Twenty names. Sixty primary documents. Three weeks on a deadline. The reading and extraction phase is the largest mechanical workload in the analyst calendar. Marvin compresses it so the judgment phase gets the time it deserves.
The problem with earnings season
Earnings season is the most mechanical, most predictable, most time-consuming workload in the analyst year. An analyst covering 20 names absorbs roughly 60 primary documents (press release, earnings call transcript, 10-Q) across a three-week window, every quarter, on a deadline.
The reading is templated. The extraction is templated. The cross-referencing against prior guidance is templated. What isn't templated is the thesis call, the model rebuild, and the conversation with the PM about what the quarter actually means. Compressing the templated phase is what frees the calendar for the part that needs human judgment.
What generic AI tools miss
The temptation, with any large language model in the loop, is to point it at an earnings release and ask for a summary. That produces a document. It does not produce useful analyst output.
An earnings release is not a standalone document. It is one event in a sequence: prior guidance, the press release, the call, the 10-Q, the analyst-day commitments, the previous four quarters of the same management saying related things. The relevant question is never "what is in this document". It is "what changed, what was promised before, and what is now different from the picture we had yesterday". Generic summarisation tools do not know yesterday. Marvin does, because each release is read against the prior record the system already maintains for that company.
The four-stage sequence
Stage 1: Ingest
Automated Data Import monitors SEC EDGAR, non-US filing systems, IR pages, and earnings call services continuously. New documents are ingested within seconds of publication. Audiovisual content is transcribed with speaker identification. Presentations and slide decks are structured for downstream querying. Nothing has to be downloaded by hand.
Stage 2: Draft
A Deep Research Agent reads each primary document and produces a structured delta against the prior record: what changed since last quarter, what's new in management commentary, what shifted in guidance. The draft evolves across the release cycle. A first version appears on the press release. It updates when the call transcript lands. It finalises when the filing drops. Each point links back to the source passage.
Agents can be pre-configured to draft directly into the firm's note template, so the analyst opens a partial draft in house format rather than a blank page.
Stage 3: Interrogate
The analyst reviews the draft, jumps to the passages flagged as material, and uses AI Analyst Chat for the specific questions that still need answering. How has management's view on China demand evolved over the last four calls. Is the capex line consistent with what they promised at the capital markets day. Did the CFO's tone on margin recovery shift between Q&A and the prepared remarks. The answers come back with citations to the underlying passage.
Stage 4: Monitor
Earnings coverage doesn't stop when the cycle closes. Pre-announcements arrive between quarters. Management revisits prior commitments at conferences. Peers report and reset the comparable. Deep Research Agents can be pre-configured to run on every release, every cycle, producing the same structured outputs (KPI deltas, guidance scorecards, peer drift, period-over-period commentary) each time. The repeatable parts of the cycle stop costing analyst time. The configuration patterns sit in the tailored outputs solution.
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What compresses, and what doesn't
The underlying research is here: up to about 40% of routine research time. Earnings coverage is one of the largest contributors to that number because it is mechanical and recurring at exactly the moments when the calendar is tightest.
What compresses is reading and extraction. Pulling guidance numbers out of a press release. Cross-referencing this quarter's commentary against last quarter's. Building the peer-comparison table that's the same shape every cycle. Identifying the three lines in the 10-Q that actually changed.
What doesn't compress is the thesis work, the PM conversation, the model rebuild when something material has shifted, the rating call. Those stay with the analyst. The product compresses the surface around them, not the judgment itself.
Where this sits in the wider workflow
Earnings season is one input into a broader research process. The structured guidance scorecard the cycle produces feeds the management quality assessment, where the question is whether management consistently delivers on what they promise. The reclaimed analyst capacity opens up the coverage expansion question of what to do with that time. And the way it lands inside an actual analyst's week looks different on the buy-side (more time for off-page work that generates alpha) than on the sell-side (a faster path to the published note).
