Investment banking teams manage thousands of unstructured documents and data points across every deal cycle. Creating materials like Confidential Information Memorandoms (CIMs), strip profiles, Public Information Books (PIBs) and buyer lists often means reviewing call notes, pulling market data, and rewriting the same language repeatedly across dozens of decks. The work is high stakes, time consuming, and often constrained by manual processes or offshore workflows.
Hebbia is built for this environment. Matrix lets bankers ingest Virtual Data Room (VDR) contents, pitch decks, transcripts, research notes, and third-party data sources into a single workspace. Teams run spreadsheet-style queries across the full body of deal content and receive structured, citation-linked outputs that can go directly into CIMs, pitch decks, or PIBs.
Behind this is Hebbia’s Information Retrieval Engine and Iterative Source Decomposition (ISD), 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 miss important nuance and trace their sources, ISD maintains full document integrity and precise source-linking for more accurate, defensible insights. This is the only system on the market capable of querying with full fidelity across thousands of deal files, and it's already in use by leading investment banking teams.
Here are six ways bankers are using Hebbia to drive speed, accuracy, and quality in every stage of execution:
Turn disparate VDR materials and call notes into a polished CIM draft in hours, not weeks
Context: CIMs are the first thing buyers see. They set the tone for a deal, establish credibility, and must present a clear and compelling view of the company. But the content that fuels them is scattered across hundreds of documents, decks, and notes from 1:1 management sessions.
Problem: Bankers spend weeks combing through 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:
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 ISD architecture, which preserves structure across PDFs, notes, and Excel files, first drafts are far stronger and require less rewriting.
Plus, with Hebbia’s recent 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.
Build accurate, clean strip profiles across an entire comp set without overnight handoffs
Context: Strip profiles show up in every pitch and every PIB. They contain firm details, product positioning, and historical deal activity across all relevant peers, and they need to be right.
Problem: The process 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 rewriting for polish takes up hours of banker time.
How Hebbia Helps: Bankers can upload or query over integrated sources like PitchBook, CapIQ, company websites, and past profiles to pull:
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.
This is only possible with Hebbia’s ISD engine, which treats every document and data source as a structured, queryable object. Unlike RAG-based tools, Hebbia maintains data integrity and formatting, making it usable immediately in client-facing materials.
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 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.
Identify, vet, and summarize strategic and financial buyers with speed and depth
Context: Whether preparing a PIB, outbound materials, or buyer recommendations, bankers need tailored, current, and defensible profiles of both strategic and financial sponsors.
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:
If bankers want to have a more thorough analysis on more complicated questions, they can use our ‘deeper’ or ‘deepest’ research functions in Deep Research, each pulling in more sources (up to 350 sources) to provide more extensive analysis on potential buyers. Results are delivered as structured, source-linked insights that bankers can easily include in PIBs or buyer screens. 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.
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.
Build diligence Q&A lists faster and respond with precision using structured source-backed outputs.
Context: During live processes, bankers are responsible for generating and responding to detailed diligence questions across all functional areas like sales, product, finance, operations, legal, and more. Questions from potential buyers must be anticipated, tracked, and answered with speed and accuracy, often by synthesizing information from management calls, internal notes, and dataroom content.
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:
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.
This precision is possible only because of Hebbia’s Iterative Source Decomposition (ISD) technology, which preserves structure and context across fragmented documents. Unlike traditional retrieval-augmented generation (RAG) systems, Hebbia can trace line-level detail and segment multiple documents accurately which is a critical advantage in live diligence workflows.
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.
