One morning in a Slack channel at Aterlo Networks, Travis, a developer, asked a question that would normally require an engineer with expertise in another area of the platform: "What was the reasoning behind reading from PubSub to load the message onto a Channel instead of processing directly from PubSub in the first place?"

The question was technical, time-sensitive, and exactly the kind that interrupts deep work. Within two minutes, Grapevine responded with a detailed explanation, complete with citations to six different code files and past Slack conversations.

Alexey, a senior engineer, replied with five words: "Yep, AI answer is good."

For Sagi, an Engineering Manager, it was a revelation. 

"I was watching the conversation and thinking 'no way it got that right.' When Alexey replied 'Yep, AI answer is good,' I realized we had something special. A senior engineer didn't need to interrupt their work, Travis got unblocked immediately, and I didn't have to coordinate anything. That's when I knew Grapevine would work."
Sagi, Engineering Manager

Before Grapevine: Buried Knowledge & Busy Engineers

Aterlo Networks is the company behind Preseem, a proactive growth, customer support, and network operations platform for regional Internet Service Providers (ISPs). The 41-person team operates almost entirely remotely, with headquarters in Waterloo, Ontario. 

Like many distributed companies, Aterlo's engineering team identified several pain points that were slowing them down:

• Knowledge buried in Slack. Important technical decisions lived in old threads that were nearly impossible to find months later. Slack became an unofficial knowledge base that wasn’t easy to search. 

• Busy engineers. Senior developers held critical knowledge and were constantly interrupted with questions.

• Isolated customer context. Insights from customer conversations were siloed within customer-facing teams, making them difficult for developers and product managers to access.

• Time-sensitive blockers. When engineers needed to troubleshoot, waiting hours for the right person to respond meant wasted time and lost momentum.

Recognizing the problem, Aterlo's CEO started experimenting. He manually ingested call transcripts from their meeting recording tool (Fellow) into an LLM (Claude) to create a chatbot that engineers could use to ask questions about customers.

It technically worked, but had a few limitations:

• It required manual work each time to update the Clause project context

• There was no easy way to export or integrate data from their tools

• It wasn’t where the team naturally asked questions (Slack) and required opening a separate app (Gemini)

When Sagi learned that Grapevine started as an internal tool at Gather (the virtual office platform his team had used for years), he decided to give it a try. As he explored what Grapevine could do, he realized it solved not only the customer context problem they'd been tackling, but multiple challenges at once. It could integrate with their entire tech stack and surface answers from everything right in Slack. 

Live in Minutes, Not Months

Grapevine setup was straightforward. Sagi configured the Slack integration and connected their data sources: GitHub, Jira, and Confluence. Within 30 minutes, he had a working Slackbot named ‘@Aterlo Networks AI` ready to answer questions.

The breakthrough came when Sagi enabled Proactive Responses in their main dev Slack channel. The next morning, Travis posted his PubSub architecture question. Nobody had tagged Grapevine for help, but within minutes, it responded with a detailed explanation and citations to six code files.

The team was skeptical at first; they'd seen plenty of AI tools that hallucinated or pretended to know things they didn't. What made Grapevine different was how it handled uncertainty:

1. Citations to actual sources. Every answer links to the specific code files, Slack messages, Jira tickets, or Confluence pages it pulled from. "We can get to the right source quicker if we click through the citations instead of manually searching," Sagi notes.

2. Confidence scoring. When Grapevine responds, it shows how confident it is in the answer. "If people see it's relatively low confidence, they're more likely to wait for the expert to chime in," Sagi explains. "But if it's high confidence, they're more likely to trust the answer. The transparency helps them decide how to act.”

3. Human validation. In those first few weeks, something important happened: senior engineers publicly validated Grapevine's answers in the threads. A developer would ask a question, Grapevine would respond, and a senior engineer would reply "Yes, that's right. I have nothing to add."

Trust compounded with each correct answer. 

How Grapevine Proved Its Value

1. Remembering Solutions From Past Work

Kyle, a developer at Aterlo Networks, was troubleshooting a deployment issue for the billing-info job. "I think we've solved this already, just having trouble remembering what needs to change," Kyle posted in Slack, describing the error message he was seeing.

Within seconds, Grapevine responded with the exact solution. It cited past Slack conversations and linked to working examples in their codebase, including the specific container image to use and the exact workflow configuration.

Kyle could immediately apply the fix and keep moving; no need to hunt through old Slack threads or interrupt someone who had solved this months ago.

"These troubleshooting situations add up," Sagi notes. "When someone can self-serve the answer and keep working instead of waiting for the right person to have time, that's real time saved."

2. The Post-Mortem That Wrote Itself

In November, Aterlo experienced a minor incident. Their typical process required an engineer to set aside 10-20 minutes after resolution to type up the post-mortem. Important work, but not interesting work. 

Sagi decided to try having Grapevine write the post-mortem based on one of their Confluence templates. Grapevine generated a complete post-mortem in minutes, referencing the Slack incident thread and related Jira tickets. 

“When Grapevine pulled the timeline from Slack, referenced the Jira tickets, and formatted everything correctly, I thought 'wow, this actually works.' My devs were thrilled to cross this off the to-do list without having to do it.”
Sagi, Engineering Manager

3. Faster Debugging with Proactive Responses

When Aterlo Networks migrated a repository from Bitbucket to GitHub, their deployment unexpectedly broke. The CTO, Scot, posted the error in Slack, and Grapevine proactively jumped into the thread.

Within seconds, it provided a diagnosis. Grapevine cited the specific line in the file causing the issue and suggested a fix, citing examples of how other repositories had handled similar migrations.

Sagi applied the suggested changes and the deployment succeeded on the next run.

"The best part is it happens automatically. Nobody had to remember to ask Grapevine. It saw someone was blocked and jumped in to help. Without Grapevine, we would have spent much more time debugging. Instead, we got the answer in less than two minutes.”
Sagi, Engineering Manager

4. Unblocking a Sales Engineer

Samuel, one of Aterlo's sales engineers, needed to understand how a specific feature in their product worked to answer a customer question. The problem? The staff developer who knew the answer was in back-to-back meetings.

Before Grapevine, Samuel would have posted in Slack and waited hours, delaying his customer response and losing deal momentum.

Instead, Samuel asked Grapevine directly. The AI analyzed the question against their documentation, past conversations, and code, then provided an answer with 85% confidence, citing the relevant sources.

The thread continued with some discussion, providing Samuel and the team with enough context to move the conversation forward while the developer was tied up. When the developer finally had time to jump in, they could build on what Grapevine had already provided rather than starting from scratch.

"Grapevine has proven its value by providing instant responses and improving efficiency as compared to searching for answers across the many systems and SaaS products we use. We couldn't have built this ourselves without dedicating a lot of engineering resources that are better spent on our product."
Dan, CEO of Aterlo Networks

Build vs Buy Retro

Looking back, the team is glad they chose to deploy Grapevine rather than maintaining the in-house build. According to Sagi, Grapevine unlocks: 

• Continuous improvements. They benefit from Grapevine's ongoing product development without dedicating their own engineering resources.

• Comprehensive integrations. Grapevine already connects to most of their primary tools (Slack, GitHub, Jira, and Confluence), while also continuing to build more integrations. 

• Speed to value. It took only minutes to go live with a full solution, compared to months of internal development.

"The math is simple: even a few hours of developer time costs more than Grapevine. Plus, we'd be maintaining it forever. This isn't our core product—we should be building tools for ISPs. Buy this one, build what makes you money."
Sagi, Engineering Manager

For a team trying to help regional ISPs deliver better service with limited resources, the metaphor is apt: sometimes the right solution is letting someone else handle the infrastructure so you can focus on what you do best.

Stop Hunting for Answers, Unblock Your Team

Like Aterlo Networks, you don't need to build and maintain your own RAG chatbot to unlock the knowledge trapped in your docs, code, and chat. Grapevine integrates with your existing stack and puts company context at your team's fingertips, right where you work.

See how Grapevine can help your team:

• Reduce engineering interruptions and context-switching

• Replace scattered wikis and knowledge bases

• Get answers in seconds, not hours

• Preserve knowledge automatically

• Unblock developers faster

Start your free trial in minutes or chat with our team to see Grapevine in action.