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Build the thesis
Convert fragmented company information into structured claims, assumptions, value drivers and risks.
AI-powered fundamental analysis
Zorev transforms company disclosures into a structured, evidence-backed investment thesis. It connects claims, assumptions, scenarios and valuation—then continuously searches for evidence that could confirm or invalidate the analysis.
From company evidence to investment conviction.
Illustrative company · Industrial components · FY2019–FY2024
Revenue growth (YoY)
11.4%
+180 bps vs. prior year
Operating margin
14.2%
−240 bps vs. prior year
FCF conversion
63%
−9 pts vs. 5-yr average
ROIC
12.8%
WACC 8.6%
Net debt / EBITDA
1.9x
Covenant 3.0x
Valuation range
42 – 61
Base case 51 per share
Operating margin evolution (%)
Scenario outcomes — value per share
Illustrative data · assumptions editable
Source documents
Principal risks
ZOREV ASSESSMENT
Source document
Annual report, page 84
Extracted evidence
Gross margin declined by 240 basis points
Financial significance
Higher input costs and adverse product mix
Model assumption
Base-case margin assumption revised
Valuation impact
Illustrative valuation impact: negative
Investment-thesis summary
Durable demand supports mid-single-digit volume growth, but the investment case depends on margin recovery and improved cash conversion. Thesis-breaking condition: input-cost pass-through remains incomplete through the next two reporting periods.
Example analytical workflow · illustrative data
Differentiation
Product value
Many AI tools retrieve information and summarise documents. Zorev structures the complete reasoning process behind a fundamental investment thesis—claims, evidence, assumptions, scenarios, risks and the conditions that would prove the analysis wrong.
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Convert fragmented company information into structured claims, assumptions, value drivers and risks.
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Surface contradictory evidence, fragile assumptions and the conditions that could make the analysis wrong.
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Track new evidence and understand exactly how it changes the original investment case.
Five connected capabilities
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Zorev applies a consistent fundamental-analysis framework rather than producing a generic AI summary. The analysis adapts to the company’s business model, sector, value drivers and risk profile.
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A citation alone is not enough. Zorev helps you assess whether the available evidence genuinely supports the conclusion.
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Zorev actively challenges the initial investment thesis rather than reinforcing it.
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Base, upside and downside cases are connected to explicit operational assumptions. When an assumption changes, Zorev explains how it affects financial performance, valuation and confidence in the thesis.
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When a company publishes new results, filings, presentations or management commentary, Zorev identifies what it means for the existing analysis.
Workflow
Each stage stays connected to the one before it, so a change in evidence flows through assumptions, valuation and confidence.
Counter-thesis
Zorev handles evidence collection, structuring, comparison and continuous monitoring. The analyst remains responsible for interpreting uncertainty, applying professional judgement and making the final decision.
The software supports professional judgement. It does not replace it.
The platform surfaces questions, evidence and alternative interpretations. The analyst determines their significance.
For investment organisations
Apply a common analytical structure while preserving individual analyst judgement.
Give reviewers visibility into the evidence and assumptions supporting a conclusion.
Preserve analytical reasoning instead of losing it across documents and conversations.
Monitor how new information affects the original analysis and investment thesis.
See how Zorev turns company evidence into a transparent, challengeable and continuously updated investment case.
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