We build AI when the math says it should exist.
Plarix sits between a process that costs too much and an AI system that's actually worth building. We measure the baseline, build a system scoped to one process, and report exactly what changed in money and hours.
The ROI gap
Most companies can build an AI pilot. Almost none of them can say, in euros and hours, what it changed. A demo gets applause in a meeting. It rarely gets a number in a budget review.
That's the ROI gap: the space between what a company built and what it can prove. MIT's 2025 review of enterprise AI found that 95% of generative AI pilots produced no measurable financial return. The problem was rarely the model. It was that nobody measured the baseline, scoped the build to a real process, or checked the result afterward.
Plarix closes that gap by doing the measurement first. Every build starts with a baseline in money and hours, not a demo.
BEFORE PLARIX
process: "invoice matching"
baseline: unknown
pilot.build()
→ result: "looks promising"
→ savings: unmeasured
WITH PLARIX
process: "invoice matching"
baseline: €300,000/yr · 8,000 hrs
eeas.build(process, baseline)
→ result: €120,000/yr · 2,500 hrs
→ savings: €180,000 + 5,500 hrs, logged
Example — illustrative calculation
How an EEAS gets built
Every engagement moves through four stages. Skipping one is how AI pilots turn into demos nobody can price.
Diagnose
We measure how the process runs today: steps, time per step, cost per step, volume, and error rate. This is the number everything else gets compared against. Skip this step and there's nothing to measure the result against later.
Design
Before any build starts, we model what the system would cost to build and run against the baseline we just measured. If the number doesn't clear, we say so. This is the stage most AI projects skip entirely.
Deploy
The agentic system gets built and integrated into your existing tools, scoped to the exact process measured in stage one. Not a general assistant. A system built for one job.
Measure
Once live, results get compared against the original baseline on a set schedule: money saved, hours returned. If the number doesn't hold up in production, that gets reported too.
What Plarix builds
The offering has four parts. The free scan is the entry point. The build is the core. The scorecard is what makes the result defensible. The platform is where this is going.
Process Scan
Free · No obligationA short, no-cost review of one process: current cost, current hours, and an estimate of what an EEAS could realistically save. You get a number before you commit to anything.
EEAS Build
EngagementThe core offering. We design and build the Economically-Engineered Agentic System around one measured process, integrated into your existing tools.
The Scorecard
Reporting layerA running comparison of baseline against actual results, updated on a schedule. Built to answer a budget review directly: what did this cost, what did it save, in money and hours.
Plarix Platform
In developmentThe long-term goal: a self-serve version of this process, so companies can run it across many workflows without a consulting team behind every one. Early customers help shape it.
AI, priced like every other investment.
Most companies today run AI pilots with no baseline, no economic model, and no result that survives a budget review. That's not a failure of the technology. It's a gap in how the work gets scoped.
Plarix is building the discipline to close that gap: measure first, build only what pays for itself, and report the result the same way you'd report any other investment. The long-term goal is a platform any company can run itself, across as many processes as it has.