Search vs. research: two AI workflows in finance

Every financial organization today has access to more information than ever before. Market data, earnings calls, filings, sentiment feeds, ESG metrics - each dataset adds another layer of potential insight.

Yet most teams still face the same challenge: turning information into understanding. The problem isn’t data scarcity - it’s cognitive bandwidth. Analysts spend hours collecting documents, checking sources, and aligning facts before they can even begin to interpret what it all means.

Retrieval versus reasoning

This AI-led evolution from retrieval to reasoning is changing how the industry approaches data systems. To bridge it, many firms are moving beyond simple search tools toward what can be called research agents, systems designed not just to find information, but to help explain it.

At Bigdata.com, these two capabilities are represented by distinct yet complementary APIs: the Search API and the Research Agent API, both built on the Bigdata Store, an unified and trusted data layer for AI in finance.

Understanding how they differ reveals more than a technical distinction as it illustrates a shift in how knowledge work itself is being redefined.

Traditional search systems excel at one thing: retrieving relevant material quickly. They answer questions like “Where is this information?” and “What matches my query?”

That’s critical for speed and precision, but it stops short of helping users understand or reason about what they find. Search can tell you that multiple companies mentioned “margin pressure” in recent filings - it can't tell you whether those mentions signal a trend, or how they connect across industries.

As financial workflows become more automated, this limitation becomes a bottleneck. The more data we can access, the more interpretation becomes the scarce resource.

The research agent: from finding to understanding

The Research Agent is designed for exploration rather than lookup. It allows users or other AI systems to engage in multi-turn dialogue with data: asking follow-up questions, refining context, and uncovering relationships across sources.

Technically, it’s built on retrieval-augmented generation (RAG), combining a large language model (LLM) with live access to the Bigdata Store. In practice, this means it can retrieve evidence from trusted datasets, reason about it, and produce synthesized, context-aware insights.

The strength of the Research Agent comes from the depth and diversity of the Bigdata Store, which integrates a growing body of financial and business intelligence:

• Public news: Over 10 million new documents monthly, spanning five years of history.
• Premium news: Licensed content from 200+ sources including Benzinga, MT Newswires, Risk.net, and Al Jazeera.
• Filings & earnings calls: Regulatory disclosures and transcripts from more than 20,000 global companies.
• Corporate communications: Real-time updates from 25,000 investor relations sites.
• Fundamentals: Historical financial data, covering three decades.
• Expert insights: Industry interviews and commentary via Knowledge Ridge.
• Podcasts: Thousands of curated finance shows, transcribed and searchable.
• Alternative data: Job trends, ESG scores, and supply chain intelligence for context beyond financial statements.

Drawing from these sources, the Research Agent doesn’t just return documents, it constructs understanding. For example, it can aggregate sentiment across earnings calls, compare disclosure language year-over-year, or explain how regulatory tone differs between regions.

Search tells you what happened. Research helps you see why it matters.

The search API: precision at scale

The Search API remains the foundation for all retrieval.

It indexes the same Bigdata Store sources but focuses purely on fast, relevant access to raw information.

It’s built for low-latency performance, making it ideal for tasks like:

• Populating an LLM’s context window with verified documents,
• Powering internal or customer-facing search tools,
• Feeding downstream agents with structured snippets or metadata.

It doesn’t interpret or summarize, it simply ensures that every result is accurate, relevant, and fast.

In many modern systems, this is the first step in a two-tier workflow: the Search API identifies the relevant data; the Research Agent interprets it.

The new data-to-insight workflow

In traditional analytics, data retrieval and interpretation were handled sequentially by people. Analysts gathered materials, then reasoned about them manually.

In agentic systems, those steps are becoming distinct machine functions:

• Search provides precision retrieval across massive datasets.
• Research provides reasoning, synthesis, and contextual insight.

Together, they form the core of what many call the data-to-insight pipeline, a shift from systems that store information to systems that understand it.

This distinction isn’t just semantic. It defines how AI will integrate into professional analysis: not as a replacement for human expertise, but as an extension of it—handling the mechanical work of retrieval and summarization so people can focus on interpretation and judgment.

Why this matters for financial organizations

The financial sector’s competitive edge has long depended on access to information. But as that access becomes commoditized, context becomes the differentiator.

Being able to connect a regulatory filing with a news narrative, or a podcast comment with an ESG signal, transforms data from a resource into a perspective. That’s where research agents - and the data layers they depend on - add real value.

As one portfolio strategist put it recently: “The hard part isn’t knowing what happened; it’s knowing what matters.” Bridging that gap is now an infrastructure problem, and one that intelligent retrieval and reasoning systems are beginning to solve.

In an era defined by data abundance, the organizations that win will be those that turn access into understanding. Search systems deliver precision and speed. Research systems deliver synthesis and context.

Together, they form the foundation for the next generation of financial intelligence, where insight isn’t found, it’s built.