Perplexity Finance Search - Retrieve live financial data in a single Agent API call

Stitching together financial data providers is one of the more tedious parts of building a finance agent, and Finance Search is built to remove that.

What it is: A tool in the Perplexity Agent API that routes financial queries to licensed datasets and real-time sources in a single call, returning results in a consistent cited schema.

Developers building on general-purpose search APIs for finance use cases get inconsistent outputs stale prices, missing filings, no citation trail. Finance Search addresses this by routing to structured financial sources rather than open web results. The schema stays consistent regardless of which provider answered, so agents do not need to handle different response formats per data type.

What makes it different: The cited-result design is the part worth paying attention to. Every returned figure is traceable to its source, which matters in finance contexts where auditability is not optional. On FinSearchComp T1, the tool showed the lowest cost per correct answer in the benchmark cohort, attributed in the source material to retrieval efficiency from structured data over general web text processing.

Key features:

• Covers prices, fundamentals, transcripts, estimates, filings, ETFs, and market activity

• Consistent response schema across 14+ licensed data providers

• Inline citations on every result

• Configurable model selection and token usage visibility

• Benchmark-tested defaults documented for faster integration

Benefits:

• Replaces multiple provider integrations with a single API surface

• Retrieval from structured sources reduces noise and cost relative to web search for finance queries

• Citation layer enables auditable agent outputs

• New data sources can be added by Perplexity without developer-side rebuilds

Who it's for: Developers and engineering teams building AI agents for financial research, portfolio monitoring, earnings analysis, or automated market reporting workflows.

The developer surface for financial data retrieval has been fragmented for a long time. A stable, cited, multi-provider abstraction is a reasonable bet on where that infrastructure needs to go.

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Stitching together financial data providers is one of the more tedious parts of building a finance agent, and Finance Search is built to remove that.

What it is: A tool in the Perplexity Agent API that routes financial queries to licensed datasets and real-time sources in a single call, returning results in a consistent cited schema.

Developers building on general-purpose search APIs for finance use cases get inconsistent outputs stale prices, missing filings, no citation trail. Finance Search addresses this by routing to structured financial sources rather than open web results. The schema stays consistent regardless of which provider answered, so agents do not need to handle different response formats per data type.

What makes it different: The cited-result design is the part worth paying attention to. Every returned figure is traceable to its source, which matters in finance contexts where auditability is not optional. On FinSearchComp T1, the tool showed the lowest cost per correct answer in the benchmark cohort, attributed in the source material to retrieval efficiency from structured data over general web text processing.

Key features:

• Covers prices, fundamentals, transcripts, estimates, filings, ETFs, and market activity

• Consistent response schema across 14+ licensed data providers

• Inline citations on every result

• Configurable model selection and token usage visibility

• Benchmark-tested defaults documented for faster integration

Benefits:

• Replaces multiple provider integrations with a single API surface

• Retrieval from structured sources reduces noise and cost relative to web search for finance queries

• Citation layer enables auditable agent outputs

• New data sources can be added by Perplexity without developer-side rebuilds

Who it's for: Developers and engineering teams building AI agents for financial research, portfolio monitoring, earnings analysis, or automated market reporting workflows.

The developer surface for financial data retrieval has been fragmented for a long time. A stable, cited, multi-provider abstraction is a reasonable bet on where that infrastructure needs to go.

I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified.