How can Alternative Data Providers get Discovered in the Agentic AI Era?
(Are you ready for Generative Engine Optimization & Answer Engine Optimization?)
Improve AI discoverability, optimize data listings for AI-powered search, and position datasets to be found, evaluated, and sourced by AI agents.
The way financial institutions discover and evaluate alternative data is changing.
As AI-powered search, large language models (LLMs), and autonomous AI agents become increasingly integrated into investment research and data sourcing workflows, alternative data providers face a new challenge: being discoverable not just by human buyers, but by the AI systems helping them find the right data.
Traditional sales and marketing strategies remain important, but providers must also consider how their datasets, company profiles, and product information are interpreted and surfaced by AI.
In an agentic world, discovery increasingly depends on whether AI systems can understand what a dataset contains, what questions it can answer, which use cases it supports, and how it fits a buyer’s specific investment or research objectives. A dataset may offer valuable signals, but if its documentation is incomplete, its attributes are unclear, or its potential applications are difficult for AI to interpret, it risks being overlooked.
AI discoverability requires more than having data available online; it requires making that data understandable, searchable, and relevant to the needs of both human decision-makers and AI agents.
For alternative data providers selling to hedge funds, asset managers, and other financial institutions, this shift creates both a challenge and an opportunity. By strengthening product descriptions, enriching metadata, publishing comprehensive data dictionaries, and clearly articulating use cases, providers can improve their chances of appearing in AI-assisted discovery and evaluation workflows.
This emerging discipline—often discussed alongside AI search optimization, Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO)—could become an increasingly important part of data provider marketing.
The question is no longer simply how to generate leads, but how to ensure your data is understood, recommended, and ultimately considered when an AI agent searches for the right solution.
How the Model Context Protocol (MCP) Is Changing AI Data Discovery
The Model Context Protocol (MCP) is an open-source standard, often described as the “USB-C for AI,” that enables large language models (LLMs) and AI agents to connect with external tools, databases, file systems, and APIs.
Rather than operating solely on information included in their training data or provided in a prompt, AI systems can use MCP to access relevant external resources and interact with connected data sources as part of a workflow.
For alternative data providers, MCP represents an important development in how datasets and data-related services could be discovered and accessed. As financial institutions build AI agents to support investment research, evaluate new datasets, and automate data sourcing workflows, standardized ways for AI systems to interact with external resources become increasingly valuable.
Providers that make their data, documentation, and capabilities accessible through well-defined interfaces may be better positioned to participate in these emerging workflows.
However, MCP connectivity alone does not guarantee discoverability. An AI agent still needs to understand what a provider offers, what the data measures, which markets or companies it covers, how frequently it updates, and which investment questions it can help answer. Clear product descriptions, structured metadata, comprehensive data dictionaries, and specific use cases remain essential. MCP can help enable access and interaction; high-quality, machine-readable information helps AI systems understand the value of what they are accessing.
For data providers, the takeaway is to prepare for both sides of agentic AI: being discoverable by AI systems and being accessible to them once discovered . As MCP and other interoperability standards evolve, providers that combine strong AI discoverability practices with appropriate technical integration may have an advantage in connecting their datasets with the next generation of AI-powered investment research tools.
Good Hygiene for GEO: Best Practices for Data Providers
Generative Engine Optimization (GEO) for data providers focuses on structuring, cleaning, and distributing data so that AI search engines (such as ChatGPT, Perplexity, Gemini, and Claude) can easily discover, parse, trust, and cite your datasets or API outputs.
Core Principles for Data Providers
Data Purity and Structure: Use rigorous schema markup, clean JSON/API structures, and transparent metadata tags so large language models (LLMs) can accurately interpret your datasets.
Chunk-Level Retrieval: Design your documentation and data tables in modular, bite-sized, and self-contained blocks (400–700 words or clear record snippets) so RAG (Retrieval-Augmented Generation) pipelines can extract exact answers.
Frontloaded Direct Answers: Place core data points, metric definitions, and summary tables immediately at the top of pages or documentation endpoints rather than burying them behind conversational filler.
Authoritative Citations & Provenance: AI engines favor sources that display clear timestamps, primary methodology, and high trustworthiness. Include explicit citations and version histories for your data feeds.
Open Crawl Accessibility: Ensure your robots.txt or site settings do not block AI scrapers and bot user-agents that feed models like Perplexity and OpenAI.
According to GEO Guide 2026: Generative Engine Optimization Explained, 47% of brands lack a GEO strategy and early adopters have a significant opportunity window before GEO becomes table stakes for digital marketing.
Digital marketing leader, Ahrefs, advises that succeeding with GEO requires mixing solid SEO fundamentals with tactics from other marketing disciplines. Marketing efforts include getting your brand mentioned across the web. For example, via press coverage from influential media outlets with high domain authority, getting mentioned in “best of” lists, reviews and case studies, as well as Reddit and social media platforms rooted in authentic user-centric feedback.
These sources of information help guide AI assistants to include your brand in their responses, whether they link back to you or not. It is a new form of word-of-mouth marketing—the more your name comes up in AI answers, the more likely people are to remember and consider your brand.
What BattleFin Offers to Help Data Providers Get Discovered in Agentic World
BattleFin is the definitive leader in connecting alternative data providers with data buyers in financial services via its global event series and technology for over a decade. The next generation of agentic sourcing is a natural extension of the company’s position in the market. Complementing the introduction of MCPs to the industry are BattleFin’s products including the Ensemble Data Catalog, Exabel Knowledge Graph and Alt Data Consensus platform.
Our Ensemble Plus is available to data providers and partners are promoted to over 7,000 buy side contacts in the Financial Services industry. By combining event exposure, automated trial workflows, and integration into BattleFin's distribution platform, Ensemble Plus streamlines dataset evaluation, shortens sales cycle and allows you to reach a much larger audience.
Learn more about how to work with BattleFin to make your dataset more discoverable by financial institutions by contacting info@battlefin.com.