NeuralVerge NEW

NeuralVerge delivers a data layer for agentic AI, providing structured, cited real-world data from 300+ sources and the open web via a single API or MCP server.

It normalizes company intelligence, corporate registries, funding, marketplace listings, social profiles and enrichment into a consistent record format.

Lookups run at query time so responses reflect current source content and include source attribution for verification.

Agents receive structured rows rather than full pages, enabling automated writes to sheets, CRMs or messaging channels through existing integrations.

The platform offers on-demand web search and page-to-JSON extraction to turn arbitrary pages or documents into machine-readable records.

Results include standardized fields and source links to simplify filtering, matching and downstream workflows.

NeuralVerge integrates with MCP clients and common LLM connectors to centralize data access across multiple external sources.

Key Features

• Single API or MCP server delivering structured, cited data from multiple sources and the open web
• Normalization of company intelligence, registries, funding, marketplace listings and social profiles into a consistent record format with standardized fields
• Query-time lookups that fetch current source content and include source attribution
• Outputs structured rows (machine-readable) for automated writes to sheets, CRMs or messaging channels via integrations
• On-demand web search and page-to-JSON extraction to turn arbitrary pages or documents into machine-readable records

Use Cases

• Automate CRM enrichment by using NeuralVerge's single API to fetch real-time, source-cited company data and normalized corporate registry fields, enriching leads with verified contact details, risk attributes and standardized records without building or maintaining scrapers
• Convert web pages, filings and public records to machine-readable JSON with page-to-JSON extraction and source-attributed fields, enabling agentic AI workflows and RPA to perform audited decisions where every datum links back to its originating source
• Create a unified due-diligence and monitoring data layer by normalizing multi-source company records, correlating corporate registry IDs, monitoring changes in real time, and triggering alerts or pipeline actions for M&A, compliance and third-party risk management

Who is it for?

• Ai/ml engineers building agentic or autonomous agents
• Software developers integrating llms with real-world data
• Data engineers and platform teams standardizing external data
• Product managers at ai/saas companies building agentic features
• Automation and integrations engineers (crm/sheets/messaging)
• Sales, sdrs and gtm teams needing company enrichment
• Market intelligence and competitive research analysts
• Due diligence, corporate research and compliance teams
• Startups and teams building data-driven agentic workflows