IRIS Foundry · P&ID Digitization
Your plant intelligence is locked in a PDF. Let's change that.
P&IDs describe how every asset, valve, instrument, and connector in your plant relates to everything else — making them the most critical, and most underused, data asset in any refinery, chemicals plant, power facility, or food and beverage operation. Most still exist as scanned drawings no AI can read. IRIS Foundry ingests, extracts, verifies, and maps them into a structured asset model wired directly into the unified namespace and knowledge graph — so every downstream AI initiative runs on real plant topology from day one. For teams building toward a digital twin, predictive maintenance, or agentic AI workflows, this is where that journey starts.
P&ID digitization is not a document project. It's the foundation for every AI initiative that follows.
Digital twin, predictive maintenance, agentic AI, connected-worker copilots — every initiative stalls when it can't answer the most basic question: "How is this plant wired together?" P&ID digitization gives your AI a deterministic, structured model of your operations to reason against. Without it, every team rebuilds that context by hand, for every project. Done once in IRIS Foundry — it feeds everything.
From static drawing to structured plant intelligence
IRIS Foundry’s P&ID ingestion transforms engineering drawings into machine-readable plant models. Vision AI trained specifically on P&IDs — not a general-purpose model — identifies every asset, tag, valve, instrument, and connector. Text is classified against tag formats and bound to the graphical element it labels. The result isn’t a digitized image; it’s a structured layer of plant intelligence.
Every digitized P&ID is verified element-by-element before it enters your asset hierarchy — because P&IDs drive safety-critical decisions and accuracy matters more than speed. Asset hierarchy mapping is 70–80% automated by AI, with the remainder flagged for engineer review. Programs that take 9–12 months manually complete in weeks.
Ingest, extract, verify, map — a clear path from drawing to data
How IRIS Foundry takes a PDF and produces production-grade plant intelligence in four steps.
Ingest
Upload a single P&ID or a bulk drawing set covering a plant, line, area, or entire facility network. Multi-page diagrams ingested as a group. Duplicate flags prevent double-counting before extraction begins.
Extract
Vision AI trained on P&ID symbol libraries identifies every element — assets, tags, valves, instruments, connectors — and assigns a confidence score to each. The drawing moves from analyzing to unverified.
Verify
Engineers review the extraction element by element. A quality gate, not a bottleneck — because P&IDs drive safety-critical decisions. Each verified drawing becomes training data for your custom model.
Map
Automap with AI: 70–80% of entities resolve automatically against your existing asset hierarchy. Lower-confidence matches surface for engineer review. Custom models retrain on your verified drawings, improving accuracy with every iteration.
Purpose-built P&ID intelligence, not retrofitted OCR
Six capabilities that separate native IRIS Foundry P&ID digitization from point solutions and document-management tools.
Vision AI trained on P&IDs
Not a general-purpose object detector — a model trained on the symbol libraries, line conventions, and tag formats used in real engineering drawings. It identifies equipment types, instruments, and process topology, not just text on a page.
Semantic OCR and tag classification
Text isn't just read — it's classified as an asset tag, instrument identifier, connector, or valve, and bound to the graphical element it labels. The difference between "knowing what it says" and "knowing what it means."
Asset hierarchy mapping, automated
Extracted entities map to your existing CMMS, EAM, or IRIS Foundry asset model. Where none exists, IRIS Foundry builds one from the diagrams. Typical automation rate: 70–80%, with flagged exceptions for engineer review.
Custom model retraining per plant
Every verified P&ID becomes training data. A food and beverage plant, a refinery, and a semiconductor fab don't draw diagrams the same way — and neither should their extraction model. Purpose-built models outperform any generic global alternative.
Natural language access via copilot
Once digitized, diagrams become queryable in plain language. Ask which instrumentation is upstream of a flagged asset and get back tags, topology, P&ID context, and live asset health — in a single answer with a direct link to the drawing.
Wired into the unified namespace from day one
Digitized P&IDs feed directly into the IRIS Foundry unified namespace — not as a separate document layer, but as structured context every agent, model, and copilot in the platform can use from the moment the drawing is verified.
70 – 80%
Asset mapping automated by AI. IRIS Foundry resolves the majority of P&ID entities against your existing hierarchy. Engineers review the flagged remainder.
Weeks
Typical program duration. Programs that take 9–12 months to complete manually are substantially done in weeks with IRIS Foundry's AI-assisted workflow.
What becomes possible when your P&IDs are alive
P&ID digitization isn't an end in itself. It's what makes every downstream initiative across IRIS Foundry faster, more accurate, and more reliable.
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Predictive maintenance that knows your topology
Predictive models don't just know a sensor's value — they know what equipment it's attached to, what process it feeds, and what failure modes are in scope. P&ID-derived asset context turns an alert into an explanation.
HAZOP support — without manual line tracing
Automated HAZOP workflows require a complete, up-to-date process topology to trace consequences through a system. P&ID digitization supplies that topology. Without it, every HAZOP review falls back to manual line tracing — slow, error-prone, and hard to audit.
Management of Change, accelerated
MOC reviews require validating current P&ID accuracy. When drawings are stale, compliance teams slow approvals or absorb risk they can't see. Digitized, current P&IDs make every change review traceable and faster.
Digital twin built on a real plant model
Digital twin simulations run on topology. Topology comes from P&IDs. IRIS Foundry's P&ID digitization supplies the deterministic plant model that makes digital twin outputs reliable — not approximations built from incomplete data.
Engineering knowledge preserved before it walks out the door
Senior engineers who carry the plant in their heads are retiring. P&ID digitization surfaces accumulated context — personal markups, undocumented exceptions, and workarounds absorbed over decades — and structures it before it leaves.
P&ID digitization is the foundation. Asset performance intelligence is what you build on it.
The asset topology you verify during P&ID digitization becomes the context layer for every predictive model that follows — failure mode detection, anomaly scoring, and work order prioritization all run better when they know what equipment they’re watching and how it connects. IRIS Foundry’s Asset Performance Intelligence builds directly on your digitized plant model.
One capability, wired into the full IRIS Foundry platform
P&ID digitization in IRIS Foundry is not a standalone tool. Every verified drawing feeds the shared foundation that powers prediction, agents, and operational AI across the platform.
Unified Namespace
Digitized P&IDs give every tag a topological home — not just a numeric value. Assets, instruments, and their relationships flow directly into the unified namespace, making each tag more meaningful to every downstream system that reads it.
Cortex (Knowledge Graph)
P&ID-derived asset hierarchies, failure modes, and process relationships become nodes and edges in the Cortex (knowledge graph) — making root cause analysis, fault tracing, and KPI diagnostics navigable through structured intelligence.
IRIS Flows — Agentic Workflows
When IRIS Flows agents run an anomaly investigation or predictive maintenance workflow, they draw on the plant topology that P&ID digitization supplied. The agent knows what’s connected to what — because the P&ID told it.
Predictive Asset Intelligence
Predictive Asset Intelligence models go from knowing sensor values to knowing asset context. The P&ID-derived topology turns an anomaly alert into an explanation — with upstream and downstream equipment, failure history, and process context attached.
Questions about P&ID digitization in IRIS Foundry
Straight answers for engineering leads and OT teams evaluating P&ID digitization as part of a broader industrial AI program.
Your P&IDs hold the plant model your AI needs. Put it to work.
Whether you're starting with a pilot on one process unit or planning a plant-wide rollout, IRIS Foundry's P&ID ingestion is built to scale with your program. Talk to an expert who knows your industry — not a generalist.