Case Study

Proof at a Glance

• Initial attendees: CTO and Product Lead
• First workshop: Agentic Product Management, Vancouver, July 2025
• Follow-on engagement: Company-wide workshop at Realtime’s office, focused on their stack and codebase
• Engineering team: ~8
• Time to first functional prototype: 2 weeks
• Previous estimate: ~6 months with a full team
• New AI hires: 0
• Business impact: New business opportunity unlocked

Before the Workshop

Realtime Networks had been experimenting with AI, but like many teams, their usage was fragmented.

People were trying tools. Some were using autocomplete. Some were testing snippets. Some had early success. Some had bad experiences. But there was no shared operating model for how AI should be used inside real product and engineering work.

More importantly, the company had already looked at one potential new business opportunity and effectively written it off. The opportunity was attractive, but the development effort required to pursue it was too large. The company did not believe it could justify the resources.

The issue was not lack of ideas.
The issue was lack of a practical delivery model.

The First Trigger

In July 2025, Realtime Networks sent its CTO and Product Lead to the Agentic Product Management workshop in Vancouver.

That first workshop changed how they saw the problem.

They immediately began applying what they were learning back at the office and quickly realized this was not just useful for two leaders. It had implications for how the broader company could build.

This was the turning point.

What they saw was not “AI as a feature.”

It was a different way of designing and delivering software.

Expanding to the Whole Company

After that initial experience, Realtime Networks hired 021:Events to conduct a company-wide workshop at their offices, focused on their actual stack, architecture, and codebases.

These workshops forced the team to move from thinking about “AI features” to designing agentic workflows with defined roles, control loops, and verification steps.

That shift mattered.

Instead of asking, “Where can we add AI?” the team started asking, “What work can now be done reliably, faster, and with less resourcing?”

This is where they stopped experimenting and started selecting what to build.

What They Identified

Using the frameworks from the workshop, Realtime identified two workflows that were strong candidates for agentic execution:

• High repetition
• Well-defined success conditions
• Low tolerance for error

Just as important, they revisited the business opportunity they had written off the week before.

Before the workshop, it looked impractical.

After the workshop, it looked achievable.

What Happened Next

The company moved quickly.

With only a couple of resources, Realtime built a first functional version in 2 weeks.

That same effort previously would have been viewed as roughly a 6-month project for a full team.

This was not a toy prototype. It was functional software tied to a real business opportunity.

And that business opportunity represented new business the company had not believed it could pursue.

Results and Outcomes

Realtime Networks unlocked a new line of business by changing how it approached delivery.

Key outcomes:

• Built in 2 weeks with a small number of people
• Comparable work had previously looked like a 6-month full-team effort
• No new AI engineers hired
• Shifted from fragmented AI experimentation to a shared company-wide approach
• Created confidence that previously rejected opportunities could now be pursued

Strategic Impact

The biggest win was not just speed.

It was strategic optionality.

Realtime Networks had a business opportunity on the table and had effectively decided not to pursue it because the resource burden was too high.

After the workshops, that changed.

021:Events did not simply help the team learn new tools.
It helped the company see that work they had ruled out was now economically and operationally possible.

That is a different category of impact.

What This Case Study Proves

This is not just a story about productivity.

It is a story about capability expansion.

Realtime Networks moved from:

• fragmented AI experimentation
• unclear delivery methods
• written-off opportunities

to:

• company-wide agentic thinking
• faster, structured execution
• new business made viable