Context Ontology for Manufacturing & Supply Chain AI

Supply Chain Ontology
Agentic AI in Manufacturing
Supply Chain AI Agents
Context Ontology Accelerator
AWS Context

Mactores builds context ontology for manufacturing: one governed map of how your materials, parts, warehouses, shipments, suppliers and customer orders connect, plus the rules that decide what can ship, what is at risk and who may act. Supply chain AI agents query that map before they answer. We build it with your planners, buyers and quality leads, run it in your AWS account on the Context Ontology Accelerator now or on AWS Context once AWS releases it, and write the production date and fee into the SOW before work begins.

Ask a supply chain agent which customer orders slip if a castings supplier confirms nine days late. It will find the purchase order in ERP, the stock in the warehouse system and the open work orders in MES. Then it guesses, because ERP calls the part P-10442, MES knows the same material as RM-7731, and the warehouse counts quarantined stock as available. Each system was right by its own rules. Nothing told the agent how they relate. That missing map is the gap between agentic AI in manufacturing that demos well and agents your planners act on.

Start with one shortage your team traced by hand

Bring the part, the late shipment and what your agent said about it. The first session shows the links it lacked, the scope of a first release and a realistic go-live date.

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94%of engagements delivered by the date Mactores committed to, or earlier
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90 daysof no-charge fixes for production defects after you accept a release
2008the year Mactores started building on AWS

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AWS Agentic AI Specialization
AWS Manufacturing and Industrial Services Competency

Why Can't Your Supply Chain AI Tell Which Orders a Late Part Will Delay?

Because no single system knows how the pieces relate. ERP, MES, the warehouse system, transport tools and supplier portals each name parts, locations and shipments their own way, and none records which orders depend on which materials. An agent can fetch every record and still miss the exposure. A context ontology supplies those links and rules.

The questions planners ask every week sound simple. Where is this material right now? What is still on the water? Which orders does a late part touch, and who else can supply it? Each answer crosses four or five systems, and every crossing is a place where meaning shifts. A part becomes a material, then an item, then a SKU. A lot in ERP is a batch in MES and a heat number at the mill. Available stock means on hand to one team and unreserved, inspected and sitting in the right plant to another.

Operations leaders already feel the cost. In PwC's 2026 Digital Trends in Operations survey of 767 US operations and supply chain leaders:

87%

said poor data quality had held back the value of their digital initiatives.

55%

of industrial products leaders named integration complexity as a reason recent technology investments fell short, the most cited cause.

37%

were comfortable letting AI agents run a whole operational process on their own.

The pattern behind those numbers looks the same on almost every shop floor we walk:

What you see	Why it happens	What a context ontology adds
The agent reports stock that can't actually ship	Quarantined, allocated and in-transit inventory all look like on-hand in at least one system	One definition of available, with quality holds and allocations checked first
Shortage alerts arrive, but nobody sees which customer orders they hit	Purchase orders, work orders and sales orders sit in different systems with no shared link	Links from each purchased part through the bill of materials to the orders that consume it
Two plants report different figures for the same part	Each site keeps its own material master and naming habits	One identity per part, with every local number mapped to it
Alternate-supplier suggestions ignore quality and export rules	Approved-manufacturer lists and compliance status live in documents, not data	Qualification and export rules written as checks the agent runs before it recommends
Nobody can explain a recommendation at the next S&OP meeting	No record of which data, definitions and rules produced it	A trace of the sources, rules and ontology version behind every answer

The pressure to fix this is rising. Gartner forecasts that 60% of enterprises using supply chain management software will have adopted agentic AI features by 2030, up from 5% in 2025, and warns that supply chain data management has to mature before those agents scale. Agents will arrive inside your planning and procurement tools either way. What they can see when they get there is the part you control.

What Goes Into a Supply Chain Ontology: Materials, Warehouses, Shipments, Suppliers and Rules?

A supply chain ontology holds the things your operation runs on, such as materials, parts, plants, warehouses, shipments, suppliers and orders, plus how they connect and the rules that govern them. Mactores also maps every local identifier to one shared identity and writes quality, allocation and sourcing rules as checks agents run before answering.

Think of it as the context your best planner carries in their head, written down where software can read it. We scope it from questions, not from schemas. The first release is defined by 10 to 20 questions that your agents have to get right. Those questions decide which entities, links and rules go in, and they become its acceptance test.

Entity	What the ontology records	What your systems call it today
Material and part	One identity per purchased or made item, with revisions and approved alternates	Part number in ERP, material code in MES, item in the WMS, SKU in order management
Lot and batch	A traceable quantity with its origin, inspection status and genealogy	Supplier lot, production batch, heat number, serial range
Location	Plants, lines, warehouses, bins and in-transit stock in one hierarchy	Plant code, storage location, site ID, warehouse zone
Shipment	Inbound and outbound movements with carrier, mode and dates	Advance ship notice, bill of lading, transport order, container ID
Supplier	Sites, parent companies, qualifications and approved-manufacturer status	Vendor number, supplier ID, manufacturer code
Order	Purchase, work and customer orders, and the bill of materials that ties them together	PO, work order, sales order, schedule line
Quality event	Nonconformances, holds and corrective actions linked to lots and suppliers	NCR, quality notification, deviation, 8D report
Rule	Allocation, quality hold, sourcing and export constraints the agent must respect	Spread across SOPs, supply agreements and spreadsheets

Every first release ships with:

A supply chain ontology in production in your AWS account, built around the questions you signed off
Identity mappings that tie each plant's part, location and supplier codes to one record
Rules for holds, allocation and sourcing written as machine checks, each approved by its owner
A governed definition for each supply chain metric in scope, such as available to promise or on-time in-full, reconciled to the report your planners already trust
Golden questions with known answers, scored before launch and again after it
Runbooks, named stewards and hands-on training so your team can extend the model

Drafted by agents

Candidate entities and links pulled from ERP, MES, warehouse and transport schemas
Part and supplier matches across plants, each with a confidence score
Draft rules read from SOPs, quality manuals and supply agreements
Mappings from the ontology back to source tables
A full test run on every change

Decided by your experts and our FDEs

Which part record wins when two plants disagree
Whether quarantined stock ever counts as available
Which supplier qualifications are binding
Who may see supplier pricing and export-controlled data
When answers are good enough for planners to act on

That split is how Mactores works as an agent-native firm, and it is why ontology work runs at a different pace now. AWS says its accelerator compresses ontology drafting from months of manual authoring to days. We put the time saved into the conversations software can't have for you, like settling with procurement and quality what an approved supplier actually is.

How Is a Context Ontology Different From a Data Catalog, a Digital Thread or a Knowledge Graph?

Each one answers a different question, and most manufacturers end up with all four. The ontology is the piece that tells agents what the others mean.

What it does	Data catalog	Digital thread	Knowledge graph	Context ontology
Shows where data lives	Yes	Partly	Partly	Yes, through its source mappings
Follows a product from design to production and service	No	Yes	If modeled	Yes, for the questions in scope
Connects suppliers, stock, shipments and orders	No	Rarely	Yes	Yes
Holds agreed business definitions	As a glossary	No	No	Yes, each with a named owner
Holds rules agents check before answering	No	No	No	Yes
Applies access rules to every answer	Partly	Partly	Partly	Yes

How Are AI Agents Used in Manufacturing and Supply Chain Once the Context Exists?

With the context in place, AI agents in manufacturing stop retrieving records and start answering operational questions: which orders a late shipment affects, which approved supplier can cover a shortage, which stock is safe to move, which serials share a suspect lot, and how each plant's OEE compares on one formula. Planners still approve what happens next.

Supply chain questions come first because they cross the most systems and cost the most when they're wrong. Plant and quality questions follow on the same model, since the parts, lots and locations are already defined by then.

Question an agent can answer	Systems it joins	What the ontology settles
Which customer orders due in the next six weeks depend on parts from a supplier whose confirmed dates slipped this week?	ERP purchasing, supplier portal, MES, order management	Which purchased parts feed which assemblies, and which orders consume them
Which approved manufacturer can supply this part without a new first article inspection?	Approved-manufacturer list, quality system, supply agreements	Qualification status by part revision and supplier site
Which stock of this component can move from Plant B to Plant A without breaking another order?	Warehouse system, ERP allocations, quality holds	What counts as available, and which allocations are firm
What is in transit for this part, and when does it clear customs?	Transport management, carrier feeds, ERP	One shipment identity across the ship notice, bill of lading and container
Which shipped serial numbers contain material from supplier lot L-4471?	MES, quality system, ERP shipping	Product genealogy from lot to batch to serial to customer
How does third-quarter OEE compare across plants on the corporate definition?	MES, historian aggregates, shift calendars	One governed OEE formula that every plant's agent resolves

The fifth question is manufacturing traceability, and it usually lands in the second release. Once lots, batches and serials resolve to one identity, AI root cause analysis follows the evidence across systems instead of stopping at the edge of one. The analytics that sit beside this work, from OEE dashboards to predictive maintenance, belong to our manufacturing data analytics on AWS practice, and the two share one plant data foundation.

How Does a Context Ontology Connect Warehousing, Shipping and Material Data on the Context Ontology Accelerator or AWS Context?

On the Context Ontology Accelerator, AI drafts the ontology from your ERP, warehouse, transport and material data, your experts approve it, and it is stored as a knowledge graph your agents query over MCP. AWS says ontologies built this way carry into AWS Context, which it announced in June 2026 and has not yet released.

The accelerator is open source under Apache 2.0, generally available since 31 July 2026, and runs in your own AWS account on Amazon Neptune, Amazon OpenSearch Serverless and models in Amazon Bedrock, according to AWS's launch announcement. It works in three stages, and each stage maps onto part of a manufacturing estate.

How Do Scan, Model and Serve Apply to a Supply Chain?

Scan. Connect the sources behind your first questions: ERP purchasing and inventory tables, MES work orders, warehouse and transport records, and documents such as supply agreements and quality manuals. AI enriches the metadata and proposes matches across plants.
Model. Draft classes and links are reviewed with your planners, buyers and quality leads. Approved elements are published as W3C OWL classes, with SHACL shapes for rules and R2RML mappings back to the source tables.
Serve. Agents reach the ontology through the included MCP server or a SPARQL endpoint, and every answer resolves through the same governed definitions.

How Does One Shortage Question Resolve?

Take a planner's question: how many units of bracket B-220 can we promise to a customer in Ohio this week? The accelerator answers it on the cheapest path that works.

Governed metric first. If available to promise is defined as a governed metric, the answer comes straight from that definition, with no language model call.
Graph query next. If the question needs joins the metric doesn't cover, a SPARQL query runs over a virtual knowledge graph that reads ERP and warehouse tables in place, translated into SQL.
Agent fallback last. If the answer depends on text, such as a quality notice or an allocation clause in a supply agreement, an agent searches the documents and walks the graph under the same access rules.

The planner gets one answer either way, and the trace shows which path produced it. Routing common questions to governed metrics also keeps the monthly run cost down.

Which Manufacturing Standards Does the Ontology Build On?

The accelerator ships with no industry ontologies, so the grounding comes from your own models and from standards like these:

Standard	What it gives the ontology	How we use it
ISA-95 (IEC 62264)	The hierarchy from enterprise to site, area, line and work center, and the information ERP and MES exchange	No official OWL version exists, so FDEs translate the parts your questions need with your plant specialists
Industrial Ontologies Foundry Supply Chain Reference Ontology	Ready-made OWL classes for supply chain concepts, built on the Basic Formal Ontology	Imported and extended rather than rebuilt from scratch
QUDT	Units of measure and their conversions	Keeps millimeters and inches, or kilograms and pounds, from mixing in one answer
ISO 22400	Standard definitions for manufacturing KPIs such as OEE	The reference point for governed metrics every plant signs up to

Should Manufacturers Wait for AWS Context?

No. AWS has said user-defined ontologies will become a fully managed feature of AWS Context, and that teams who build ontologies with the accelerator will be able to manage them there. As of early November 2026, AWS had published no preview or general availability date. The slow work, agreeing what a part, a lot and available stock mean across plants, is the same on both, and it doesn't wait well. Every quarter without it is another quarter of shortages traced by hand.

How Does Mactores Deliver a Manufacturing Context Ontology, From First Question to Production?

Mactores delivers a manufacturing context ontology in five phases: choose the questions, map the sources, settle the definitions, prove the answers, then go live and hand over. A signed exit closes each phase, and go-live is timed around your S&OP cycle, month-end close and plant shutdowns rather than the other way round.

The sequence follows how Mactores works on every program, adjusted for supply chain realities. Planners and buyers have little time at month-end, and plant specialists are hardest to reach during shutdowns, so working sessions are booked around both.

Phase 01
Choose the questions
What happens
The 10 to 20 supply chain questions for the first release are picked and ranked, owners named, sources listed, platform confirmed and the production date set
Owner
Shared
Signed exit: Question set and SOW signed
Phase 02
Map the sources
What happens
Agents scan ERP, MES, warehouse, transport and supplier data, then part, location and supplier identities are matched across plants and reviewed
Owner
Agents and FDEs
Signed exit: Source map and identity matches approved by data owners
Phase 03
Settle the definitions
What happens
Planners, buyers and quality leads agree what each term means; hold, allocation and sourcing rules become checks; access policies are drafted
Owner
FDEs and your experts
Signed exit: Definitions and rules approved by business owners
Phase 04
Prove the answers
What happens
Golden questions run against answers your planners already know to be right, and access tests run for every role, including the requests that must be refused
Owner
Agents and FDEs
Signed exit: Acceptance gate passed
Phase 05
Go live and hand over
What happens
Agents switch to the ontology in stages, monitoring goes live, stewards are trained and runbooks pass to your team
Owner
Shared
Signed exit: Customer-signed acceptance

Before any agent answers from the ontology in production, it passes these checks:

Each term in scope carries one agreed meaning and a named steward
Every plant's part, location and supplier codes resolve to a single identity, with exceptions listed
Golden questions pass at the threshold set in phase one
Governed supply chain metrics match the report your planners already use
Each rule returns pass or fail with a stated reason
Role-by-role access tests pass, including refusals for supplier-confidential and export-controlled data
Each answer records its sources, rules and ontology version
The accelerator release is pinned, with a documented upgrade path
Spending limits are set for graph, search and model usage
Your team holds the runbook, and someone on it is on call

When phase one closes, scope, fee and go-live date go into the SOW as one commitment. A delay that starts with Mactores is ours to absorb. One that starts on your side, such as plant data access or an approver unavailable at a phase exit, is billed as time and materials at the rate stated in that SOW. The full terms are in our delivery commitment.

What Are We Building for an Aerospace Supply Chain, and What Have We Shipped on Plant Data?

For a global aerospace manufacturer, Mactores is correlating warehousing, shipping and material data into a supply chain ontology, so its AI can say where stock sits, what is in transit and where shortages will bite. Already shipped: HP's smart factory command center on unified MES, PLC, quality and logistics data, and live decision agents for a renewable grid operator.

Global aerospace manufacturer · In progress

A supply chain ontology across warehouses, shipments and materials

Parts, storage locations and shipments carry different identifiers in each of this manufacturer's systems, so its supply chain AI couldn't reliably say where a material was, what was still in transit or which orders a late part would hold up. The work starts from three datasets: warehousing, shipping and materials. Mactores is reconciling their identifiers, modeling the ontology alongside the client's supply chain specialists and linking the result in a graph the AI can traverse. Delivery is still under way, so we aren't quoting outcomes yet.

Manufacturing and industrial at Mactores

HP · Smart factory

A factory command center on unified plant data

Operators at HP's smart factory stitched MES, PLC, quality-sensor, logistics and ERP data together manually. Previous attempts ran aground on integration cost, which would have consumed the budget before any command center got built. Agents mapped the sources, built the integrations and lineage, and set up real-time streaming. FDEs owned the command center design and the operator workflows, and cut over while the line kept running. That unified estate is the ground a plant-floor ontology stands on.

Read the HP story

Renewable grid operator · Energy

Decision agents that act within set limits on live signals

Grid decisions depend on weather, demand, generation and storage, all changing minute to minute, yet the operator was deciding from lagging data and fixed rules. Mactores built agents that reason over real-time grid data and act only inside an agreed authority boundary, with people handling the edge cases. The operator's own team runs the system today. Industrial agents need what a supply chain agent needs: a clear map of what they may decide.

Read the grid story

What links the three is structure, not model choice. Each system works because the AI was told how the operation fits together and where its authority stops. More programs are on the Mactores stories page.

How Do You Keep Manufacturing AI Agents Within Bounds on Supplier, Export-Controlled and Plant Data?

By writing the limits into the ontology the agents read. Access policies decide which roles and agents see supplier pricing, export-controlled drawings or a single plant's data, rules decide which actions need a person's approval, and every answer records its sources, definitions and version. Stewards approve each change, so the model stays accountable as it grows.

Manufacturing data carries obligations a general-purpose agent knows nothing about. Technical data on a defense program may fall under ITAR or EAR. A joint-venture partner may see shared forecasts but never your cost roll-ups. A supplier's quote is confidential to the buyer who asked for it. Get one of these wrong in an agent's answer and the cost is legal, not just operational. The sector is also the one attackers favor: IBM's X-Force Threat Intelligence Index 2026 found manufacturing was the most attacked industry for the fifth year running, with 27.7% of incidents.

Who may see what. Policies by role, plant, program and supplier apply on every query path. The accelerator enforces them with Cedar policies and a SQL firewall, alongside the IAM and AWS Lake Formation permissions you already run.
Who may act. Recommendations that move stock, change an order or switch a supplier go to a named planner or buyer for approval before anything happens.
Who may change meaning. Stewards own each definition, changes go through review, and golden questions rerun on every release.
What happened and why. Each answer keeps a trace of the data, rules and ontology version behind it, ready for an audit or the next S&OP review.

We map these controls to the NIST AI Risk Management Framework and its Govern, Map, Measure and Manage functions. The payoff is trust as well as safety. Gartner predicts that by 2029, explicitly modeled business decisions will be five times more trusted and 80% faster than ungoverned ones, and its top supply chain technology trends for 2026 include both decision governance and product provenance built on knowledge graphs.

Which Audit and Customer Questions Can the Ontology Answer?

The question	Where the answer comes from
Which shipped units contain material from a recalled lot?	Product genealogy from supplier lot to serial number and customer shipment
Who approved this part's alternate supplier, and against which qualification?	The sourcing rule and the stewardship log for that part
Could the agent have shown export-controlled data to this user?	Access policy tests for that role, and the query log
Which battery models we sell in the EU still lack passport data?	Product, material and supplier declaration records linked in the graph, ahead of the EU battery passport becoming mandatory on 18 February 2027
Why did the agent recommend moving this stock?	The answer trace: sources, rules checked and ontology version

Which Manufacturers Need Supply Chain Context First: Aerospace, Automotive, Industrial or Electronics?

Manufacturers with long, regulated supply chains and many systems naming the same part gain first. Aerospace and defense lead, because shortages, certification and export control collide there. Automotive follows on supplier risk and traceability, industrial equipment on service parts, and electronics on constrained components and lot genealogy. Each starts with its costliest wrong answer.

Aerospace and defense

Thousands of purchased parts, long lead times and AS9100 quality records mean one late forging can hold up an aircraft program. The first release links each part to its approved sources, its first article inspection status and the customer orders it feeds, while ITAR and EAR rules decide who sees which drawings. Shortage and in-transit questions come first, because they are the ones planners chase daily. Manufacturing and industrial work

Automotive and mobility

Tier-1 and tier-2 suppliers, PPAP approvals and IATF 16949 traceability make supplier risk the opening question. Agents see which vehicle programs a slipping supplier touches and which lots a quality spill reaches, and quality teams get a containment list built from linked records instead of spreadsheets. Launch questions come next, such as which parts still lack PPAP approval for a new model year. EV battery makers add passport data for the EU market to the same model. See the manufacturing practice

Industrial equipment and machinery

Long-lived installed bases make service parts the pressure point. The ontology ties each serial number in the field to its bill of materials, current revision and the spare stock nearest the customer, so service agents only promise parts that exist and fit. Warranty and field-failure data join the model in a later release to support root cause work. Dealers and field technicians are often the first users, so answers are tested against real service tickets before go-live. Explore manufacturing at Mactores

Electronics and semiconductors

Allocation on constrained components and wafer-to-die genealogy drive the first questions in this segment. Agents see which builds a component shortage will stall, which alternate parts are already qualified for that design, and which shipped units trace back to a suspect lot. Because allocations change weekly, these models lean hardest on governed definitions of available and committed stock. Export controls on advanced chips and fabrication tools add access rules that the agents respect on every answer. More on our manufacturing work

Process manufacturers in chemicals, food and pharmaceuticals usually start elsewhere, with batch genealogy and specification limits, and the same approach applies.

What Does a Manufacturing Context Ontology Cost, and How Long Does It Take?

A first release covering 10 to 20 questions on a few source systems usually takes 8 to 12 weeks. The fee depends on how many questions, plants and source systems are in scope, how far part and supplier data disagree, how many rules need encoding and your export-control scope. Mactores fixes the fee and date once phase one closes.

Pricing note. Ontology work isn't priced from a list. The effort lives in your part masters, plants and rules, and the accelerator itself costs nothing to license. The drivers below shape the quote, and the figures that bind are the ones in your signed SOW.

Cost driver	Why it moves the effort
Questions in the first release	Each one pulls in its own entities, links, rules and tests
Plants and sites	Every site adds its own part, location and supplier codes to reconcile
Source systems	Several ERP instances, MES versions and warehouse systems mean more mappings
Master data conflicts	Parts and suppliers that disagree across plants need working sessions with their owners
Rules and documents	Supply agreements, quality manuals and SOPs have to be read, encoded and approved
Export control and confidentiality	ITAR, EAR and supplier-confidential data add policy design and testing
Platform	The self-managed accelerator needs more hardening and on-call planning than a managed service will

Run cost. Once live, what you pay each month depends on graph storage, search and model calls in your AWS account. Routing is the biggest lever: questions that resolve to a governed metric, such as available to promise, skip the model call entirely. Development stacks shut down when idle, and finance gets a cost report per use case before launch.

What Do Manufacturing Context Ontology Terms Mean?

Context ontology: A model of your business that AI reads when it answers: the things you deal with, how they connect, what each term means and the rules that apply.
Supply chain ontology: The part of a context ontology that covers materials, parts, locations, shipments, suppliers and orders, with the rules for stock, sourcing and allocation.
Knowledge graph: The connected data itself, stored as entities and links and organized by the ontology, so an agent can follow a path from one record to the next.
Digital thread: The connected record of a product from design through production and service, so a change or defect can be followed across every stage.
Product genealogy: The record of which lots, components and process steps went into each finished unit, used for recalls and root cause analysis.
Available to promise (ATP): The quantity of an item that can be committed to a new customer order once existing allocations, holds and incoming supply are counted.
ISA-95: An international standard, also published as IEC 62264, for the hierarchy and information flows between enterprise systems such as ERP and plant systems such as MES.
Industrial Ontologies Foundry (IOF): An open effort that publishes reference ontologies for industry, including a supply chain ontology in OWL.
Governed metric: A KPI such as OEE or on-time in-full with one approved formula and a named owner, so every plant and agent calculates it the same way.
SHACL shape: A W3C format for rules that check data against conditions, such as every serial number having at least one consumed lot.
Model Context Protocol (MCP): An open protocol that gives AI agents a standard way to call tools and data sources, an ontology among them.
FDE: A Mactores specialist placed with your supply chain, plant and data teams for the whole program. FDEs lead the modeling sessions, own architecture decisions and carry the go-live date.

How Can Mactores Help You Out?

Mactores puts the missing context under your supply chain and plant AI, with the go-live date written into the contract. Agents already live but unreliable? We begin with an answer review. Questions known but unanswered? A first-release supply chain ontology. Plant and ERP data still scattered? The data foundation comes first. Each path ends in a fixed fee.

Mactores has been building on AWS since 2008, much of it for manufacturers who need modernization without stopping the line and whose auditors expect evidence. That background shapes how we model context: definitions with owners, rules with tests, export-controlled data fenced off before an agent asks its first question, and cutovers booked around the production plan.

If this sounds like you	Where we start	What you walk away with
Supply chain agents are live, but planners double-check every answer	Supply chain answer review	A ranked list of what the agents were missing, plus a scoped first release with a date
You know the questions and need agents to get them right	First-release supply chain ontology	Agents answering from a live ontology, with your stewards running it
Plant and ERP data are still scattered across sites	Plant data foundation	A governed data platform on AWS that the ontology can sit on

When AI agents in manufacturing still need building, our AI agent development services take them from pilot to production on the same ontology, with the same signed phase exits and the same fixed-date terms. Many manufacturers run both at once: the ontology defines what parts, lots and stock mean, and the agents decide what to do with that knowledge inside the limits you set.

On the AWS side, Mactores holds Premier Tier Services status, the Manufacturing and Industrial Services Competency and the Agentic AI Specialization. The full list sits on our AWS partnership page. The ontology, mappings and rules stay with you, in open W3C formats in your own AWS account, and your planners stop reconciling part numbers before they can trust an answer.

What Do Manufacturers Ask About Context Ontology?

What is a context ontology for manufacturing?

It is a governed model of how a manufacturer's operation fits together, made available to AI agents when they answer. It names the materials, parts, lots, locations, shipments, suppliers and orders you work with, links them across ERP, MES, warehouse and transport systems, and holds the rules for stock, sourcing and quality. Agents query it, so their answers match how your planners think.

How are AI agents used in manufacturing and supply chain?

Most start with questions that cross systems: which orders a late shipment affects, which approved supplier can cover a shortage, where a part sits and what is in transit, which serials share a suspect lot, and how OEE compares across plants. With a context ontology underneath, agents answer those reliably, and planners or buyers approve any action that moves stock or changes an order.

Do we need a context ontology if we already have a data catalog or a digital thread?

Usually, yes. A catalog tells you where data sits, and a digital thread follows a product from design to service. Neither tells an agent which supplier lot feeds which customer order, what counts as available stock or which sourcing rules apply. The ontology adds those links, definitions and rules, and it can draw on the catalog and thread you already have.

Which systems can a manufacturing context ontology connect?

Typically ERP, MES, warehouse and transport management, PLM, quality management, procurement and supplier portals, plus historian aggregates where a question needs them. On the accelerator, much of this data is queried where it lives through a virtual knowledge graph, so you don't build another copy. Documents such as supply agreements and quality manuals feed the rules.

Is AWS Context available yet, and should manufacturers wait for it?

No. AWS Context was announced on 17 June 2026, and by early November 2026 AWS still hadn't given a preview or general availability date. Waiting gains little. AWS says work done in the Context Ontology Accelerator carries forward, since ontologies built there will be manageable in AWS Context, and the slow part, agreeing what parts, lots and available stock mean across plants, is the same on both.

Is the Context Ontology Accelerator free, and who runs it in production?

There is no license fee, because the code is open source under Apache 2.0. It is self-managed, though, and AWS Support doesn't cover it. Your AWS bill covers the services underneath, such as Amazon Bedrock, Amazon Neptune and Amazon OpenSearch Serverless, and someone on your side or ours has to own upgrades, release pinning and the on-call rota. Mactores can operate it for you or train your team to.

Can an agent trace a defect from supplier lot to shipped serial number?

Yes, once product genealogy is in the model. The ontology links supplier lots to production batches, machines, process steps, serial numbers and customer shipments, so an agent can list every unit built with a suspect lot and the customers who received it. Most manufacturers add this traceability in the second release, after the core supply chain questions.

Can every plant's agent use one OEE definition?

Yes. OEE becomes a governed metric with one approved formula, including how planned downtime and changeovers are treated, owned by operations and aligned with ISO 22400. Every plant's agent resolves OEE through that definition, so a cross-plant comparison means the same thing everywhere. Local variants can stay for plant use, labeled as such.

How do you keep export-controlled and supplier-confidential data out of agent answers?

Access policies in the ontology decide which roles, programs and agents can see each class of data, and they apply on every query path, document search included. ITAR and EAR technical data, supplier quotes and joint-venture information each get their own rules, and tests confirm the refusals work before go-live. Answer traces show what each agent touched.

How long does a first release take?

Usually 8 to 12 weeks for 10 to 20 questions on a handful of source systems at one site or product line. More plants, more ERP instances or heavy export-control scope extend it. The production date is agreed when phase one closes and written into the SOW, and later releases add questions and plants on their own dates.

Is a context ontology practical for a mid-sized manufacturer?

Yes. The first release is sized by questions, not by the size of the company, so a single plant or product line with a few source systems is a sensible start. Mid-sized manufacturers often move faster, because fewer ERP instances and plants need reconciling before agents can answer reliably.

Who owns the ontology after go-live, and does Mactores stay involved?

You own it. Everything we build, from ontology and mappings to rules, tests and runbooks, sits in your AWS account and code repositories, and ownership and exit terms are agreed in the SOW at signing. Your stewards are trained to extend it. Mactores can stay on to add plants and questions or to operate the platform, but nothing depends on us staying.

Which Shortage Did Your AI Miss Last Quarter?

Bring it to us. We'll find the links and rules your agents were missing and commit to a date for fixing them.

01
Walk an FDE through the shortage, the systems involved and your agent's answers in one working session.
02
Within a week, you have our read on what went wrong, a scoped first release and a proposed go-live date.
03
Sign a fixed-fee SOW and begin phase one.

Backed by our 30-day satisfaction guarantee, and any production defect found within 90 days of acceptance is fixed at no charge.

Fix my supply chain answers →lets-talk@mactores.com