AI-Powered FMEA Knowledge Assistant for an OEM
How an OEM turned connected FMEA knowledge into a conversational, traceable engineering application using DCH, M4AI, and Skill Flows.
Challenge
FMEA knowledge is connected — search is not
FMEA data contains critical engineering knowledge about systems, components, functions, failure modes, causes, and effects. But finding the right information often requires engineers to know the exact FMEA number, component name, or internal terminology.
Traditional search struggles because engineering questions rarely follow a fixed structure. An engineer may ask:
“Which system elements are related to this FMEA?”
“What functions are connected to the stator?”
“Which failures could affect this component?”
Answering these questions requires more than keyword search. The system must understand which engineering entities the user means, follow the correct relationships across the engineering knowledge graph, and return an answer that can be verified.
The knowledge involved spans the full FMEA model: FMEA numbers, system elements, functions, components and part numbers, failure modes, causes, effects, requirements, tests, and evidence — all connected, and all scattered from the perspective of keyword search.
Solution
A controlled engineering workflow, not another chatbot
The OEM implemented an AI-powered FMEA application — FMEA KI Apps — using the Context64.ai stack:
- Data Context Hub (DCH) processes the source data, applies the configured entity and relationship model, and builds the engineering knowledge graph through governed workflows.
- Memory 4 Your AI (M4AI) makes this graph context available to specialised agents.
- FMEA KI Apps provides the application layer engineers interact with.
The application combines conversational access to FMEA knowledge, entity and parameter identification, user confirmation for ambiguous matches, dynamic knowledge-graph navigation, reusable Skill Flows, and traceable agent and graph execution.
The result is not simply another chatbot. It is a controlled engineering workflow built around the OEM's FMEA model.
- Parameters identifiedFMEA number · system element
- Entity confirmed“Stator (E-Drive)” — user confirmed match
- Query generatedPlanner agent → read-only Cypher
- Records returnedDCH executes · results consolidated
- Answer traceableEvery step inspectable
How it works
01 · Engineering data is structured in DCH
Source data is mapped into defined target entities and relationships. The OEM configures FMEA entities, system elements, components and part numbers, functions, failure modes, causes and effects, the relationships between these entities, and the import and processing workflows. DCH generates the required workflows and Airflow DAGs to build and update the knowledge graph.
02 · A Skill Flow defines the application behaviour
Each FMEA chat is connected to a published Skill Flow, which defines which business parameters must be identified, which DCH agents participate, which views or functions are available, whether fixed tools or dynamic graph navigation should be used, which agent produces the final response, and how large result sets are processed. Different engineering use cases can be configured without creating a separate application for each question type.
03 · KI Apps identifies what the user means
Before querying the graph, the application identifies relevant parameters such as the FMEA number, part number, system element, or component. When several possible matches exist, the user is asked to confirm the correct entity — preventing the system from silently selecting the wrong component or FMEA and generating an answer from incorrect context.
04 · The Knowledge Graph Navigator creates the query
For open-ended engineering questions, the Knowledge Graph Navigator generates a new read-only graph query for the specific request: the user question and confirmed FMEA parameters go to a planner agent that generates Cypher, DCH executes the query, and a consolidator produces the answer from the graph results. Unlike a predefined view, the Navigator is not limited to questions configured in advance — it dynamically follows the relevant entities and relationships available in the FMEA knowledge graph.
05 · The answer remains traceable
For each response, the application can expose the identified parameters, confirmed entity values, planner activity, generated graph query, DCH function execution, number of records returned, worker processing for large results, and the final consolidation. Engineering teams see how the answer was produced — and can distinguish between missing graph data, incorrect entity selection, and an overly restrictive query.
Fixed Skills and dynamic navigation
The application supports two complementary execution models, both governed through the Skill Flow configuration:
- Fixed Skill Set — preconfigured DCH views, functions, or agent systems are exposed as tools. Best suited to known, repeatable questions where deterministic behaviour is important.
- Knowledge Graph Navigator — a planner agent generates a new query for each question and executes it through a read-only DCH function. Better suited to exploratory questions where users navigate the engineering graph in ways that were not predefined.
Handling large engineering results
Some graph queries return hundreds of records — more information than a single agent should process at once. In these cases the application splits the result into manageable sections, sends the sections to several worker agents in parallel, combines the worker findings, and passes the consolidated context to the final response agent. Broad engineering questions are handled without truncating important graph information.
Impact
Controlled, inspectable access to FMEA knowledge
The OEM gained a domain-specific AI application that enables engineers to interact with FMEA knowledge through natural language while keeping the process controlled and inspectable.
Key benefits include:
- Faster access to connected FMEA knowledge
- Less dependence on exact internal terminology
- Safer entity selection through user confirmation
- Dynamic traversal of complex engineering relationships
- Reusable application workflows through Skill Flows
- Transparent graph and agent execution
- A scalable foundation for additional engineering KI Apps
Key Takeaway
Beyond FMEA
The same architecture extends to other engineering domains where information is distributed across connected systems: requirements analysis, test and validation, quality management, change-impact analysis, Jira and issue intelligence, product lifecycle data, root-cause investigation, and compliance and traceability.
Data Context Hub provides the governed knowledge layer. Memory 4 Your AI provides graph-based context for agents. KI Apps turns those capabilities into focused applications for real engineering workflows.
Explore the Context Layer for Engineering, browse more success stories, or talk to our engineering AI team about your FMEA and quality engineering use cases.
Challenge
FMEA knowledge is connected — search is not
FMEA data contains critical engineering knowledge about systems, components, functions, failure modes, causes, and effects. But finding the right information often requires engineers to know the exact FMEA number, component name, or internal terminology.
Traditional search struggles because engineering questions rarely follow a fixed structure. An engineer may ask:
“Which system elements are related to this FMEA?”
“What functions are connected to the stator?”
“Which failures could affect this component?”
Answering these questions requires more than keyword search. The system must understand which engineering entities the user means, follow the correct relationships across the engineering knowledge graph, and return an answer that can be verified.
The knowledge involved spans the full FMEA model: FMEA numbers, system elements, functions, components and part numbers, failure modes, causes, effects, requirements, tests, and evidence — all connected, and all scattered from the perspective of keyword search.
Solution
A controlled engineering workflow, not another chatbot
The OEM implemented an AI-powered FMEA application — FMEA KI Apps — using the Context64.ai stack:
- Data Context Hub (DCH) processes the source data, applies the configured entity and relationship model, and builds the engineering knowledge graph through governed workflows.
- Memory 4 Your AI (M4AI) makes this graph context available to specialised agents.
- FMEA KI Apps provides the application layer engineers interact with.
The application combines conversational access to FMEA knowledge, entity and parameter identification, user confirmation for ambiguous matches, dynamic knowledge-graph navigation, reusable Skill Flows, and traceable agent and graph execution.
The result is not simply another chatbot. It is a controlled engineering workflow built around the OEM's FMEA model.
- Parameters identifiedFMEA number · system element
- Entity confirmed“Stator (E-Drive)” — user confirmed match
- Query generatedPlanner agent → read-only Cypher
- Records returnedDCH executes · results consolidated
- Answer traceableEvery step inspectable
How it works
01 · Engineering data is structured in DCH
Source data is mapped into defined target entities and relationships. The OEM configures FMEA entities, system elements, components and part numbers, functions, failure modes, causes and effects, the relationships between these entities, and the import and processing workflows. DCH generates the required workflows and Airflow DAGs to build and update the knowledge graph.
02 · A Skill Flow defines the application behaviour
Each FMEA chat is connected to a published Skill Flow, which defines which business parameters must be identified, which DCH agents participate, which views or functions are available, whether fixed tools or dynamic graph navigation should be used, which agent produces the final response, and how large result sets are processed. Different engineering use cases can be configured without creating a separate application for each question type.
03 · KI Apps identifies what the user means
Before querying the graph, the application identifies relevant parameters such as the FMEA number, part number, system element, or component. When several possible matches exist, the user is asked to confirm the correct entity — preventing the system from silently selecting the wrong component or FMEA and generating an answer from incorrect context.
04 · The Knowledge Graph Navigator creates the query
For open-ended engineering questions, the Knowledge Graph Navigator generates a new read-only graph query for the specific request: the user question and confirmed FMEA parameters go to a planner agent that generates Cypher, DCH executes the query, and a consolidator produces the answer from the graph results. Unlike a predefined view, the Navigator is not limited to questions configured in advance — it dynamically follows the relevant entities and relationships available in the FMEA knowledge graph.
05 · The answer remains traceable
For each response, the application can expose the identified parameters, confirmed entity values, planner activity, generated graph query, DCH function execution, number of records returned, worker processing for large results, and the final consolidation. Engineering teams see how the answer was produced — and can distinguish between missing graph data, incorrect entity selection, and an overly restrictive query.
Fixed Skills and dynamic navigation
The application supports two complementary execution models, both governed through the Skill Flow configuration:
- Fixed Skill Set — preconfigured DCH views, functions, or agent systems are exposed as tools. Best suited to known, repeatable questions where deterministic behaviour is important.
- Knowledge Graph Navigator — a planner agent generates a new query for each question and executes it through a read-only DCH function. Better suited to exploratory questions where users navigate the engineering graph in ways that were not predefined.
Handling large engineering results
Some graph queries return hundreds of records — more information than a single agent should process at once. In these cases the application splits the result into manageable sections, sends the sections to several worker agents in parallel, combines the worker findings, and passes the consolidated context to the final response agent. Broad engineering questions are handled without truncating important graph information.
Impact
Controlled, inspectable access to FMEA knowledge
The OEM gained a domain-specific AI application that enables engineers to interact with FMEA knowledge through natural language while keeping the process controlled and inspectable.
Key benefits include:
- Faster access to connected FMEA knowledge
- Less dependence on exact internal terminology
- Safer entity selection through user confirmation
- Dynamic traversal of complex engineering relationships
- Reusable application workflows through Skill Flows
- Transparent graph and agent execution
- A scalable foundation for additional engineering KI Apps
Key Takeaway
Beyond FMEA
The same architecture extends to other engineering domains where information is distributed across connected systems: requirements analysis, test and validation, quality management, change-impact analysis, Jira and issue intelligence, product lifecycle data, root-cause investigation, and compliance and traceability.
Data Context Hub provides the governed knowledge layer. Memory 4 Your AI provides graph-based context for agents. KI Apps turns those capabilities into focused applications for real engineering workflows.
Explore the Context Layer for Engineering, browse more success stories, or talk to our engineering AI team about your FMEA and quality engineering use cases.
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Talk to the team behind this work. We will walk you through the architecture, the deployment shape, and the path to your first production agent.