Starbridge is a GTM platform that serves over 300 public-sector sales teams in the US. They’ve helped education and govtech companies drive pipeline, source hundreds of opportunities, and increase lead conversions.
Jordan Kindler, COO, and Daniel Goynatsky, Founding Customer Success, lead the team of 10 customer solutions architects (CSAs) who manage the full post-sales lifecycle — from onboarding to day-to-day support to renewals.
Fragmented tools and no visibility on account health
Starbridge’s CSAs were operating across a patchwork of tools with no central source of truth. They had recently adopted Salesforce as their CRM and used Scratchpad to manage customer data, while all customer communications were happening separately in Slack and email.
Handling Slack support became especially tricky, with more than 50% of customers using Slack to communicate with Starbridge’s team. “If you looked at the Slack workspace of any of our CSAs, your jaw would drop,” Jordan says. There was no triaging or routing, so CSAs had to manually keep up with unread messages and SLAs.
“If you looked at the Slack workspace of any of our CSAs, your jaw would drop. It’s just tons and tons of unread messages — very, very hard to stay on top of. There's no triaging or engagement, let alone an AI layer for CSAs to manage customer comms of any kind.”
Jordan Kindler, COO
Account health was equally opaque. Individual CSAs might sense when a customer was at risk, but there was no way to track or surface those signals at scale.
To patch the gaps, the team built their own AI workflows with a mix of n8n, Claude Cowork, and data from Slack, Salesforce, and Gong call recordings. They were able to generate AI account summaries this way, but felt like they were missing data points for the full customer 360.
One platform for the post-sales lifecycle
When Starbridge started evaluating different solutions, they weren't looking for a point tool. Jordan and Daniel wanted an AI-native platform that could serve their unique, all-in-one customer success function at every stage of the post-sales journey: onboarding, omnichannel support, relationship management, and customer success.
After considering tools like Zendesk and Vitally, Pylon stood out because the team could manage the full post-sales lifecycle from a single platform.
“We weren't looking for a support tool in isolation, or an onboarding tool in isolation, or a customer success management platform in isolation… It just seemed Pylon was the best of all worlds, being really spiky where we wanted it to be.”
Jordan Kindler, COO
Centralized issue triage across Slack and email
With Pylon, CSAs don’t have to juggle inboxes anymore. The team handles nearly 100% of customer Slack and email communications directly in Pylon, with kanban issue boards giving them a prioritized view of every conversation.
“The team is more efficient. Being able to see all your issues helps you visually prioritize what you want and need to spend time on.”
Daniel Goynatsky, Founding Customer Success
Unified account context for faster meeting prep
CSAs are now saving 10 hours per rep/week on meeting prep with account views and AI notebooks that pull customer history, recent conversations, and usage data into centralized views. The team can use AI to generate pre-written notes based on the type of conversation they’re preparing for — whether that’s a renewal, re-engagement, or something else.
For leaders like Jordan and Daniel, these unified views also make it easy to understand how any given account is performing without having to chase down individual CSAs.
“Being able to have a lot of data in a way that is very easy to manipulate and very easy to elicit the relevant information has been a complete game changer. We are able to monitor trends in usage, sentiment, and issue triage all in the same place.”
Daniel Goynatsky, Founding Customer Success
Optimizing pricing, renewals, and QBRs with AI
One of the most powerful tools the team has set up is an AI notebook for renewal pricing. Jordan and Daniel wrote a prompt detailing Starbridge’s pricing methodology and calculation framework, including references to custom account fields like sentiment or usage in Pylon.
Based on those inputs and each customer’s existing spend, AI makes tailored recommendations for pricing changes and renewal messaging. Daniel’s team can then use the AI reasoning feature to understand and validate those outputs.
“Pylon actually suggests pricing and messaging for renewal on a per-customer basis, based on our exact framework.”
Jordan Kindler, COO
CSAs take it a step further by connecting Pylon MCP to Claude. This allows them to automate quarterly executive reports that highlight trends, risks, and feedback based on a specific customer's renewal timeline. By creating Claude Code skills that pull usage data, support issues, and customer quotes from Pylon, they’ve eliminated 85% of manual pre-work that used to come with renewals.
“We have the tools to begin a conversation not at ground zero, not at ground 60, but like a ground 85. CSAs no longer need to chase down information from 5+ sources to have a successful connect with a champion, but can leverage one unified platform to communicate success.”
Daniel Goynatsky, Founding Customer Success
Now the team has more time for tactical work, like refining the content and materials for renewal conversations and executive reviews.
Results
Pylon ultimately gives Jordan and Daniel’s team the capacity to manage more customers than they could before. They went from CSAs handling around 40 accounts to now managing roughly 50-55 accounts per person.
Here’s the business impact:
- 20-25% increase in team capacity
- 85% of manual pre-renewal work eliminated with AI workflows
- 10 hours saved per CSA/week on meeting prep with centralized context and AI summaries in Account Intelligence
- 100% of customer Slack and email conversations managed in Pylon
- 2 separate AI workflow tools replaced with Pylon MCP and native AI workflows
For Jordan, the key is to centralize customer data and workflows as much as possible. “That just makes adoption, repeatability, standardization, and measurability across the team easier,” he says. And then? “Be as creative as you can in figuring out where you can apply AI to all of your different CS workflows. Maybe as a starting point before even thinking about what constraints you have within your current system.”