Paradym Next: early evidence from four projects

We compared four legacy modernisation projects delivered with Paradym Next against a commercial estimate for the same work done without AI. The reductions in time and cost were large, and consistent across every project.

What we measured

For each project we took a commercial estimate for traditional, non-AI delivery and compared it with the actual time and cost recorded in Paradym Next, for work that was either complete or in progress.

The four projects covered Java, C, C++ and Progress OpenEdge systems, rebuilt on React, .NET, Azure and AWS.

What we found

Across the four projects, time and cost both came in at roughly 70% below the estimate for traditional delivery.

Why it matters beyond the headline

Faster delivery does more than cut the project budget. It shortens the period in which two systems have to run in parallel, brings the benefits of the new system forward, and reduces how long the organisation depends on an ageing stack and the few people who understand it.

Where the savings come from

Paradym Next uses AI to accelerate the slowest parts of modernisation: understanding the legacy system and rebuilding its logic. People stay in the loop throughout, leading discovery with the teams who know the system and reviewing what the agents produce.

What this does and does not show

This is a preliminary review, not a controlled trial. The baseline is an estimate rather than a measured alternative, the projects differ in scale and complexity, and some figures cover work still in progress. The findings are directional, and results will vary with the size and condition of each system. The sample is small and the evidence early, but the effect is consistent across every project.