Picture this: You're researching a potential investment and ask ChatGPT about management's commentary on supply chain challenges in their latest earnings call. It responds confidently, citing specific quotes and providing analysis. You dig deeper—and discover the article it referenced doesn't exist. The quotes are plausible but fabricated. You've just wasted 45 minutes chasing phantom sources.
Or this scenario: Your compliance team asks you to explain how your AI-assisted investment recommendation was generated. They need an audit trail. You have nothing to show them except a chat history with no source attribution, no timestamp verification, and no way to prove the data was accurate at the time of decision.
These aren't hypothetical problems. They're common frustrations for financial professionals trying to leverage AI tools built for general consumers, not institutional investors.
I recently explored BigData.com, a platform built by RavenPack specifically to address these gaps, and found it offers a genuinely different approach. Rather than retrofitting enterprise features onto consumer AI, it's built from the ground up as financial research infrastructure. Here's my assessment.
The Problem Space
Most AI platforms weren't designed for financial use cases. Beyond the hallucination and audit trail issues, there are deeper structural problems:
Financial institutions need several capabilities that consumer AI doesn't provide:
- Model routing governance to keep sensitive portfolio data on internal systems
- Point-in-time data integrity so historical queries reflect what was known at specific moments
- Financial identifier mapping (CUSIPs, ISINs) for integration with existing systems
- Paragraph-level source attribution for compliance and verification
Generic platforms like Perplexity or ChatGPT can't deliver this. They force you to use their models, can't access premium gated content, and provide limited audit trails. BigData.com attempts to solve these problems with financial-specific infrastructure.
Platform Architecture
The Data Foundation
BigData provides access to 15,000+ curated sources including premium content inaccessible to web crawlers: Dow Jones, Wall Street Journal, Barron's, MT Newswires, Benzinga, Risk.net, and FactSet. The platform includes 20+ years of archived content, regulatory filings from 50+ countries (since 2010), 30+ years of fundamentals, and alternative data (jobs, ESG, transcripts).
This meaningfully differs from generic AI. Queries access earnings transcripts and regulatory filings, not Wikipedia and blogs—sources where information asymmetries still exist.
Knowledge Graph & Embeddings
The platform includes a financial knowledge graph with 12+ million entities (companies, executives, products, locations) and 7,000+ event categories. More importantly, it uses custom 384-dimension embeddings fine-tuned for financial language.
Why this matters: When you search for "pricing power," the system understands the financial concept (ability to raise prices without losing customers), not generic market dynamics. This domain-specific context is where BigData differentiates from general-purpose models.
Three Core Components
- Research Agent: Conversational interface with responses grounded in curated sources and paragraph-level citations. Combines web data, premium sources, and uploaded proprietary content.
- Search Service: Retrieval-only API for building workflows. Filter by source, date range, entity, document type, and sentiment. Returns structured JSON for downstream processing.
- Bring Your Own (LLM/Files): Route queries to your own models and search across private data. Sensitive queries stay on your infrastructure. Uploaded files remain private and aren't used for training.
Practical Applications
BigData provides implementation guides called "Cookbooks" that demonstrate real workflows across four pillars. Having reviewed these, here's what the platform actually enables:
Market & Financial Analysis
Pricing Power Analysis: The platform can mine news and transcripts to identify companies with strong versus weak pricing dynamics. For example, during inflationary periods, you could systematically screen which consumer companies successfully passed through cost increases versus those facing resistance.
Thematic Research: Multiple cookbooks tackle trend mapping. The "AI Cost Cutting" workflow distinguishes companies selling AI efficiency tools from those adopting them, tracking adoption momentum over time.
Inflation & Macro Analysis: The "Tracking Inflation Drivers" cookbook deconstructs inflation narratives into components (energy, wages, supply chain), maps them to corporate discussions, and produces time-stamped insights.
Risk Management
Risk Taxonomy Systems: The "Risk Analyzer" cookbook shows how to build custom risk taxonomies (geopolitical, regulatory, operational), search relevant documents, and score exposure by company or sector.
AI Disruption Assessment: One cookbook evaluates companies' exposure to AI disruption versus their strategic adaptation efforts, producing net resilience scores.
Trade & Tariff Analysis: The "US Tariffs" cookbook detects corporate mentions of tariff risks, classifies mitigation strategies (supplier diversification, pricing adjustments, etc.), and generates reports.
Screening & Monitoring
Thematic Screeners: Convert investment themes into structured taxonomies, then scan filings and transcripts for related narratives. The platform quantifies exposure by company and sector with evidence-based labels.
Credit Events: Automated extraction of rating agency actions, watchlists, and outlooks across companies builds time-series event data.
Governance Tracking: Monitor specific executives or directors across news and filings, measuring visibility, timing, and sentiment.
Assessment of Use Cases
The cookbooks demonstrate breadth but also reveal the platform's nature: it's infrastructure for building workflows, not turnkey solutions. Each cookbook requires customization—defining taxonomies, selecting sources, calibrating thresholds. The value proposition is control and repeatability, not plug-and-play simplicity.
The quality depends heavily on taxonomy design and source selection. Narrative-based signals work best when paired with quantitative data for confirmation.
Critical Comparison
Generic AI platforms (Perplexity, ChatGPT, Google) provide conversational interfaces and basic source citations but lack:
- •Premium gated content access
- •Financial metadata filtering (filing types, entity relationships)
- •Point-in-time accuracy guarantees
- •Model routing control
- •Enterprise audit trails
BigData adds financial-specific infrastructure: curated premium sources, custom embeddings, knowledge graph with 12M entities, complete model control, and regulatory-grade audit trails. The tradeoff: it's infrastructure requiring setup, not a plug-and-play chatbot.
Implementation Considerations
For analysts: Start with the chat interface for ad-hoc research, build custom watchlists, and explore the Cookbooks for workflow examples.
For teams: API integration enables custom agent development with frameworks like LangGraph. Upload proprietary content for unified search across public and private data.
The learning curve is steeper than consumer AI, but that's inherent to the control and customization the platform offers.
Platform Strategy: Building an Ecosystem
An interesting aspect of BigData's approach is their focus on community building. The platform encourages users to share their use cases and implementations, creating a library of practical examples beyond their official Cookbooks.
More significantly, BigData actively recruits third-party content partners to publish data through their platform. This partner program allows content providers to monetize their data by reaching BigData's institutional user base, while expanding the platform's source coverage.
Potential advantages:
- •Continuous expansion of source coverage without BigData building everything internally
- •Network effects as more partners attract more users and vice versa
- •Specialization opportunities (niche data providers reaching institutional buyers)
The strategy positions BigData as a platform/marketplace rather than just a data vendor, which could accelerate coverage breadth but requires strong curation and quality management.
Limitations & Questions
Some considerations based on the platform review:
- Cost structure isn't publicly disclosed - likely enterprise pricing
- Lock-in concerns with proprietary embeddings and knowledge graph
- Data freshness depends on source update frequencies
- Completeness of premium source coverage varies by region/sector
The platform is clearly designed for institutional users with significant research budgets and existing data infrastructure.
Bottom Line
BigData.com addresses real gaps in the financial AI landscape. Generic platforms were built for consumer use cases and retrofitted for finance. BigData was purpose-built for institutional investment workflows.
The core value proposition: access to premium sources, financial-specific entity recognition, model routing control, and audit trails that compliance demands. These aren't features you can bolt onto ChatGPT—they require different architecture.
Who should consider it:
- •Investment teams spending significant time on manual research
- •Institutions needing regulatory-grade audit trails
- •Organizations building custom AI workflows for portfolio management
- •Firms wanting to analyze premium sources at scale
Who doesn't need it:
- •Individual investors doing casual research
- •Teams satisfied with generic AI and public sources
- •Organizations without resources to integrate API-driven infrastructure
The platform represents a category shift: from AI as a research assistant to AI as infrastructure you build on. Whether that tradeoff makes sense depends on your workflows, compliance requirements, and the value you place on accessing premium financial sources programmatically.
For institutions serious about AI-driven research that can withstand regulatory scrutiny, BigData merits evaluation. It's not the only solution in this space, but it's among the first to treat financial AI as an infrastructure problem rather than a chatbot problem.
Related reading
Get the next essay in your inbox
One or two essays a month on AI and investing, written from inside the industry. No product spam.
Medusa is the AI-native operating system for lean investment teams, built on your stack. The ideas in these essays run in production there.
Watch the Risk Agent demo →