Fiscal.ai — the platform formerly known as FinChat — is built around AI-assisted, conversational research: ask a question in plain language and get an answer synthesised from financial data, filings, and transcripts. ScreenerHero is built around the opposite workflow: set explicit filter criteria and get a ranked list of every matching stock across a market. Both are genuinely useful, but for different stages of the research process — AI-conversational tools are strongest once you already have a specific question about a specific company; systematic screeners are strongest for the earlier step of discovering candidates across an entire market you haven't looked at yet.
Last updated: July 2026.
What Fiscal.ai does well
Fiscal.ai's core product is an AI copilot layered over institutional-quality financial data — revenue and profit segment breakdowns, analyst estimates and price targets, DCF modelling tools, and the ability to ask natural-language questions that get answered by pulling from company filings, transcripts, and fundamentals directly. It also includes a stock screener, watchlist dashboards, and competitor comparison tools.
For an investor who wants to ask something like "how has this company's gross margin trended over the last five years, and what did management say about it on the last earnings call?" and get a synthesised answer without manually digging through transcripts, this is a genuinely useful capability that a traditional filter-based screener doesn't attempt to replicate.
Where the AI-conversational model has real trade-offs for systematic screening
Conversational tools answer one question about one (or a few) companies at a time. Fiscal.ai's strength is depth on a specific query; a systematic screener's strength is breadth — scanning an entire market simultaneously against explicit, adjustable criteria. "Which European companies have operating margin above 15% and Net Debt/EBITDA below 2x" is a screener-native question; it's a much less natural fit for a conversational, one-company-at-a-time research flow.
AI-synthesised answers require some trust in the underlying summarisation. A raw filter result ("this company's operating margin is 17.3%") is directly verifiable against the source data. An AI-generated summary of a trend or a management comment, while generally reliable for straightforward factual queries, introduces a layer of synthesis between you and the underlying number that a raw-filter screener doesn't.
European small-cap and alternative-market coverage depth is a question mark for any tool built primarily around US-centric filings and transcripts. AI research tools trained and built around SEC filings and US earnings call transcripts don't automatically have the same depth for European small-cap disclosure, which follows different formats and, for many smaller companies, has far less transcript and analyst-estimate data to synthesise from in the first place.
Fiscal.ai vs. ScreenerHero: comparison table
| Feature | Fiscal.ai | ScreenerHero (free) | ScreenerHero ($29/mo) |
|---|---|---|---|
| Core interaction model | Conversational AI query | Filter-and-sort UI | Filter-and-sort UI |
| Systematic multi-criteria screening | Basic screener included | ✓ (core feature) | ✓ (core feature) |
| AI-synthesised filing/transcript summaries | ✓ (core feature) | ✗ | ✗ |
| DCF modelling tools | ✓ | ✗ | ✗ |
| US large/mid cap coverage | ✓ | ✓ | ✓ |
| EU small cap / alt markets | Uncertain depth | ✓ | ✓ |
| Free tier | 10yr financials, 2yr KPIs, 1 dashboard, basic AI Copilot | Full screener | — |
| Price | Free · $39/mo Pro (billed annually) | Free | €29/mo |
Who should use Fiscal.ai
Investors who want to research a specific, already-identified company in depth quickly. If you already know which company you're looking at and want fast synthesis of its filings, transcripts, and trends via natural-language questions, this is exactly the tool's intended use case.
Investors who want DCF modelling tools built into the same platform as their research. The combination of AI-assisted research and modelling in one product is a genuine convenience for that specific workflow.
Who should use a dedicated screener instead
Investors who want to discover candidates across an entire market first. If the question is "show me every company in this market matching these criteria," rather than "tell me about this specific company," a filter-and-sort screener covering the full listed universe is the more direct tool.
Investors who want to verify every number against a transparent, filterable source rather than an AI-synthesised summary. Raw filter results are directly auditable in a way that adds no interpretive layer between the data and the number shown.
Investors specifically screening European small- and micro-cap markets, where a tool built primarily around US filings and transcript depth is less likely to have equivalent coverage depth.
Bottom line
Fiscal.ai and ScreenerHero aren't really solving the same problem — Fiscal.ai is built for conversational depth on a specific company once you already have a question; ScreenerHero is built for systematic breadth across an entire market when the question is "who matches these criteria at all." Many investors reasonably use both at different stages: a screener to generate a candidate shortlist across a market, and an AI research tool to dig deeper into the shortlisted names once identified.
Frequently asked questions
Is FinChat the same as Fiscal.ai?
Yes — FinChat rebranded to Fiscal.ai. The product and underlying platform are the same; only the name has changed.
Can Fiscal.ai replace a stock screener?
It includes a basic screener, but its core strength is AI-assisted, conversational research on specific companies rather than systematic, filter-based screening across an entire market. For discovering candidates across a full market by explicit criteria, a dedicated screener is generally the more direct tool.
Does Fiscal.ai cover European small-cap stocks?
Coverage depth for European small- and micro-cap names is less established than for US companies, since AI research tools built around filing and transcript synthesis tend to have the most depth where source material (SEC filings, earnings call transcripts) is most standardised and abundant — a condition that applies more consistently to US large- and mid-cap companies.
Should I use an AI research tool or a traditional screener?
They serve different stages of the research process. Use a systematic screener to discover which companies match specific fundamental criteria across an entire market. Use an AI-conversational research tool once you have a shortlist and want faster synthesis of a specific company's filings, transcripts, and trends.
Screen European, US, and Canadian stocks by raw fundamentals → — free, no account required. Discover candidates across all major exchanges before diving into deeper research.