Solution
For buy-side and sell-side research teams — analysts, portfolio managers and CIOs: filings, transcripts and broker notes turned into cited model inputs on earnings day, with tone shifts and red flags surfaced against the thesis.
The problem
Analysts move numbers from PDF tables into Excel by hand — hours per name, every quarter, at analyst salaries.
The model that finally refreshes in week two of earnings season answers a question the market priced on day one.
Confirmation bias reads the transcript for you. Footnotes that cut against the position go unread until they show up in the price.
The product, not a promise
How it works
Pull filings, transcripts, broker notes and news for the coverage universe as they publish.
Extract financial statements, KPI tables and guidance ranges, adjusting for non-GAAP items and fiscal-year offsets.
Map structured data into your proprietary valuation models — no copy-paste from PDF tables.
Surface tone shifts, footnote red flags and divergences between management promises and delivery.
Log every input, assumption and revision with its source, building a governed research record.
Who it's for
Research analyst
Portfolio manager
CIO / compliance
Research teams drown in information. Quarterly filings, earnings transcripts, broker notes and alternative data arrive faster than any team can read, so analysts trade depth for breadth. The highest-paid hours in the building go to copying numbers from PDF tables into Excel, and the model that finally updates in week two of earnings season answers a question the market asked on day one.
The Investment Research Workbench takes the mechanical layer off the analyst’s desk. It ingests filings, transcripts and notes as they publish, extracts financial statements, KPI tables and guidance ranges, and maps them into your proprietary models — handling non-GAAP reconciliations and fiscal-year differences that make naive extraction useless. Models update on earnings day, and every input carries a citation to the page it came from.
The workbench is built to counter the failure mode of human research: confirmation bias. It quantifies shifts in management tone across earnings calls, flags footnote items that cut against the thesis, and highlights divergences between what management promised and what they delivered. Analysts can run semantic screens across the whole universe — “companies discussing supply-chain on-shoring with declining margins” — in the time a keyword search used to take. The system surfaces evidence; the analyst owns the call. Every buy or sell recommendation stays a human decision, backed by traceable data.
An investment process is only as defensible as its record. Because every model input, assumption and revision is logged with its source, the CIO and the analyst look at the same evidence base — and compliance, client reporting and post-mortems draw from a governed research archive instead of a folder of spreadsheets. Coverage grows per analyst because the reading scales; conviction stays human because the deciding doesn’t move.
Objections, answered
Every input carries a citation to the exact page and table it came from, with non-GAAP adjustments and fiscal-year offsets shown rather than silently applied. Checking a number is one click, and anything the platform is unsure about is flagged instead of filled.
No. Structured data maps into your models as they are — the workbench feeds the model, it does not replace it. Your adjustments, your definitions, your sheet structure.
Every model input, assumption and revision is logged with its source, building a governed archive. Compliance, client reporting and post-mortems draw from the same record the analyst worked from.
Ingestion is feed-based — filings, transcripts and notes flow in as they publish. Start with one sector's models and widen from there; nothing requires a big-bang migration.
Watch its latest quarter become cited model inputs, tone signals and flags — live in the demo.
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