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PODO® AI Framework

Where industrial data becomes action.

A manufacturing AI framework that connects shared plant context, manufacturing-specific skills, purpose-built applications, and AI Engineering Agents — so teams can detect, diagnose, and act from the same understanding of the plant.

Explore the framework → See PODO in your plant
One system. Four connected layers.
Knowledge · Skills · Applications · Collaboration
01 / DETECT See what changed
02 / DIAGNOSE Understand why
03 / ACT Move work forward
Explore the framework
01 / The shared model

Every agent sees the same plant.

PODO® builds a working model of your plant — assets, lines, batches, work orders and the people who run them, plus the relationships between them. Equipment data, quality records, maintenance history and operator knowledge resolve to the same entities, so every agent reasons over the same plant instead of its own private slice.

// SHARED PLANT MODEL ILLUSTRATIVE CONTEXT BUILD
ENTITIES 01
RELATIONSHIPS 00
AGENT VIEWS 00
LIVE BUILD · ONE PLANT / ONE FRAME OF REFERENCE
RESOLVING IDENTITY
Historian Pump 4 Equipment tag
CMMS P-104 Asset ID
Operator note CIP skid pump Shop-floor name
Asset · shared entity P-104 Pump 4 · CIP skid pump
Line Line 2 Packaging
Batch B-2841 Current run
Shift Shift B 22:00–06:00
People Ops Team B Assigned crew
Fault Bearing wear Detected event
Repair BRG-07 Bearing replacement
Work order WO-1842 Maintenance record
AI Engineering Agent Reliability Same plant context
AI Engineering Agent Quality Same plant context
AI Engineering Agent Energy Same plant context
AI Engineering Agent Process Same plant context
RUNS ON USED IN RAN DURING FAULT ON LED TO RECORDED IN CREWED BY
3 NAMES → 1 SHARED ENTITY · P-104 is now the same asset everywhere.
Shared plant model → Four connected layers
02 / The core architecture

One system. Four connected layers.

The PODO® AI Framework starts with a shared model of your plant. On top of it sits a library of skills, the agents built from those skills, and the coordination that lets those agents work as one.

// FOUR-LAYER FOUNDATION ● PODO FRAMEWORK
PODO® AI Framework four-layer manufacturing AI architecture
One integrated framework · four connected layers
LAYER 01

Knowledge

Manufacturing ontology · shared plant context
Operational foundation
What it does

Maintains a living representation of how equipment, processes, people, events and operating conditions relate to one another, giving AI a shared frame of reference for reasoning across the plant.

Why it matters

Without shared context, every model and application works from a different picture of operations. The ontology gives the rest of PODO® a common manufacturing foundation.

Manufacturing ontology Living plant context Equipment · process · people relationships Operational + enterprise data
Foundation shared plant context →
LAYER 02

Skills

Manufacturing-specific AI techniques
Shared toolkit
What it does

Provides a library of manufacturing-specific techniques that applications and AI Engineering Agents can draw on across use cases, so proven reasoning carries forward as the system grows.

Core skills
Pattern detection Root-cause reasoning Document + text understanding Process simulation Task sequencing + orchestration
Apply shared skills across use cases →
LAYER 03

Applications

Purpose-built, job-specific experiences
Ready for manufacturing work
What it does

Turns shared context and manufacturing-specific skills into purpose-built applications for the manufacturing priorities engineers work on every day.

Purpose-built applications
Asset Performance Management Detect emerging issues, prioritize maintenance and improve reliability. Explore application → Asset Performance Benchmarking Compare performance across similar equipment, lines and facilities. Explore application → Energy Management Detect waste and uncover efficiency opportunities across equipment and operations. Explore application → Process Performance Management Support decisions that improve quality, throughput and yield. Explore application →
Experience job-specific answers engineers can use →
LAYER 04

Collaboration

AI Engineering Agents · decision to action
Connected to the workflow
What it does

AI Engineering Agents carry recommendations—or safe, pre-approved actions—the rest of the way, working alongside the people accountable for the outcome and drawing from the same context, skills and applications.

How collaboration works
Domain-specific AI Engineering Agents Natural-language interaction Multi-agent coordination Human review + approvals Actions grounded in shared plant context
Outcome decision to coordinated action →
03 / AI Engineering Agents

AI Engineering Agents, connected to the work.

Ask what changed, why it matters and what to do next. Each AI Engineering Agent works from the same plant context, gathers the evidence and returns when human judgment is needed.

Always curious.
Always connected to the work.

PODO stays close to the signals, systems and people behind every operational decision.

PODO · ON SHIFT
PODO robot appearing from the edge of the page
Reliability · Motor M-217 PODO is watching the asset context
● CONTEXT CONNECTED
Message Reliability AI Engineer ↗
04 / The layers at work · illustrative example

A signal becomes action before the shift ends.

At 2:00 a.m., Motor M-217 starts running hot. PODO connects the signal to its operational context, identifies the likely issue, and turns it into a maintenance-ready response.

Diagnose · Across connected evidence

The alert arrives with the reason attached.

Current product mix, duty cycle, maintenance history and comparable motors resolve into one operational answer.

01 Deviation detected early DETECT
02 Evidence gathered across systems DIAGNOSE
03 Likely bearing wear identified DECIDE
04 Work order prepared for review ACT
A / SHOP FLOOR Inspect Motor M-217 during the next available window. The technician receives the likely cause, supporting evidence and recommended next step — not just an alert.
B / OPERATIONAL IMPACT Protect the production plan before the issue spreads. The same event is traced forward to schedule, energy and delivery implications.
01 / KNOWLEDGE Connects the evidence and operating context
02 / SKILLS Applies manufacturing-specific reasoning across the evidence
03 / APPLICATIONS Frames the job-specific reliability response
04 / COLLABORATION Brings the recommendation into the workflow
Ready to close the loop?

Put connected intelligence to work.

See how the PODO® AI Framework turns the signals your plant already collects into coordinated actions your team can review, authorize and carry forward.

See PODO in your plant → Read the white paper
Works with your stack Built around your plant People stay accountable
// FOUR-LAYER FOUNDATION ● SYSTEM READY
01
Knowledge Manufacturing context connected
LIVE
02
Skills Manufacturing skills available
READY
03
Applications Purpose-built application ready
READY
04
Collaboration AI Engineering Agents connected to the workflow
ROUTED
OUTCOME Ready for action.
→
Your team decides.
I’ll keep the context connected.
PODO robot