At the end of Q4 2024, the leadership team at Protogen Corp faced a complex decision with serious operational and financial consequences. Expanding deployment of their experimental propulsion units required input from multiple departments, each working with different tools, metrics, and timelines. Finance had projections, Engineering had performance data, and HR tracked safety incidents. All the right pieces existed, but they were scattered and disconnected. That’s where Phlow stepped in. By aligning data across departments through shared context and timelines, Phlow helped decision-makers move from fragmented information to clear, confident action.

Context

At the end of Q4 2024, the executive team at Protogen Corp needed to make a major decision: whether to greenlight expanded deployment of their experimental propulsion units across new fleets.

The stakes were high, with implications across:

• Finance: cost overruns and revenue impact
• Engineering: performance metrics and failure rates
• HR: crew safety incidents and staffing concerns

But each department reported separately: The CFO had one view, Engineering had another, and HR tracked incidents in its own siloed tool.

“We had the pieces. But no picture.”

The Challenge

The decision required seeing connections:

• Were the technical failures responsible for the financial losses?
• Were safety incidents isolated, or tied to specific units?
• Were the risks worth the projected gains?

But pulling that insight together meant a week of chasing files, aligning timestamps, and decoding spreadsheets.

“You end up in meetings just trying to agree on what’s real. Not what to do about it.”

What Phlow Did

The team ran a single query: “Compare Q4 2024 financial forecasts with technical performance and safety incidents.”

Phlow immediately surfaced:

• Financial projections and actuals from the Finance team
• Engineering reports with Q4 failure logs
• Internal HR documents on crew safety reports
• All tied to Q4 2024, the shared time context

Instead of digging through folders or asking around, they had:

• A consolidated view of what mattered
• A timeline of incidents and costs
• AI-generated refining questions that prompted deeper thinking like:

“What additional data would clarify if underperformance correlates with safety incidents?”

Phlow didn’t just fetch documents. It connected dots.

Outcome

The executive team made the decision in two days, not two weeks.

• They identified a correlation between specific engine batches and rising maintenance costs
• Flagged overlooked safety concerns tied to field deployment
• Paused the expansion, saving millions in potential recall costs

“We didn’t just move faster. We moved smarter. That’s the real ROI.”

Why This Matters

Most tools give you more documents.

Phlow gives you answers with context, decisions with confidence, and connections that matter. In fast-moving industries, that’s the difference between being informed, and being ready.