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Your AI platform answers questions about companies. HG Insights tells it what technology those companies actually run.
HG Insights is a vetted vendor on SpringDB DataExchange for AI and LLM platforms. 30,000+ technology products tracked across millions of organizations , structured, proprietary, and not available through web scraping , ready to embed as contextual intelligence inside your AI product.
OpenAI existing direct customer , $393K ARR · Palantir and Scale AI identified as highest-probability targets · Glean OEM integration opportunity scoped
AI platforms that answer questions about companies need structured, proprietary data. Web scraping doesn't get you there.
When a sales rep asks your AI platform "what technology does this company run?" or "are they currently investing in security tools?", the answer needs to come from somewhere reliable. Public websites, LinkedIn, and scraped sources give you fragments. HG Insights gives you a structured, maintained, proprietary dataset covering 30,000+ products across millions of organizations.
That's exactly what AI products need as a contextual data layer: structured schema, regular updates, comprehensive coverage, and provenance that stands up to enterprise scrutiny.
SpringDB DataExchange features HG Insights in this segment because the data quality is right for AI use cases and the integration model is already proven with at least one major AI company running it today.
The way HG data fits your AI product depends on what your product does.
Contextual data inside your AI product
HG data becomes a licensed knowledge source embedded in your platform. When a user queries your AI about a company , its technology stack, IT investment, vendor relationships , HG data provides the answer. The data is contextual: it informs responses without being redistributed as a raw feed.
Fits: Palantir Foundry, Glean enterprise search, Perplexity Enterprise Pro, any AI platform where company intelligence is a core output.
Structured data for retrieval-augmented generation
HG data is ingested into your RAG pipeline as a structured retrieval source. When your LLM needs to ground a response about a company's technology environment, HG records are retrieved and surfaced as context. Clean schema, regular updates, and consistent entity linking make it retrieval-ready without heavy preprocessing.
Fits: Cohere enterprise fine-tuning, Scale AI data platforms, any LLM workflow requiring structured business context.
On model training
HG data is available for contextual intelligence and RAG retrieval. Foundation model training use cases require separate legal structuring around usage rights and derivative outputs. SpringDB can help navigate this , it's part of what we do when we structure these deals.
AI answers are only as good as the data they retrieve. Here's what makes HG data right for this use case.
Proprietary, not scrapeable
HG data cannot be reproduced by scraping public websites. It's collected through proprietary methods, which means your AI product has access to information competitors can't easily replicate.
Structured schema
Technology installations, IT spend, contract timelines, and firmographics all arrive in a consistent, clean schema. No unstructured text to parse. Retrieval pipelines can query specific fields directly.
Regularly maintained
HG data is continuously updated , not a static export. Technology installations change, contracts expire, IT budgets shift. Your AI product answers with current context, not stale records.
Enterprise-wide coverage
14,000+ technology products tracked across millions of organizations globally. Broad enough to be useful in enterprise AI workflows where users ask about any company, not just a curated subset.
Clear data provenance
Enterprise buyers expect to know where AI answers come from. HG's data provenance is documented and defensible , a requirement for AI products selling into regulated enterprise environments.
Signals that matter for AI sales workflows
Contract timing, IT spend trajectory, and technology displacement signals are exactly what AI-powered sales and competitive intelligence tools need to be genuinely useful, not just impressive demos.
Five AI platforms where HG data adds immediate intelligence value.
Already using HG data for competitive intelligence on AI adoption across enterprises. Separate OEM partner opportunity under evaluation.
Foundry users analyzing competitive landscapes could access HG technology adoption intelligence on any company. OEM embed inside Foundry analytics workflows.
Data platform for AI companies needing structured proprietary datasets. HG data powers new context-aware AI products built on Scale's platform.
Enterprise AI search across 2,000+ companies. When a sales rep asks Glean about a prospect's technology, HG data provides the answer from inside the knowledge graph.
Enterprise Pro queries about technology adoption ("Which healthcare systems run Epic?") grounded in HG data rather than general web retrieval.
OpenAI already uses HG Insights data today. The pattern is proven.
OpenAI is a $393K ARR direct customer of HG Insights, using technographic data for competitive intelligence , tracking AI adoption across enterprise accounts. A separate OEM partner opportunity for embedded AI experiences is under evaluation.
The questions AI platforms can answer with HG data embedded:
These are questions your enterprise users are already asking your AI. HG data gives you real answers instead of hallucinated ones.
Partner details confidential. Happy to walk through specifics on a call.
Proprietary structured data that makes your AI answers accurate instead of approximate.
AI products that answer enterprise questions about companies are only as credible as their data sources. HG's structured, maintained, proprietary dataset is the difference between an AI that knows and an AI that guesses.
Grounded AI answers
Enterprise users lose trust in AI tools that hallucinate company facts. HG data grounds technology intelligence answers in verified, structured records.
Differentiated product capability
Technology intelligence is a capability competitors cannot quickly replicate. Your AI platform answers questions no other product can answer reliably at scale.
Clean licensing path
SpringDB helps structure the licensing agreement for contextual and RAG use cases with usage rights clearly defined. The legal complexity is manageable with the right structure in place from day one.
AI data licensing is a new deal type. We help structure it so both sides know exactly what they're signing.
AI data licensing requires explicit terms that most standard data agreements don't cover: contextual use versus model training, derivative output rights, per-query pricing versus flat licensing. SpringDB DataExchange has been involved in structuring these conversations and knows where the ambiguity lives.
We map HG data to your specific AI architecture, help define the usage boundaries, and work alongside your team through the commercial and technical setup.
Architecture mapping
We identify which integration model fits your product: OEM embed, RAG retrieval, or contextual API access.
Usage rights structuring
We help define contextual use versus training data terms, derivative output rights, and pricing model before any agreement is signed.
Integration support
API, Snowflake, or Databricks. We support the technical setup alongside your data engineering team.
Live and grounded
Your AI answers technology questions with verified data. Your enterprise users trust the answers they get.
Vetted vendorTechnology intelligence for AI and LLM platforms. 30,000+ products. Structured, proprietary, regularly maintained. OEM embed and RAG retrieval models supported.
Let's talk
If your AI platform answers questions about companies, this conversation is worth 20 minutes.
We're talking to product, data, and partnerships leaders at AI and LLM platforms. If your enterprise users ask about company technology and you want grounded answers instead of approximations, let's get on a call.