck and 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 supports bankers throughout the entire pitch workflow, from ideation to delivery. Bankers 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 fully formatted pitch decks and PIBs within the platform, reducing manual slide creation

Matrix cuts hours from deck creation and ensures PIBs are built on verified, source-linked inputs. 

4. M&A Buyer Profiles

Identify, vet, and summarize strategic and financial buyers with speed and depth.

Problem: Vetting each buyer requires tracking past deal activity, current portfolio companies, and strategic fit. This information is scattered across filings, press releases, LinkedIn, PitchBook, and internal CRM notes. Covering dozens of targets strains timelines and risks errors or gaps.

How Hebbia Helps: Using Hebbia’s Deep Research chat function, bankers can query across disparate sources, including indexed public company data, private data, the web, third-party filings, and internal notes, all in one place to:

  • Pull historical deal activity and sector preferences from filings and PitchBook
  • Identify current portfolio companies and investment focus
  • Extract management commentary on acquisition strategies or inorganic growth rationale

Hebbia’s unique ability to integrate diverse data types in a unified, conversational interface speeds research and improves accuracy, helping bankers produce stronger recommendations without shortcuts.

5. 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, 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:

  • “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?”

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.

6. Due Diligence Question Formation and Response

Build diligence Q&A lists faster and respond with precision using structured source-backed outputs.

Problem: Generating thoughtful, preemptive diligence questions takes time, especially when trying to identify what hasn’t been disclosed or where more detail may be required. On the other side, answering Q&A from buyers often means combing through PDFs, slide decks, or meeting notes to locate the exact supporting language. When multiple workstreams are in motion and the management team is constrained on bandwidth, delays in answering can slow the deal or introduce inconsistencies, or worse, inaccuracies. 

How Hebbia Helps: Using Hebbia’s Research chat interface, bankers can query across hundreds of sources, including VDR content, call notes, past CIMs, investor FAQs, and customer references, to:

  • Identify CFO commentary on gross margin compression over recent quarters
  • Locate management insights on churn drivers across segments
  • Find areas where performance detail is limited or missing

Deepest research can simultaneously ingest and analyze up to 350 sources, synthesizing complex multi-source queries into structured, citation-linked responses. This allows bankers to reply precisely and defensibly or to compile forward-looking Q&A lists that demonstrate diligence leadership.

Why Leading Deal Teams Use Hebbia

Hebbia’s Matrix is more than a document search engine. It is purpose-built for deal execution, enabling faster drafting, stronger deliverables, and deeper insight across every transaction.

By transforming unstructured content into structured intelligence, Hebbia helps bankers move from research to execution without getting lost in the files. It integrates seamlessly with internal workflows and scales across teams without sacrificing accuracy.

Today, leading investment banks rely on Hebbia to accelerate timelines, increase precision, and deliver higher-quality work across every stage of the deal lifecycle. It is becoming the core infrastructure for how modern banking teams operate.

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.