“We had AI everywhere and intelligence nowhere. Every team was running their own tools, their own prompts, their own workarounds. The output was inconsistent and nobody could tell you what any of it cost.”
Panopto Product Team
Panopto
Challenge
The more AI tools Panopto adopted, the harder it became to use any of them well.
Enterprise AI adoption rarely fails because organizations choose the wrong tools. It fails because organizations choose too many of them, each operating independently, none of them aware of the business they are supposed to serve. That was exactly the situation facing Panopto's internal teams.
Sales was spending hours each week researching individual prospects. That process meant opening a browser, pulling from LinkedIn, cross-referencing company websites, searching for recent news, and then attempting to synthesize it all into something useful before a call. The process was not just slow. It was inconsistent. Two sales reps approaching the same prospect would arrive at different levels of preparation and different outreach strategies, because there was no system enforcing a standard.
Marketing faced a different version of the same problem. Content creation depended on the judgment and availability of whoever was writing that week. There was no templated structure, no defined output format, no way to guarantee that a blog produced on a Monday would match the quality or positioning of one produced on a Friday. The writing happened, but the strategy behind it was thin.
Underneath both of those problems sat a shared infrastructure failure: none of the AI tools in use carried any knowledge of Panopto as a business. They were generic. A prompt sent to a generic model produced a generic output, and a generic output required a human to close the gap between what the tool returned and what the business actually needed. The teams were not getting bad results from AI. They were getting results that required too much human correction to be worth the time.
We weren't getting bad results from AI. We were getting results that required too much human correction to be worth the time. The tools didn't know us, so we had to re-explain ourselves every single time.
Solution
Cogent Labs replaced a collection of disconnected tools with a single AI platform built around how Panopto actually operates.
The Panopto AI Hub is a centralized agent platform that gives every internal team access to purpose-built AI workflows from one dashboard. Instead of opening five different tools, each requiring its own context and its own prompts, Panopto employees open one interface and select the agent designed for their task.
The first agent Cogent Labs built was the AI SDR. Sales reps submit a LinkedIn URL and the agent does the rest. It pulls data from LinkedIn, the company website, Google News, and academic sources, then combines that information with Panopto's internal knowledge base to produce a complete intelligence report. That report includes a prospect's background, their company context, insights into their communication style, recommended outreach strategies, drafted emails, and prepared objection handling. The entire output arrives in approximately two minutes. The same research process that previously consumed hours now takes less time than a coffee break.
The second agent addressed marketing's content problem. The Content Writer agent uses pre-defined templates covering trend lists, problem-solution frameworks, step-by-step guides, and trend analysis pieces. Marketing selects the format, sets the word count, and the agent produces SEO-optimized content in real time, complete with auto-generated titles. Consistency is no longer a function of who happened to be available. It is a function of the platform.
What makes both agents meaningfully different from generic AI tools is the knowledge base integration sitting behind them. Cogent Labs built the platform so that every agent operates with Panopto's business context already loaded. The outputs are not generic responses shaped by a prompt. They are responses shaped by an understanding of what Panopto does, who they sell to, and how they communicate. The platform also introduced something Panopto had never had with their scattered toolset: cost visibility. Every task runs through a credit tracking system that shows usage per user, cost per task, and aggregate consumption across the organization. Administrators can monitor and control spend in real time, which means AI is no longer an unaccounted cost buried in a software subscription.
For the first time, we could see what AI was actually costing us per task. That visibility alone changed how our teams thought about which workflows were worth automating.
Results
Panopto's teams now execute in minutes what used to take hours, at a cost that makes every task worth running.
The most immediate impact showed up in sales. A prospect intelligence report that previously required an hour of manual research now takes two minutes and costs less than a dollar. Sales reps arrive at conversations better prepared, with consistent information structured the same way every time. That consistency compounds. Better preparation leads to more relevant outreach, and more relevant outreach leads to higher conversion rates. The improvement is not just in speed; it runs through the entire sales motion.
Marketing gains a different kind of leverage. The Content Writer agent removed the ceiling on output that had always been set by writing capacity. The team can now produce structured, on-brand blog content at a pace that manual writing could never match, without sacrificing the strategic consistency that makes content valuable.
The broader gain is organizational. Panopto entered the project with AI everywhere and no way to govern it. They leave with a platform that centralizes agents, enforces structured workflows, tracks every task against a cost, and is architected to expand as new agents are added. What began as a fix for two broken workflows is now the foundation for how Panopto runs AI across the business.
2 min
to generate a full sales intelligence report
$0.70
maximum cost per AI-generated prospect report
Zero
scattered tools — one unified agent platform
Our sales team used to walk into calls hoping they'd done enough research. Now they walk in knowing they have. That shift in confidence is real, and it shows up in how they perform.
Conclusion
The cost of fragmented AI is not just inefficiency. It is the absence of intelligence itself.
Panopto had tools. What they did not have was a system that could turn those tools into a coherent operational layer. Each tool ran in isolation, unaware of the company it was supposed to serve, producing outputs that required constant human correction to become useful. The platform Cogent Labs built did not simply consolidate that toolset. It replaced the underlying model entirely.
There is a pattern that shows up consistently in enterprise AI adoption: organizations accumulate tools faster than they develop the infrastructure to govern them. The result is not more capability. It is more noise. The teams that break out of that pattern are the ones that stop asking which AI tool is best and start asking how AI should fit into how their business actually works.
That question has a structural answer. When agents are built around business context, deployed through a governed platform, and tracked against real costs, AI stops being a tool that employees use and becomes a system that the organization runs. The difference between those two things is the difference between scattered productivity gains and compounding operational advantage.
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