Use Cases

Generating a validated pipe route from a written spec, not a blank CAD file

Agentic CAD generation for industrial tube and pipe routing, with every AI-proposed constraint passing deterministic engineering checks before it reaches the model

Close-up of complex stainless steel industrial pipe routing in a processing plant

USE CASE / CAD GENERATION

Published product metrics

Validation gates

4 deterministic checks

Constraint traceability

100% logged

Geometry source

Deterministic, not LLM

These figures match the published metrics on the Predictim NeuroPipe product page — this walkthrough is a composite scenario, not a specific engagement.

The challenge

Agentic CAD generation for industrial tube and pipe routing, with every AI-proposed constraint passing deterministic engineering checks before it reaches the model

Segment: Constrained-geometry CAD generation

Constrained-geometry design problems like industrial tube and pipe routing start from natural-language requirements, existing CAD assemblies, and 2D drawings that an engineer has to manually translate into a parametric model. Using a large language model to speed that up sounds appealing until it proposes a constraint or a placement with no engineering check behind it — geometry an LLM invents directly is exactly what a validation-conscious engineering team can't sign off on.

The approach

How the deployment was structured

Constraints extracted, not invented

A semantic layer reads free-text requirements, CAD metadata, and 2D drawings to propose candidate constraints, each with a confidence score and a link back to its source.

Four deterministic gates before anything counts

Every candidate constraint passes geometric and engineering checks before it can influence generation; anything uncertain routes to human review instead of being silently accepted.

A parametric component, not a static export

Validated constraints parametrize an editable 3D component through a deterministic CAD engine, so engineers keep working in their existing tooling instead of starting over from a mesh.

Placement solved, not guessed

Combinatorial solvers propose routing and placement that satisfies every validated constraint while minimising footprint — the same optimisation lineage as Predictim's routing and stowage products.

What changed

Routing every AI-proposed constraint through four deterministic gates — instead of trusting the model's geometry directly — is what makes an LLM-assisted CAD workflow something an engineering team can actually sign off on.

Product used