Scalestack Discussions

Scalestack uses GenAI and automation to help companies know who to target when and why, at scale, and to activate the right GTM motion depending on the target. We orchestrate, at the org level (e.g. working with RevOps, Sales Ops or Marketing Ops teams), all the GTM data across systems, data sources and tools, natively. We only work for enterprises (e.g. SOC2 compliant and can handle any scale), and we enrich the CRM with highly customisable data that matters to each customer, with AI agents that reason and filter the sources behind the data. There are two big use cases which are typically the entry points for our customers: 1) Account profiling, TAM calculation, accounts insights, at scale. We help companies like MongoDB and Remote make sense, at scale, of hundreds of thousands of target accounts so that sales managers can dynamically distribute the book of business across reps and territories based on all the key attributes that relate to each customer's ICP. Our goal is to provide reps with better information to prospect into their accounts (reduced time for research) and an increased focus (right accounts at the right time) 2) Lead prioritization, de-anonymization and buying group enrichment at scale. Our AI-powered lead workflows orchestrate data sources and our AI agents to properly enrich leads even if they are coming with personal emails. Because of the mix of data orchestration and AI agents, Scalestack's average enrichment is industry-defining. Once we de-anonymize, enrich and prioritize, it’s possible for us to make sense of the data - for instance detecting teams or buying groups (e.g. relationships between the leads) which is a huge signal. This is a game changer to increase collaboration between marketing and sales (MQL --> SQL). When users leave Scalestack reviews, G2 also collects common questions about the day-to-day use of Scalestack. These questions are then answered by our community of 850k professionals. Submit your question below and join in on the G2 Discussion.