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AI Equity Research Platform

2026Finance & Capital MarketsIn-depth

What it is

Generates full equity research reports, portfolio analysis and stock comparisons on demand.

Client work is shown without identifying imagery

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Why this existed

Equity research is expensive to produce and therefore rationed to large clients. Retail and mass-affluent investors get a price target with no reasoning attached.

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What we built

A commercial research product: structured multi-section company reports with a user-configurable section count, live company financials, web-grounded company research, side-by-side stock comparison, IPO analysis, portfolio analysis, and a structured recommendation engine with follow-up questioning. Behind it, a saved-report library with filtering and analytics, tiered subscriptions with Razorpay verification and cancellation, usage metering, Google and email auth, a broker instrument search and quote integration, and a programmatic SEO surface with sitemap, JSON-LD and canonical discipline.

Structured multi-section company reports with configurable section count
Live company financials and web-grounded company research
Side-by-side stock comparison and IPO analysis
Portfolio analysis across holdings
Structured recommendation engine with follow-up questioning
Saved report library with filtering and analytics
Tiered subscriptions with payment verification and cancellation
Usage metering per account
Broker instrument search and live quotes
Programmatic SEO with sitemap, structured data and canonical discipline

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How a company uses it

A broker, wealth platform or financial media business offers research at a price point human analysts cannot reach — and keeps the report library as a retention and SEO asset.

Built with

Next.jsOpenAIPerplexitySupabasePrismaRazorpay

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Questions clients ask

Is AI-generated equity research trustworthy?

It is trustworthy in proportion to how much of its reasoning it shows. Research that states a conclusion is worth little; research that lays out the assumptions, the sources and the sensitivities can be argued with — which is what makes it useful. The design choice throughout is to expose structure rather than hide it.

Where does the underlying data come from?

A combination of live financial data feeds, web-grounded research and a broker instrument API for quotes and identifiers. Data licensing is a real constraint in this space and worth resolving early, because it shapes both cost and what you are permitted to redistribute.

Could we white-label this for our clients?

The architecture supports it — the report engine, the subscription layer and the presentation are separable. The questions that actually decide the project are regulatory: what you are licensed to publish, in which jurisdictions, and with what disclaimers.

[ RELATED_WORK ]