How Handle grew ACV by 70% with real-time strategic account visibility

For two years, Handle's CEO had the same standing request:

Patrick wanted to pull up any strategic account and see the full picture. Engagement history, stakeholder map, competitive positioning, renewal risks. No chasing down three different team leads for three different versions of the truth.

The request was simple. Fulfilling it was not. Handle had plenty of data: call transcripts in Chorus, notes scattered across Salesforce, competitive intel in various Google Docs, stakeholder information buried in email threads. The problem wasn't access. It was synthesis. Making sense of fragmented information at the moment of decision.

When Handle could finally operationalize that visibility, the impact showed up where it mattered most. Strategic account cycles that previously ran 12 to 18 months moved closer to 6 to 9 months, because the team could align faster on what was true, what was missing, and what to do next. And because research became dramatically faster and more accurate, Handle increased ACV by ~70% and improved win rate by 9%.

Ryan Vanshur, who leads Handle's GTM enablement systems, had experimented with AI tools to solve pieces of this puzzle. Automated transcript extraction. Account research frameworks. Custom dashboards. Each worked in isolation. None could deliver what Patrick actually needed: a unified superintelligence layer that connected everything Handle already knew and made it accessible on demand.

Building interactive internal tools with Endgame's MCP server

Handle's first use of Endgame was intentionally simple: standardize how reps build account context.

Ryan rolled Endgame out with a five template research sequence that every rep ran on target accounts. Outputs were published and reviewed. Quality became consistent. A new BDR could produce the same caliber of account intelligence as a tenured enterprise rep because the institutional knowledge lived in the system, not in someone's head. That alone was a meaningful step forward. But it was still mostly a workflow. Reps ran the sequence, got better context, and used it in their day to day.

1. The Pipeline Dashboard

The real unlock came once Ryan started building interactive tools for his sales team on Endgame's MCP server. The first was a pipeline overview dashboard that pulled in contextual details on each account -- beyond the standard report that only showed numbers.

2. The Strategic Dashboard

The second was a strategic overview dashboard that showed the status of every target whale account they were targeting.

3. The Single Account Dashboard

The third was a single account overview once you clicked into any whale account.

MCP made Endgame's intelligence layer programmatically accessible inside his workflows, which meant he could build tools on top of the same account context instead of keeping it trapped in templates or chat threads.

The executive dashboards once dreamt by Handle's CEO requested now exist. Anyone can pull up a whale account and see dynamically updated intelligence, synthesized from Salesforce, transcripts, Slack, and web research, with Endgame as the connective layer. This isn't a rep typing a question into a chat interface. It's Endgame functioning as the intelligence engine that powers Handle's interactive tools to drive executive decisions.

From sales visibility to intelligence powering product, marketing, and customer success

Handle's CEO got the visibility he asked for. But once the intelligence layer was in place, Endgame evolved from a sales tool to org-wide intelligence infrastructure.

Customer success used the same dashboards to spot risk before it escalated. In one account review, the system surfaced issues that had only been partially tracked: a key executive had moved roles, creating a sponsorship gap, and the account was nearing volume caps that would force a pricing conversation. Instead of finding out too late, the team flagged it early enough to act.

Product and engineering gained customer context they had never had access to before. Ryan built persona-based interfaces for won accounts so anyone could ask account specific questions grounded in real deal history. Engineers pulled enhancement requests mentioned during sales calls. Product managers surfaced gaps customers experienced. When Patrick saw it, he asked the team to present the capability at the company's all-hands meeting.

Marketing began using the same intelligence artifacts to sharpen ABM targeting and messaging. In leadership discussions about wins and improvements to customer experience, Endgame came up repeatedly across teams. Different use cases, same underlying engine.

What Ryan built isn't just a better research process. It's infrastructure that compounds. Every new application leverages the same intelligence layer, and every integration makes the whole system smarter.