# QuadSci Release 3.0: GTM Agents that know what's coming # Source: https://quadsci.ai/blog/quadsci-ai-3 # Format: RAG-optimized full article text with YAML frontmatter --- content_type: blog_post title: "QuadSci Release 3.0: GTM Agents that know what's coming, powered by the world's most predictive customer intelligence AI" url: https://quadsci.ai/blog/quadsci-ai-3 date_published: 2026-07-15 category: Product author: Dan Harmeson (cofounder and co-CEO) --- The AI wave was supposed to fix growth for B2B software. It hasn't. For most B2B software companies past $100M in ARR, AI hasn't moved the number that decides everything: net revenue retention. Agents made revenue teams faster, but speed hasn't shown up in retention or quota attainment, because none of those tools ever learned how customers actually use the product, or how that usage ties to revenue. In B2B software that gap is existential: 95% of revenue comes from existing customers, and a one-to-two point slip in gross retention can't be backfilled with new pipeline inside a fiscal year. The question leadership is asking is no longer "are we using AI?" but "which AI investment actually moved revenue?" QuadSci is the only AI that synthesizes how customers actually use the product (via product telemetry) with GTM actions, support patterns, and now the external events unfolding inside customers' businesses. It turns that behavioral truth into intelligence delivered conversationally in the QuadSci app, via MCP, or in the tools revenue teams already use, early enough to change the outcome. ## What's New Q-Chat is now fully conversational. Revenue teams ask direct natural-language questions inside QuadSci and receive telemetry-grounded answers. Insights are available inside Salesforce, Gainsight, Clari, Salesloft, Gong, and Slack, answering questions like: - Why is this customer trending toward expansion? - Which accounts are beginning to show risk? - What should the account team focus on this week? - What are the ideal expansion offers for customers not in pipeline? Every answer draws on the richest customer intelligence available to a software company. Q-Chat reasons across what customers do on the platform, what they have been saying about their experience, and the external forces shaping their decisions. It does not run on generic rules and it does not guess. ## Seeing the Full Customer Product telemetry tells you what customers do. It is objective, continuous, and free of the bias in every human interaction. But it does not tell you what customers think, what they expected when they bought, or what pressures outside your product are shaping how they decide. Growth AI 3.0 layers two additional signal sources on the behavioral foundation. The first is conversational intelligence: what customers said in past sales cycles, the expectations that were set, how their perception of value has evolved, and what they are saying now. Platforms like Gong, Clari Copilot, and Gainsight Staircase bring this layer into the system. The second is market signal: leadership changes, acquisitions, funding rounds, and financial pressure in the markets customers operate in. Telemetry is objective but silent on context; conversation is rich but filtered through what people choose to say; market signal explains forces neither layer sees alone. Together they produce a more complete and honest view than any single source. ## The World's Most Customer-Behavior-Aware Agents QuadSci launched the QuadSci MCP Server. Most revenue agents today run on the same thin 20% health scores always ran on: CRM fields, conversation logs, and siloed signals across GTM tooling. The QuadSci MCP Server exposes QuadSci's behavioral intelligence as a native, queryable layer inside any agentic workflow — not a report exported after the fact, but a predictive, telemetry-grounded signal available at the moment of decision. It works with Salesforce Agentforce, Microsoft Copilot, Bedrock, LangGraph, CrewAI, and custom agents on the OpenAI and Anthropic SDKs — any MCP-compatible stack. QuadSci is the only platform that can produce the signal base that makes agents accurate, prescriptive, and product-aware. ## From Reactive to Strategic Most revenue teams are not losing customers because they lack data. They are losing them because the signal arrives too late. By the time risk surfaces in a health score or CRM flag, the decision is often already forming on the customer's side. When you have a complete view of an account a year or more in advance, the engagement changes entirely — you are shaping a relationship with knowledge of what the customer is experiencing, what they believe about the value they are realizing, and what outside pressures they are navigating. For the people closest to customers, that means starting every week knowing which accounts matter most, why they matter, and what actions are most likely to change the outcome — a clear view of where to focus and what to do, not a dashboard to decipher. For leaders, it means an organizational view of revenue grounded in actual customer behavior, with the evidence to direct teams toward work that protects revenue and accelerates growth. The same intelligence that reveals churn risk reveals expansion opportunity. ## What This Makes Possible Most AI systems are reasoning from what teams have logged, what reps have entered, and what customers have chosen to say — a thin and noisy foundation for the decisions that determine whether a business grows. QuadSci's intelligence is grounded in the behavioral truth of how customers use software, enriched with what they've said and the external pressures they are under, delivered conversationally in the tools revenue teams already use with enough lead time to change outcomes. This past year the team shipped conversational Q-Chat, rebuilt the experience end to end, expanded the integration landscape, and completed SOC 2 compliance, all while pushing model accuracy further.