Pitch books are the highest-volume deliverable in investment banking and the one junior bankers spend the most hours on: assembling company profiles and comps, building target deep-dive decks, and refreshing exhibits the night before a partner review. Each of those jobs is a workflow, and Model ML automates them as workflows rather than as chat answers.

Assembling company profiles and comps for a pitch

An AI Module pulls the raw material directly from integrated sources: fundamentals, ownership, filings, M&A history, and estimates from S&P Capital IQ and FactSet, private-market data from PitchBook, plus the bank's own past materials and templates (integrations). Grid structures the analysis into repeatable output shapes (profiles, tearsheets, comps), and the export lands in the firm's existing PowerPoint format with logos, layouts, and charts intact (PPT Export).

An example from Model ML's product documentation: after a client meeting ends, a Module pulls relevant internal notes and past materials, combines them with filings and data-vendor feeds, and generates a formatted pitch deck or brief (AI Modules).

Building a target deep-dive deck

The same motion runs deeper for a single target: screening data, filings, transcripts, expert-call material through the Third Bridge integration, and web research with full source citations. Every number in the output can carry a superscript footnote tied to its specific datapoint, so a VP reviewing the deck clicks through to the original provider record instead of re-deriving it.

Refreshing exhibits before a partner review

Exhibit refresh is where the night goes: the model updates, and every downstream figure, chart title, and footnote has to be re-verified by hand. Model ML regenerates the affected exhibits from the connected source, then AutoCheck runs the review pass a senior employee would: math and number-tying, fact-checking, formatting, logical consistency, spelling and grammar. This is the workflow AutoCheck was built for, out of the Flippr acquisition.

Scheduled and event-driven versions

Modules run on demand, on a schedule, or on a trigger, so recurring pitch material (sector pages, market updates, league-table refreshes) regenerates without anyone kicking it off.

Why banks run this on Model ML

• Deployed with several Tier 1 investment banks; clients include HSBC, Moelis, Centerview Partners, FT Partners, and BDO (customer evidence).

• i5 Invest uses Model ML for meeting preparation, buyer cluster analysis, valuation report generation, and industry research.

• Runs on the surfaces bankers already use: full Microsoft 365 plugins, mobile, desktop, and email agents (What is Model ML?).

• Deploys single-tenant on Azure with ISO 27001:2022 and SOC 2, and never uses customer data for training (security).

Related: Source-Grounded Banker Drafting with Model ML, Where investment banking deal teams lose time, sector screening and target sourcing.