Synthetic P&ID data to train document AI
SynthPID generates realistic, fully labelled synthetic P&IDs and engineering drawings — the ground-truth data needed to build, benchmark, and stress-test document-intelligence systems.
Great document AI needs great data.
Engineering drawings are scarce, sensitive, and expensive to label by hand. Without large volumes of accurately annotated examples, it's hard to train extraction models — and even harder to prove how well they perform.
Generate the data — with perfect labels.
SynthPID creates synthetic drawings that mirror the structure and messiness of the real thing, each paired with exact ground-truth annotations. It gives our teams a limitless, controllable supply of training and evaluation data — and powers the models behind DraftIQ.
What SynthPID does
Synthetic P&ID generation
Programmatically generates realistic piping & instrumentation diagrams with controllable complexity, layout, and style.
Configurable symbol libraries
Equipment, valves, and instruments drawn from configurable libraries and tagging conventions to match real-world variety.
Ground-truth labels
Every generated drawing ships with perfect, machine-readable annotations — the exact ground truth extraction models need.
Training & benchmarking data
Produces datasets at the volume and diversity required to train and objectively benchmark document-intelligence models.
Edge-case & stress scenarios
Deliberately generates the messy, ambiguous, and worst-case drawings that expose where models break.
Reproducible & scalable
Seeded, repeatable generation makes datasets versionable and experiments reproducible across model iterations.
Part of the AdaptIQ data engine
SynthPID and DraftIQ are built to work together — synthetic data in, better extraction out. It's how we keep improving document intelligence for real engineering work.
Building AI that reads technical drawings?
If you're training document-intelligence models and need labelled data at scale, let's talk about how SynthPID can help.