Tool LaunchHG Insights

HG Insights Launches Revenue Growth Intelligence Platform with Agentic AI

HG Insights unveils the RGI Platform unifying technographic, intent, and spend data with three AI copilots and an agentic Agent Builder for GTM teams.

6 min readPublished 2026-04-20

What happened

On March 24, 2026, HG Insights announced the launch of its Revenue Growth Intelligence (RGI) Platform — described as the industry's first unified platform connecting technographic, buyer intent, IT spend, buying center, and contact intelligence in a single AI-driven experience. Alongside the platform, HG Insights introduced the RGI Agent Builder, an agentic infrastructure layer that enables enterprise GTM teams to build custom AI agents powered by HG Insights' proprietary data.

The RGI Platform is built on what HG Insights calls the "Revenue Growth Intelligence Fabric" — a data layer constructed from nearly 50 petabytes of aggregated and curated third-party information collected over 15 years and refined into a domain-specific model for go-to-market intelligence. The Fabric delivers firmographic, technographic, spend, competitive, and buyer intent data from billions of data points, designed to power precise, granular GTM analysis across CRMs, data management platforms, and AI copilots.

The platform ships with three AI copilots, each targeting a distinct GTM function. Market Analyzer helps organizations identify and size market opportunities, giving CMOs, RevOps, and strategy leaders insight into which segments to prioritize and where competitors are gaining or losing ground. Sales Copilot puts intelligence in front of sellers at the point of decision, automating account research, triggering sales plays, and shortening sales cycles. Data Studio enables marketing and RevOps teams to score and prioritize leads and accounts by combining internal data with HG's Fabric data and applying predictive models.

Why it matters for practitioners

HG Insights' RGI Platform represents a significant shift in how market intelligence is delivered and consumed by GTM organizations. Rather than positioning as a standalone data provider, HG Insights is now offering an intelligence operating system — a platform where data, copilots, and agentic workflows converge to close the gap between insight and execution.

1. The Agent Builder introduces agentic AI to market intelligence workflows. The RGI Agent Builder, now available in early preview, uses Model Context Protocol (MCP) tools and pre-built agents to enable organizations to build custom AI-agentic meshes across their GTM stack. This connects HG's intelligence, agents, and copilots to platforms such as Microsoft Copilot, Salesforce Agentforce, and OpenAI. For CI practitioners exploring how to automate competitive intelligence, the Agent Builder represents a concrete example of how agentic AI can be applied to intelligence workflows — not as a theoretical capability but as a production-ready infrastructure layer that connects to existing GTM systems.

2. The convergence of technographic and intent data changes the competitive landscape. By unifying technographic intelligence (what technologies a company uses), intent data (what they're actively researching), IT spend data (what they're investing), and buying center intelligence (who makes the decisions) into a single platform, HG Insights is challenging competitors that offer these data types as separate products. For CI teams evaluating data providers, the RGI Platform raises a practical question: is it more effective to assemble a best-of-breed stack of specialized data providers, or to consolidate around a platform that contextualizes multiple data types in a single experience?

3. Sales Copilot moves intelligence into the deal flow. The Sales Copilot's focus on automating account research and triggering sales plays directly aligns with the deal intelligence use case that revenue organizations are increasingly prioritizing. Rather than requiring sellers to navigate a separate intelligence platform, Sales Copilot surfaces contextual intelligence within the tools sellers already use. For CI practitioners, this pattern — intelligence delivered in the flow of work rather than through standalone research — reinforces the need to design CI deliverables (battlecards, competitive alerts, win/loss insights) for consumption at the point of decision.

4. MCP integration signals a broader platform interoperability trend. HG Insights' adoption of Model Context Protocol for its Agent Builder reflects a growing consensus in the GTM intelligence market: AI agents need standardized ways to connect to data sources. MCP enables HG's intelligence to be consumed by agents running in other platforms — Salesforce, Microsoft, OpenAI — without requiring custom integrations. For CI teams that are building or evaluating AI-augmented intelligence workflows, MCP compatibility is becoming a selection criterion that determines whether a data source can participate in an agentic architecture or remains siloed.

Key details

  • Announcement date: March 24, 2026
  • Platform: Revenue Growth Intelligence (RGI) Platform
  • Data foundation: RGI Fabric — nearly 50 petabytes of data collected over 15 years
  • Data types unified: Technographic, buyer intent, IT spend, buying center, and contact intelligence
  • AI copilots:
- Market Analyzer — market sizing, segmentation, and competitive positioning for CMOs, RevOps, and strategy leaders - Sales Copilot — automated account research, sales play triggers, and cycle acceleration for sellers - Data Studio — lead and account scoring with explainable predictive models for marketing and RevOps
  • Agentic infrastructure: RGI Agent Builder (early preview) — custom AI agent creation using MCP tools and pre-built agents
  • Platform integrations: Microsoft Copilot, Salesforce Agentforce, OpenAI
  • Customer base: Serves 75% of tech companies in the Fortune 100

Market implications

HG Insights' RGI Platform launch reflects a broader market pattern: intelligence vendors are evolving from data providers into platform companies that embed intelligence directly into GTM workflows. This is the same trajectory that Gong, ZoomInfo, and 6sense have pursued — each expanding from a core data capability (conversation intelligence, contact data, intent signals) into a broader platform that integrates AI, automation, and workflow tools.

For the market intelligence category specifically, HG Insights' platform approach raises the competitive stakes. Vendors that continue to sell data feeds or static dashboards will face increasing pressure from platforms that contextualize data, deliver it through copilots, and enable agentic workflows. The 50-petabyte data foundation and 15-year collection history give HG Insights a defensible position in the technographic intelligence subcategory, but the platform play is a bet that data depth alone is no longer sufficient — buyers want intelligence that acts, not just informs.

The RGI Agent Builder's early preview status is worth monitoring. If enterprise GTM teams adopt MCP-based agent architectures at scale, the competitive dynamics of the market intelligence market could shift significantly. Vendors whose data can be consumed by autonomous agents will be embedded deeper into customer workflows than vendors who require manual interaction. For CI practitioners, this is an early signal to evaluate: which intelligence vendors in your stack are building for an agentic future, and which are still designing for human-only consumption?

HG Insights' emphasis on explainable predictive models in Data Studio is also notable. As the broader market grapples with AI trust concerns — a theme echoed by Gong's recent research on enterprise AI trust barriers — explainability is becoming a differentiator. CI teams evaluating market intelligence platforms should assess whether the AI-driven outputs they consume (lead scores, account priorities, competitive signals) come with sufficient transparency to build internal trust and stakeholder buy-in.

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