AI Fundamental Stock Analyst is an equity research agent that analyzes NYSE and NASDAQ stocks through relative valuation, earnings quality, and variant perception. Unlike generic chatbots that forget your thesis and lead with EPS, this agent remembers holdings, peer sets, and debates. Built on Jenova's unified platform, it combines models from OpenAI, Anthropic, Google, and more with live filings search, consensus retrieval, and research-memo export. This is informational analysis, not licensed investment advice.
Fundamental Stock Analyst prices stocks with relative valuation, defaulting to EV/EBITDA and adding EV/Revenue or FCF Yield when growth, losses, or cash conversion matter. Before any multiple is shown, the AI normalizes non-recurring items, flags stock-based compensation treatment, and uses diluted shares outstanding so the math is comparable across capital structures. Output is an implied price range versus a disclosed peer set or sector median, plus a plain-language read of what premium or discount is already priced in.
Fundamental Stock Analyst treats every quarterly or annual print as an estimate-comparison problem: actuals versus consensus on revenue, operating income or EBITDA, key segment revenue, and forward guidance. Narrative always leads with revenue and margin dynamics—gross margin, operating margin, and mix—because EPS is treated as the lowest-signal metric, polluted by accounting choices and one-time items. The AI searches for consensus estimates before analyzing results, then explains whether the “real” story was the beat, the miss, or the guide.
Fundamental Stock Analyst drafts 3,000–5,000 word buy-side investment memos when you explicitly request a full report, covering business model, competitive position and moat, growth drivers, financials, management, bull and bear cases, relative valuation, and the two or three debates that actually matter. Sell-side structure is available on request, still without a formal rating. After the memo is delivered in-chat, you can export it as a PDF or Word document, or drop comps tables to CSV.
Fundamental Stock Analyst starts from how the company makes money—unit economics, scalability, segment drivers—then asks where the consensus narrative might be wrong. Competitive position, TAM, reinvestment needs, and free cash flow trajectory are used as inputs to intrinsic-value thinking even when the published output stays in multiples. The point of the work is the expectations gap: what the market is pricing versus what might actually happen.
Fundamental Stock Analyst uses discounted cash flow as the conceptual gold standard—revenue growth, operating margins, reinvestment, free cash flow, discount rate, and terminal value—but does not build full DCF models or three-statement forecasts. For “what’s it worth?” questions, the AI pivots to peer multiples with assumptions stated, and will walk through DCF mechanics when you want the framework rather than a fabricated intrinsic value.
| Traditional Approach | Fundamental Stock Analyst |
|---|---|
| Generic chatbots lead with EPS | Leads with margins and cash generation |
| Peer set lost mid-comp analysis | Holds comps, theses, and holdings |
| Stale multiples from model memory | Searches live EV/EBITDA and estimates |
| Weeks to draft a research memo | Delivers 3,000–5,000 word buy-side memos |
Unlimited Memory vs. Chatbot Amnesia
Relative valuation is path-dependent: swap the comps and the implied price silently moves. Generic chatbots lose the curated peer set, the last premium-or-discount stance, and whether you are a learning investor or already speaking in margin trajectories. Fundamental Stock Analyst keeps holdings, tickers already analyzed, open debates, and sophistication level across sessions so a follow-up on the same name continues the thesis instead of restarting a blank “is it cheap?” screen.
Multi-Model AI Access
Screening a name on EV/EBITDA, unpacking a 10-Q margin bridge, and writing a 4,000-word investment memo are different jobs. Single-vendor tools lock you into one stack—ChatGPT to OpenAI, Claude to Anthropic—through all three. On Jenova, paid tiers can route dense comps tables, qualitative moat write-ups, and long-form buy-side memos across OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more without juggling accounts.
Intelligent Tool Integration for End-to-End Workflows
A ticker or earnings question becomes a production path: Google Search for current multiples, 10-K/10-Q filings, and consensus estimates; Reddit-targeted search for contrarian views; YouTube search for earnings-call discussion; then normalized EV/EBITDA tables and implied prices in-chat. From there the same thread can export a memo to PDF or Word, comps to CSV, pin an earnings date on Google Calendar, file notes in Notion, archive to Google Drive or Dropbox, or share via Gmail.
Earnings Quality Over Headline EPS
Most assistants treat an EPS beat as the story. Fundamental Stock Analyst treats net income as a low-signal residual and leads with gross margin, operating margin, EBIT/EBITDA trajectory, and segment mix—the inputs that actually compound into future free cash flow. That hierarchy is why a 2% revenue beat with a guidance cut is not framed as a clean win.
Individual investors doing US equity due diligence
If you are trying to decide what a NYSE or NASDAQ name is pricing in—not what a headline says—the AI will run peer multiples, name the key debates, and show the work.
Learning investors who over-weight EPS
If P/E and earnings beats are the only language you have, this agent explains why margins, cash generation, and diluted share count change the conclusion, without pretending a chatbot can output a licensed price target.
Experienced investors stress-testing variant perception
If you already think in consensus versus bull/bear, you can skip definitions and go straight to what’s priced in, which comps are fair, and where reasonable people disagree.
Portfolio holders tracking existing theses
If you disclose positions, entry context, or a working comparison set, follow-up analysis stays attached to those names instead of treating every ticker as a first look.
Independent researchers who need a long-form memo
If you want a structured buy-side write-up with business, financials, risks, and relative valuation in one document, request a full report and export it when the draft is done.
Free For exploring Jenova:
Plus ($20/month) For individuals:
Premium ($50/month) For regular users:
Pro ($100/month) For power users:
Max ($200/month) For professionals:
Ultra ($500/month) For heavy professionals:
Enterprise ($1,000/month) For businesses:
All plans are billed monthly with no daily usage caps — your full quota is available immediately on each billing date.
Fundamental Stock Analyst is a specialized AI for fundamental equity analysis that runs peer-multiple valuation, earnings-versus-consensus work, and long-form investment memos on US-listed stocks. It is opinionated about method—EV/EBITDA over headline EPS, expectations over current-state snapshots—and educational about the limits of those methods. It does not replace a licensed advisor or a full internal model.
ChatGPT, Claude, and similar tools are general assistants that forget your comps, holdings, and last valuation stance, then often lead with EPS because that is the public language of stocks. This agent keeps peer sets, theses, and investor sophistication in memory; can switch among OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and other providers depending on whether you need a tight comps table or a 3,000–5,000 word memo; and chains live search, filings, Reddit sentiment, and YouTube call coverage into normalized multiples and exportable research. The difference is a continuing research workflow, not a one-off chat about a ticker.
Fundamental Stock Analyst pulls current prices, multiples, estimates, and filings through Google Search, reads retail and contrarian debate via Reddit-targeted search, and finds earnings-call discussion through YouTube Search. Google Scholar is used when you ask about factor models or valuation methodology. Finished work can go to PDF, Word, or CSV, with Google Calendar for earnings dates, Notion for a research library, Google Drive or Dropbox for archives, and Gmail when you need to send the analysis.
No. Fundamental Stock Analyst will not construct a full discounted cash flow model, a three-statement forecast, or a single “intrinsic value” number presented as fact. DCF remains the teaching framework for how revenue, margins, reinvestment, and discount rates create free cash flow, but actionable output is relative valuation with stated assumptions and implied prices. If you need a living spreadsheet model, that work still happens outside this agent.
No. The AI will not say buy, sell, overweight, or “this stock is cheap” as advice. Valuation is framed as implications of assumptions: what peer-median EV/EBITDA implies per share, what a premium embeds about growth or margins, and what multiple compression would mean if the thesis is wrong. Substantive valuation and earnings write-ups carry an informational-analysis disclaimer.
EV/EBITDA is the default because it strips capital structure, reduces depreciation-policy noise, and sits closer to cash-generative capacity than net income. P/E is used when you ask for it or when EBITDA is a poor fit, such as financials. EPS can appear in an estimate-comparison table because that is how consensus is quoted; it is not used as the lead signal of business quality.
Yes, with caveats. Primary coverage is NYSE and NASDAQ equities; the same principles can be applied to other markets, but data availability, accounting standards, and country risk need to be stated. Pre-revenue companies are treated as a stretch for traditional multiples—scenario thinking or revenue multiples only, with the speculative nature made explicit.
A requested full report is a 3,000–5,000 word buy-side memo: executive summary, business overview, industry and moat, growth drivers, financial analysis, management, investment thesis, bear case, relative valuation, key debates, and a monitoring conclusion. Casual questions like “what do you think of this ticker” do not auto-trigger that document; the AI confirms first. Charts and graphs are not generated; numerical comparison lives in tables.
No. There is no scheduled review, price alert, insider-trade watch, or background monitoring. Upcoming earnings dates can be discussed and, when useful, added to Google Calendar in the moment, but nothing runs after you leave the conversation. Recurring “ping me every quarter” workflows are outside the product.
Earnings analysis always includes an actual-versus-consensus table and typically runs 800–1,200 words or more for messy prints. The AI searches estimates first, then weights guidance versus the quarter, segment mix versus the consolidated line, and whether the tape moved on the headline or on something the headline buried. A beat with a cut in the outlook is not treated as a clean win.
Yes. The same agent, memory, and tools run with full parity on web, iOS, and Android, so a comps follow-up or earnings recap is not trapped on desktop. iOS: https://apps.apple.com/app/jenova/id6747692301 — Android: https://play.google.com/store/apps/details?id=ai.jenova.app.prod
No. Conversations, holdings you disclose, and uploaded research are not used to train public models, are encrypted in transit and at rest, and are not sold to advertisers. Treat that as operational privacy, not a substitute for your own compliance rules if you handle client information.
Fundamental Stock Analyst is part of Jenova, the most powerful AI agent platform in the world. Jenova provides unified access to leading AI models from OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more — alongside intelligent tool integrations for end-to-end task completion. The platform features persistent cross-session memory, automated workflow orchestration, and full parity across web, iOS, and Android. Developed by Azeroth Inc., a New York-based technology company, Jenova serves users in over 70 countries.
For developers, the Jenova Agent API provides a fully-managed agent backend with built-in orchestration, multi-model routing, memory, storage, and MCP tool support — replacing the need to stitch together raw LLM APIs, vector databases, and custom tool servers.
Contact: [email protected]