- Not every platform marketed as AI-native was built that way from the start
- The search layer is where the difference between genuine AI architecture and a retrofitted interface becomes immediately measurable
- AI-native platforms surface companies based on what they do, closing the gap that keyword search leaves open
- Platforms with agentic search capabilities build a picture of a company from its full digital presence
NEW YORK, NY, April 09, 2026 (GLOBE NEWSWIRE) -- As of April 2026, investment professionals evaluating platforms are asking valid questions about what "AI-powered" means beyond the product page. Rather than believing sweeping claims, firms are realizing the underlying AI architecture determines how useful a platform actually is. Grata, the leading private market intelligence platform, was built on AI from inception, and its search capabilities yield results that are structurally different from those of a repurposed database.
Nevin Raj, General Manager at Grata, identifies two categories of AI that fall short in practice. The first is legacy platforms that inject a chatbot layer on top of existing data, an approach that adds interface complexity without changing what the underlying search actually returns. The second is LLM wrappers marketed as AI-native, where outputs are generated without supervision and quality control.
"The quality is low but seems plausible at first glance," Raj says. "Customers must closely examine the quality of the information and rigorously scrutinize its sourcing and quality control processes. Look for companies that are truly AI-native. This means they're built on a scalable foundation of AI annotated by humans and quality-controlled to provide information that is accurate and trustworthy. These AI-native companies also have exclusive, proprietary data that others do not have access to. They produce features for their customers that actually automate verticalized workflows without adding another chatbot that is rarely used."
KEY FACTS:
- Grata covers 21M+ private companies with investment-grade data
- Grata maintains 99% data accuracy, verified by an independent internal audit team on a daily basis
- Grata has 4,000+ curated published company lists available for direct deal sourcing
- Grata's platform includes 8M+ verified executive contacts
- Agentic Search from Grata considers a company's full website and digital footprint
- Grata's Similar Company Search surfaces look-alike companies by business attributes instead of keyword matches
AI is Dramatically Improving Precision at the Search Layer
Many deal teams underestimate how much of their sourcing workflow is spent on search. A large number of queries across industries, geographies, revenue ranges, and ownership types add up quickly, and every relevant company that fails to surface on the search layer misses out on potentially lucrative opportunities. Legacy databases don't help to speed up the process, typically indexing companies based on short descriptions and classifying them using broad industry codes that don't capture what a business does at the micro level.
On the other hand, AI-native platforms acknowledge what a user is looking for and find companies that match that description, regardless of the specific words those companies use to describe themselves. The practical effect is a curated list of more relevant targets, often including new companies competitors have yet to discover.
Three Categories of AI in Deal Intelligence Today
PE firms evaluating private market intelligence platforms in 2026 are navigating a vendor landscape that is sorted into three categories:
- Legacy platforms with an AI layer: Inject a conversational interface onto existing database infrastructure. The underlying data model and search architecture remain unchanged, which means the results are largely the same regardless of how the question gets asked.
- General-purpose LLM wrappers: These wrappers apply a large language model to existing data and then label it as 'AI-native.' Search quality is constrained by data that was not built for private markets.
- Purpose-built AI-native platforms: These are built on a scalable AI foundation and annotated by humans. The features they produce automate real workflows rather than adding another interface layer. The difference shows up in search results and proprietary data that third parties can’t replicate.
What Investment Professionals Should Test in Any Platform
A platform with AI powering its search engine produces fundamentally different results than one using AI to run a Q&A assistant on top of a traditional database.
The test that reveals the difference is the search for a specific niche. Pick a sub-industry or a business model that's difficult to describe in a few words. For example, a company that does precision testing for medical device components, or a firm that provides outsourced HR compliance services to mid-market manufacturers. Run those searches on multiple platforms to pull the results. The platform with real AI-native search will deliver a longer, more relevant list of targets than the keyword-based platform, which may only find a few companies that have used the right words.
A second test is the look-alike search. Start with a company you know well, for example, a prior acquisition or a competitor. Ask the platform to find similar businesses. On a keyword platform, the results will cluster around companies with similar descriptions. On an AI-native platform like Grata, the results will include companies that operate the same way but might describe themselves completely differently. This is the category that builds proprietary deal flow.
Where AI-Native Deal Intelligence Is Heading
The current generation of AI-native platforms has moved beyond the search problem. The infrastructure now exists to support continuous market surveillance, tracking ownership changes, monitoring conference activity, generating look-alike lists, and surfacing acquisition signals before deal teams have run a single search query.
The next operational shift is agentic automation at the workflow level. A sourcing professional will describe an acquisition thesis in natural language, and the platform will act on it, monitoring relevant markets, filtering for criteria, and delivering a refined set of targets without manual input.
Platforms built on scalable AI infrastructure from the start are moving toward a workflow where deal intelligence operates continuously in the background, and deal teams engage with outputs rather than processes.
FAQ
Q: How does AI search compare to traditional keyword-based platforms in private market intelligence?
A: Traditional keyword search brings up companies whose descriptions match the exact terms a deal team enters. AI-native search finds companies based on what they do by matching search intent with how target companies represent themselves across their full digital presence. The result is a more complete and accurate set of targets, which includes companies that might not have come up in a keyword search.
Q: Which market intelligence platform has the strongest AI capabilities?
A: The strongest AI capabilities belong to platforms built on AI from the very beginning. The distinction is evident in the search layer that determines if AI powers company discovery at scale or just adds a conversational interface on top of regular results. Platforms that gather company characteristics from digital signals cover a materially different set of companies.
Q: What is the difference between human-annotated AI and a fully automated AI system in private market intelligence?
A: Human-annotated AI means that model outputs are reviewed and corrected by domain experts at the point of training, not just at the point of delivery. This matters in private market intelligence because the universe of private companies is too large and too varied for automated systems to self-correct reliably. Platforms built this way produce results with higher precision than fully automated systems, which are prone to plausible-sounding errors that are difficult to detect without domain knowledge.
Q: How does proprietary data factor into the quality of AI-native search results?
A: AI search quality is bound by the data it operates on. A platform applying AI to data that was built for public markets will return results shaped by public market coverage gaps, skewing toward funded companies, announced transactions, and businesses with institutional track records. Platforms with proprietary private company data not available to third parties create a materially different search universe, including companies that do not appear in shared datasets at all. This is where the differentiation between AI architecture and AI-branded architecture becomes concrete.
About Grata
Grata is the leading private market intelligence platform that unifies investment-grade data, an active network, and agentic AI so dealmakers can source smarter, screen faster, and build conviction sooner—all in one platform. Grata provides comprehensive data on 21M+ private companies with 99% accuracy, serving over 1,000 global customers, including 35 of the top 40 PE firms and 9 of the top 10 management consulting firms. With offices in New York, London, Paris, Frankfurt, and Sydney, Grata enables investment professionals to navigate private markets with greater speed and confidence. For more information, visit grata.com.
