Services Solutions · Agentic AI for Parametric CAD Generation

Predictim NeuroPipe

Predictim NeuroPipe is an agentic AI platform for parametric CAD generation, available as both SaaS and developer API. It pairs large language models with a deterministic, four-gate symbolic validator so that no AI-proposed constraint reaches a generated component without passing rigorous engineering checks — the LLM never outputs geometry or makes a placement decision directly. Built on neuro-symbolic research developed within Predictim's optimisation programme, it turns natural-language requirements, existing CAD assemblies, and 2D drawings into validated, editable, parametric components for constrained-geometry design problems such as industrial tube and pipe routing.

Close-up of complex stainless steel industrial piping

CAD / AGENTIC AI · NEURO-SYMBOLIC

Validation gates

4 deterministic checks

Constraint traceability

100% logged

Geometry source

Deterministic, not LLM

Illustrative results from representative deployments; not a guarantee of outcome.

Capabilities

What this platform does

Each capability is a deployed module — not a feature on a roadmap. Engagements typically begin with a 4-week scoping exercise to confirm fit against your operational data and constraints.

Natural-language & drawing constraint extraction

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

Four-gate symbolic validator

Every candidate constraint passes deterministic geometric and engineering checks before it can influence generation. Anything uncertain routes to human review — never silently auto-accepted.

Parametric CAD generation, not a static export

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

Constraint-aware placement & routing optimisation

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

Full audit trail

Every accepted or rejected constraint is logged with a natural-language justification, so a generated component's reasoning is traceable end to end, not a black box.