The following guest article was submitted by Mikheil Shengelia, Head of Content Strategy at Eagle Alpha, an alternative data aggregation platform providing supporting advisory services for data buyers and vendors. It was written in collaboration with Orbit, a platform specializing in AI-powered data infrastructure for financial institutions that covers 150,000+ companies across 80 countries and processes over 70 million documents annually, delivering the data infrastructure essential for AI-driven finance.
The financial services industry stands at a turning point in AI adoption. While Large Language Models (LLMs) such as GPT-4, Claude, and Gemini demonstrate powerful reasoning and fluent analysis, model sophistication alone no longer determines success. Instead, the defining factor is the data architecture that underpins those AI systems. Without a robust infrastructure, even the most advanced models risk becoming expensive experiments with little return on investment.
Through extensive client engagements with asset managers, hedge funds, and financial institutions, Orbit has identified three recurring misconceptions that consistently derail AI initiatives:
- Treating data as static assets rather than dynamic, AI-ready resources.
- Underestimating the hidden infrastructure costs of production-grade AI.
- Attempting to layer AI onto legacy systems without fundamental reorganization.
This article explores why infrastructure — not model choice alone — has become the primary differentiator in successful agentic AI deployment for investment research.
Orbit will be joining Eagle Alpha’s flagship Alternative Data Conference in New York, taking place on September 17th at One Five One on West 42nd Street. This year’s event will feature two tracks of curated content, the Explorer Hub showcasing new-to-market datasets, and more opportunities to connect with peers and data providers. Please contact us to learn more.
The Commoditization of Intelligence
The performance gap between leading LLMs has narrowed significantly, with differences in financial analysis tasks now measured in marginal percentages. As models converge, competitive advantage no longer lies in which model is chosen but rather in the quality, structure, and accessibility of the data fueling those models.
Consider a seemingly straightforward investment research query: “What strategic shifts has NVIDIA disclosed in the last four quarters?” A general-purpose LLM might generate a superficially plausible answer synthesized from news articles, blog posts, and cached web content. However, an LLM with access to correctly parsed, indexed, and entity-linked SEC filings can provide direct quotes from management discussion sections, quarter-by-quarter comparisons of risk factors, extracted financial metrics with source citations, and detected changes in forward-looking statements.
The difference in output quality is not attributable to model sophistication but rather to the underlying data infrastructure. While vendors and consultants emphasize model selection and prompt engineering, Orbit’s research indicates that organizations achieving meaningful returns from AI investments share a common characteristic: they have fundamentally reimagined their data infrastructure for the AI era.
The Static Data Paradigm and Its Obsolescence
The traditional conception of data ownership in the financial services industry reflects decades of human-centered workflows. Organizations have long equated data possession with data value — if PDFs reside on servers, if spreadsheets exist in shared drives, if transcripts are archived in document management systems, the organization ostensibly “has data.” This static conception functioned adequately when human analysts manually processed information, but it becomes a critical limitation in the age of agentic AI.
Agentic AI systems cannot work effectively with static files without transformation. Financial documents must be parsed into machine-readable text while preserving structural elements like tables and charts, indexed for search, linked to entities and timelines, and enriched with metadata—continuously as new versions appear.
The transformation from static to dynamic data requires sophisticated pipelines that organizations consistently underestimate. At Orbit, processing a single complex financial document involves multiple stages of advanced PDF parsing that preserves tables, charts, and formatting integrity. Natural language processing models specifically tuned for financial terminology must extract meaning from technical language.
Entity recognition and disambiguation systems must correctly identify and link references across our database of over 150,000 companies globally. Temporal alignment algorithms ensure that period-over-period comparisons maintain accuracy. Quality validation processes guarantee 99 percent or higher accuracy in extracted information. The figure below shows an example of a portfolio monitoring tech leaders.
Figure 1: Example of Orbit’s Platform
The Hidden Economics of Production-Grade Agentic AI
The true cost of production-grade agentic AI extends far beyond LLM usage. While organizations carefully budget for API calls—typically $0.001 to $0.02 each—they often overlook the infrastructure “iceberg” beneath the surface. In practice, for every $1 spent on LLMs, $5–10 is required for supporting systems. These costs span multiple layers: $2–3 for data parsing and normalization, $1–2 for vector databases and retrieval infrastructure, another $1–2 for orchestration and workflow management, and up to $3 for governance, compliance, and security.
The impact becomes clear in real-world scenarios. Monitoring earnings for 500 companies may cost only $40 in LLM usage, yet supporting infrastructure—from ingestion and parsing pipelines to audit systems—can exceed $10,000 annually. As firms scale use cases across dozens of workflows, infrastructure costs can quickly surpass $100,000 per year, requiring dedicated teams to manage data pipelines, integrations, and compliance. Each new data source or workflow compounds complexity, making scalability impossible without upfront investment in robust architecture. Organizations that ignore these hidden economics risk expensive retrofits—or abandoned AI initiatives altogether.
The Integration Imperative: Why Systematic Transformation Matters
Many financial institutions fall into the “bolt-on fallacy”—assuming AI can simply be layered onto legacy systems. In reality, agentic AI requires deep architectural change. Decades of siloed growth have left financial data fragmented across systems, with inconsistent entity mapping, poor temporal alignment, and access patterns designed for humans, not AI. Such environments cannot support AI agents that depend on unified, contextualized information.
The required transformation is profound. Organizations must establish a single source of truth that encompasses all data types, ranging from structured financial metrics to unstructured documents and alternative data streams. Consistent entity resolution must link every reference to companies, securities, and individuals across all sources. Temporal synchronization must enable seamless time-series analysis regardless of data origin. A unified access layer must provide AI agents with consistent interfaces to all information. Complete audit trails must track every piece of data from source to insight. Role-based access controls must enforce compliance requirements while maintaining system performance.
This systematic transformation encompasses three interdependent dimensions. First, data governance must evolve from a compliance checkbox to a strategic capability. Organizations must establish clear data ownership, implement quality standards that ensure AI readiness, create master data management for entities and relationships, and develop systematic update and validation processes to maintain data currency.
Second, technical architecture must transition from file-based to API-based paradigms. Traditional architectures built around batch processing and file transfers cannot support the real-time, interactive nature of agentic AI. Organizations must implement event-driven processing pipelines that react to new data immediately. Vector databases must complement traditional storage to enable semantic search. Stream processing capabilities must handle real-time data flows. Modern observability tools must monitor system health and performance to ensure optimal operation.
Third, organizational structures must adapt to the new reality. The traditional separation between investment professionals and technology teams becomes untenable when AI agents require constant tuning and monitoring. Organizations must hire data engineers who understand financial markets alongside traditional analysts who understand technology. Cross-functional teams must bridge the gap between business requirements and technical capabilities. AI governance committees must establish policies that strike a balance between innovation and risk management.
Building AI-Native Infrastructure: A Systematic Approach
Organizations cannot jump straight to complex multi-agent systems; success requires phased progression. The foundation phase (months 1–3) focuses on auditing data assets, parsing key documents like SEC filings and transcripts, building entity masters, and deploying basic search and retrieval—often exposing hidden gaps in usable data. During this phase, organizations often discover that data they believed they possessed is actually inaccessible or unusable for AI purposes, necessitating remediation efforts.
The integration phase (months 4–6) unifies systems through APIs, ensures temporal alignment for accurate time-series analysis, and launches initial agentic workflows with audit trails and governance frameworks. This stage often demands strong change management to overcome resistance.
The scaling phase (months 7–12+) expands coverage to full investment universes, adds real-time processing, and deploys sophisticated multi-agent workflows. Real-time processing capabilities must be implemented for time-sensitive data such as earnings releases and regulatory filings. At this point, infrastructure optimization—through caching, compute allocation, and storage management—becomes critical to control rising costs.
Measuring Success in the AI-Augmented Era
The metrics that matter in AI-augmented investment research differ fundamentally from traditional KPIs. Coverage metrics reveal the multiplicative effect of AI augmentation—successful implementations enable analysts to meaningfully cover five to ten times more companies than previously possible. Velocity metrics demonstrate the acceleration of insight generation, with the time from data release to actionable intelligence dropping by 90% or more. Quality metrics ensure that increased speed does not compromise accuracy, with leading organizations achieving extraction accuracy exceeding ninety-nine percent. Economic metrics validate the business case, showing total cost per insight generated falling by eighty percent despite significant infrastructure investment.
These quantitative measures tell only part of the story. Qualitative transformations often prove equally significant. Investment teams report fundamental shifts in how they approach research, moving from reactive analysis of individual documents to proactive monitoring of entire sectors. The ability to ask natural language questions of vast document collections changes the types of hypotheses that can be tested.
The Competitive Imperative and Future Outlook
The organizations that will thrive in the AI-augmented future of investment research are those that recognize a fundamental truth: LLMs are rapidly becoming commoditized utilities, but the data infrastructure that feeds them remains a source of sustainable competitive advantage.
The implications extend beyond individual institutions to the industry’s overall structure. As AI-native organizations achieve order-of-magnitude improvements in research productivity, traditionally organized competitors face an existential challenge. The gap between leaders and laggards will widen rapidly, potentially reshaping market dynamics. Early movers who invest in comprehensive infrastructure now will compound their advantages as they accumulate processed data, refined workflows, and operational experience.
For more information on topics relating to data sourcing and compliance and to learn more about how Eagle Alpha is assisting financial institutions with their alternative data strategy, please contact us.