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 |