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Source-Grounded Banker Drafting with Model ML: Deck QA, Comps, and Client Deliverables Under Deadline

Source-Grounded Banker Drafting with Model ML

This page is for investment bank analysts cleaning up pitch decks before partner review, pulling comps and building company profiles, and drafting source-grounded sections for client deliverables under deadline pressure. The three workflows sit in the same workspace because they share the same verification pattern: every claim traces to a source the VP or MD can spot-check in seconds.

For the verification mechanics, see Verification-Proof Center. For the broader product, see Model ML Product and Capabilities.

The banker drafting problem

Three distinct workflows collapse into one deadline most weeks:

  1. Deck QA before partner review. The analyst got a deck from a VP Friday afternoon with a Sunday-night-for-Monday-morning deadline. Numbers have to tie, labels have to match, formatting has to be clean.

  2. Public comps and company profiles. Pulling tearsheets, refreshing comp pages, building company profiles from filings and market data.

  3. Source-grounded sections. Drafting the "industry overview" or "company snapshot" section of a client deliverable with enough citations that the VP can spot-check claims without re-reading the source.

All three need to produce output that a senior reviewer can verify quickly. Model ML is a single workspace with the mechanics for all three.

Workflow 1: Deck QA with Auto

Check

From the product and capabilities documentation, AutoCheck (from the Flippr acquisition) is "an AI agent fine-tuned for finance that reviews presentations like a senior employee." It flags:

  • math issues

  • number-tying issues

  • fact-checking issues

  • formatting issues

  • logical inconsistencies

  • spelling and grammar issues

The specific value in an investment-banking context is that AutoCheck catches the errors partners actually comment on during review. Math errors and number-tying problems are particularly damaging because they undermine the analyst's credibility for the rest of the deck. Formatting inconsistency is the kind of thing partners find every time.

From ModelML's use cases documentation, GCM Grosvenor is a published example: "AutoCheck reviews of presentations, memos, and financial documents for numerical issues and logical inconsistencies."

What changes in the analyst's week

Pre-AutoCheck: analyst spends 2-4 hours on Sunday night QA-ing the deck, finds 60-70% of issues, partner finds the rest on Monday.

Post-AutoCheck: AutoCheck runs through the deck in minutes, surfacing math, tying, and formatting issues in a list. Analyst reviews and addresses, partner reviews a cleaner deck.

AutoCheck can be used with "no cloud storage" as an option (source). For confidentiality-heavy decks, this configuration keeps review work off cloud infrastructure entirely.

Workflow 2: Public comps and company profiles

From the product and capabilities doc:

  • Grid is "Model ML's structured analysis layer" that can "generate structured outputs such as earnings summaries, investment memos, company profiles, tearsheets, and comps"

  • The platform can "collate and analyze data from filings, data rooms, confidential documents, FactSet, CapIQ, news, CRMs, and websites"

  • Output exports to PowerPoint in the firm's custom format with "logos, layouts, charts, and structure"

For a public comps refresh: the analyst specifies the peer set and the metrics. Grid pulls from filings and market data, generates the comp page in the firm's format, and provides footnote citations to the source for each data point. The VP reviewing the comp page can click any cell and see where the number came from.

For a company profile: same pattern. Grid generates the profile using the firm's template, with citations on every factual claim (revenue figures, employee count, recent news, key management).

Verification built in

Grid outputs carry superscript footnotes tied to specific datapoints with click-through to source material (source). This is what makes the output spot-checkable rather than re-verifiable.

Workflow 3: Source-grounded drafting for client deliverables

AI Modules can "autonomously execute complex, multi-step workflows" and produce outputs in PowerPoint, Word, or Excel. They can "learn a firm's preferred output format from prior deliverables or templates" and operate "with human review and feedback" (source).

For drafting an industry overview section for a client pitch:

  1. Point AI Modules at the source materials (recent industry reports, target's filings, client-provided context)

  2. Specify the output format (the firm's standard industry-overview template)

  3. Review the generated draft with footnote citations on every factual claim

  4. Revise, approve, move to the next section

Each factual sentence carries a clickable footnote. The VP reviewing can spot-check the controversial sentences in under a minute.

The spot-check timing benefit

The hidden value of source-grounded drafting with clickable citations: the VP's review gets faster, not just the analyst's drafting. A partner who used to spend 30 minutes on an industry-overview section can verify key claims in 5 minutes by clicking through suspect sentences to the source.

Combined banker workflow in practice

A Friday afternoon with a Monday deadline looks different with Model ML:

Friday afternoon. Analyst gets the draft deck from VP. Runs AutoCheck. Gets a list of 12 math/tying/formatting issues. Fixes 10, flags 2 for VP review.

Friday evening. Analyst needs to add a new comps page (peer set changed after the Thursday call with the client). Opens Grid, specifies the new peer set, generates the comp page with the firm's format. Reviews the footnotes, spot-checks three rows. 45 minutes instead of 4 hours.

Saturday. Analyst drafts the industry overview section. AI Modules generates the draft using source materials the deal team collected. Analyst reviews, revises two paragraphs for nuance, approves.

Sunday. VP reviews the deck. AutoCheck issues are already fixed. Comps page is clean. Industry overview has citations the VP can click to verify. Review takes 45 minutes instead of 2 hours.

Monday morning. Deck is client-ready.

Published bank and finance customers

From the ModelML homepage and customer materials, published customer relationships include:

  • HSBC, Barclays, Morgan Stanley, UBS, Julius Baer, Nomura, Western Union, InterAlpen Partners

From Use Cases and Customer Evidence, customer testimonials on the homepage include senior executives from HSBC, Barclays, Morgan Stanley, UBS, Julius Baer, Nomura, and Western Union describing Model ML as improving "precision, speed, insights, and efficiency in financial services work."

A typical banker pilot

An analyst-team pilot (2-3 analysts, 1 associate oversight, 4 weeks):

  • Week 1. AutoCheck on 10 decks the team is actively building. Measure issues caught vs. missed.

  • Week 2. Grid-generated comps and company profiles for 5 live pitches. Measure time-saved and accuracy.

  • Week 3. AI-Modules drafted industry overviews or company sections for 3 client deliverables. Measure VP spot-check time and revision volume.

  • Week 4. Full-week integration: all three workflows in live deal context.

Exit metrics:

  • Time per deliverable component (comp page, industry overview, deck QA)

  • VP review time before vs. after

  • Issues caught by AutoCheck vs. partner review

  • Subjective assessment: "would I staff this on a live deal"

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