Build your next financial agent
Written by Bigdata.com 's Chief Product Officer, Aakarsh Ramchandani
I've tried to solve the financial search problem more times than I care to admit.
Over 10 years, I've built search systems that looked great in demos but broke in production. I've integrated with APIs that promised financial intelligence but couldn't distinguish between Apple Inc. and Apple Hospitality REIT. I've watched teams spend months retrofitting compliance/audit trails into systems that were never designed for them from the ground up.
When we started building Bigdata.com, I thought we'd be building the best Financial Search Engine. Here we are, 8 months after launch, and I've realized we accidentally solved something much more fundamental: the search infrastructure problem that every financial agent implementation eventually hits.
My own breakthrough came from finally understanding why all my previous attempts had failed. It wasn't about building better search algorithms or more sophisticated NLP. It was about accepting that financial intelligence workflows have requirements that general-purpose AI systems simply weren’t designed for.
The Demo That Always Breaks
"That's impressive, but can I configure which queries get routed to my in-house compliance-approved LLM versus your hosted models? Is this information point-in-time, or did you overwrite historical data? Can you map these results back to my portfolio CUSIPs? Can you show me the exact context chunks that influenced this specific response? Can I configure which sources get pulled in for <these> queries?"
I've watched this moment happen dozens of times. A sleek AI vendor walks into a bank or hedge fund, shows off their impressive financial chatbot that can answer complex investment questions, and the room nods appreciatively. Then the CTO asks the questions that kill every deal—not about basic source attribution (everyone can do that now), but about enterprise configuration, data integrity, and workflow integration.
The demo suddenly gets very quiet.
After 10 years of building search systems for financial institutions, I've seen this pattern repeat endlessly. Beautiful demos that crumble the moment they encounter enterprise reality. Teams spend months trying to retrofit their enterprise needs into systems that were never designed for it. CTOs reluctantly build their own search infrastructure because nobody else understands what "mission-critical" actually means in finance.
That's why we built Bigdata.com differently.
The Enterprise Problem
Here's what every AI team building agents in financial services knows but rarely says out loud: basic source attribution isn't the problem anymore—it's enterprise configuration and data governance.
Modern LLMs can show you which documents they referenced. What they can't do is let you configure which types of queries get routed to your compliance-approved internal models versus external APIs. They can't guarantee point-in-time data integrity where historical information remains unchanged. They can't map results back to your specific portfolio holdings using your reference data systems.
Consider what sophisticated financial institutions actually need:
Try configuring any of this with Perplexity's API, Google’s search or OpenAI's web search. You'll quickly discover that having basic source attribution is just table stakes—the real challenge is enterprise-grade configuration and governance.
What Everyone Else Gets Wrong
Let's be honest about the competitive landscape. Every major AI provider has some form of search or grounding capability:
Perplexity offers excellent conversational search, but their API is a black box. You send a query, you get an answer, and you have zero visibility into how that answer was constructed. Can’t filter for specific document types, can’t provide a list of entities you are looking to find, can’t see an audit trail of what specific information was referenced when citing its answer. Try explaining that audit trail to your PM or even worse, your compliance team.
OpenAI has web search in their completions API. I personally use it and love it. But it's designed for general internet search, not financial intelligence workflows that depend on access to deep datasets. Try asking it: "Look through last quarter's filings for the SP500 and identify all companies that mentioned Uber and in what context. Prepare me a report on this". It will produce an amazing report. You’ll never know if it actually searched through all filings, or just found the top most relevant filings and constructed a, to be completely fair, pretty amazing answer.
Google offers grounding with search results. Yet, all you get is a link per citation. This is transparent, but it is unable to cite what exactly within the doc was read, what part of the document was cited. Their search still doesn't allow you to specify a filing type, an earnings call type, or select a premium source of news. Neither does it help you pass in queries that help you distinguish Apple Inc, Apple Hospitality REIT, and Apple Green Holdings.
IMPORTANT NOTE: They all can search (and search really well!), but you'll quickly discover they give you very limited control. You can't filter by filing date ranges, it doesn't give you control over document types, has no entity recognition for financial contexts, and can't target source/section-specific metadata suitable for downstream agent processing. If you want control, you’re going to have to build it yourself.
Here's the deeper problem: all of these solutions assume you're okay with using their models. For a lot of users, this is OK. But for far too many enterprise use cases, you want control over the LLM depending on the queries you run. You want to be able to route queries to the LLM best suited for that answer. Effectively, and something I see play out more and more, you're not really building your intelligence stack—you're renting theirs.
For consumer applications, that's fine. For mission-critical financial agents, the type that you will build your next alpha/workflow on top of, it's almost architectural malpractice.
Your Answers Are Only As Good As Your Sources
Here's another thing I learned the hard way: even if you solve the enterprise configuration problem, even if you build the perfect search API, your financial intelligence is still only as good as your sources.
Do you want better answers? You need access to sources you actually trust. And most of those sources—an increasingly larger portion of them—are gated behind paywalls where web scrapers can't reach them.
We understood this problem 20 years ago when we partnered with Dow Jones, giving us access to the Wall Street Journal, Barron's, MarketWatch, and their entire financial news ecosystem. Since then, we've systematically expanded our premium source coverage to include everything from management presentations and earnings transcripts to specialized providers like FXStreet, Midnight Trader, Quartr, Benzinga, FactSet, S&P, Global Capital, Risk.net, CryptoWires to name a few. We're adding new premium providers every month.
Soon, you'll be able to search Analyst Research reports, hedge fund letters, investor podcasts, private company websites, and other newsletters that never see the public web.
We're also building native email & Google Drive adapters that can collect and organize your most important alpha—what you actually read, subscribe and save. Your proprietary research subscriptions, your internal analyst reports, your curated industry newsletters. The sources that give you your competitive edge.
This is where every traditional Chat API falls short. They're often limited to what they can scrape from the public web, not what you can pay for to get access to the best curated content that matters to you.
We're building infrastructure that connects to the sources that actually matter for financial decision-making.
Knowledge Graph + Full Control
This is where Bigdata's architecture becomes fundamentally different. We provide the search infrastructure and knowledge graph, but you bring your own models. You own the entire intelligence stack.
Let me show you what this looks like in practice:
Scenario 1: M&A Intelligence with Audit Trails
Imagine you're building an AI system to monitor M&A activity. Your system needs to track X Corp, which used to be… "Twitter". You need systems that understand that both entities refer to the same underlying business for historical analysis.
We support this natively.
Now, how would you leverage this in a query?
The key difference: You can prove exactly how "Twitter" was mapped to entity ID "F8A21C", how that entity automatically includes X Corp and TWTR ticker references, which specific analyst sources were searched, and how each document was retrieved and ranked. Every step is logged, encrypted, and owned by you.
Scenario 2: Multi-Company Intelligence with Entity Recognition
Here's a query that breaks every other system: "Look through last quarter's filings and identify all companies that mentioned Uber and in what context. Prepare me a report on this."
This requires multiple sophisticated capabilities:
This is financial intelligence, not just search.
You get entity recognition across thousands of documnts, contextual classification of mentions, and structured data that your agents can immediately process—all with complete audit trails.
Beyond Chat: Building Workflows That Need Control
Chat interfaces are great for end-user experiences. We know this and support this natively. You can also use our Search API to implement your own chat completion service if you prefer to DIY this.
But here's what we've learned: not all workflows need "chat".
If you're building a financial copilot, a stock text screener, your own ReACT agent, or any sophisticated research automation, you want full control over what is searched and how it is searched. You need to configure search parameters, apply specific filters, route different query types to different data sources, and get structured outputs that your downstream systems can process.
Sometimes, the intelligence layer underneath needs granular control. You need to decide whether a query should search earnings transcripts or SEC filings, whether to apply time-based filters, how to handle entity resolution, and what structured metadata to extract.
This is why the search-first architecture matters. Your ReACT agent can orchestrate multiple targeted searches, your screener can apply precise filters, and your copilot can provide exactly the context it needs without being limited to what a chat completion decides to search for.
This approach is impossible with traditional completions APIs. You don’t get the precision of targeted search, the flexibility of dynamic context expansion, or the ability to get structured outputs that you can stitch together with your reference data.. The stuff that your production agent frameworks require, all while maintaining complete audit trails.
Building Mission-Critical Financial Intelligence
When you control the entire intelligence stack and get structured/unstructured, agent-ready outputs, entirely new capabilities become possible:
Multi-company competitive intelligence where your agents can analyze how hundreds of companies discuss the same topic, automatically classify relationship types, and generate comprehensive market analysis reports with complete source attribution.
Cross-filing trend analysis that can track how language around specific topics (ESG, AI adoption, supply chain disruption) evolves across industries and time periods, with entity recognition that understands corporate relationships and subsidiaries.
Real-time compliance monitoring that can prove exactly which market events triggered which algorithmic decisions, with complete source attribution, entity resolution chains, and model versioning.
Agentic research workflows where LangGraph-like agents can orchestrate complex multi-step investigations across thousands of documents, with each step fully auditable and each result structured for downstream processing.
This isn't about building better chatbots. This is about creating financial intelligence systems that can operate at the scale and rigor that modern markets demand, while feeding structured data to the sophisticated agent frameworks that are becoming the standard for enterprise AI.
The Choice is Architectural
Every AI team in the orgs we’ve worked with faces the same choice: build your intelligence stack on someone else's foundation, or build it on infrastructure you can own and control.
The demo that looks impressive in the conference room becomes a liability in production when you can't explain how it works. The AI system that saves analysts hours becomes useless when auditors can't trace its decision-making process.
Bigdata.com gives you a third option: bring your models to our search infrastructure. Get enterprise-grade financial search capabilities without ceding control of your AI stack. Build intelligence systems you can own, audit, and defend.
Stop retrofitting enterprise compliance and governance into systems that were never designed for it. Stop explaining to regulators why you can't provide audit trails. Stop depending on someone else's black box for your mission-critical decisions.
Your models. Our search. Your competitive advantage.
Ready to build financial intelligence you can actually trust? Explore our API at docs.bigdata.com or see the platform in action at app.bigdata.com.