Conducting a high-quality FMEA has historically presented a difficult trade-off between depth and speed. Engineers often face the choice of spending weeks meticulously building failure chains or rushing the process to meet deadlines, potentially leaving important risks undiscovered.
Our application is designed to resolve this tension. By leveraging advanced generative AI within a specialized engineering framework, we automate the heavy lifting of structure creation, failure mode identification, and risk mapping, delivering a comprehensive analysis in a single run.
Unlike standard AI tools that simply "predict text," our application utilizes an agentic workflow. This means the AI simulates a cross-functional team of experts working in collaboration, rather than a single entity attempting to perform all tasks at once.
Instead of generating a flat list of risks, the system employs a multi-stage process with built-in quality gates. It deconstructs your documents to understand system architecture, generates logical failure chains, and rigorously self-corrects against engineering standards before the output is presented.
This approach enables the system to generate up to 200 technically distinct, logically consistent failure modes in a single run, with a depth that mirrors a facilitated engineering workshop.
The quality of the generated FMEA is directly proportional to the quality, clarity, and relevance of the documents you provide. The AI does not invent technical knowledge; it grounds its analysis strictly in your inputs.
The application supports PDF, CSV, TXT, XLSX, and image files. However, PDF format is strongly recommended.
Modern multimodal AI models do not only read text, but they also interpret visual structure. PDFs allow the system to extract information from:
This visual understanding significantly improves system decomposition, interface identification, and functional accuracy.
To preserve deep contextual reasoning, each analysis run currently supports up to 100,000 tokens, equivalent to approximately 300-400 pages of typical technical documentation.
Uploading fewer, higher-quality, and well-structured documents usually produces better results than uploading many loosely related files.
Focus on documents that define system behaviour, boundaries, and physics:
These documents allow the AI to ground functions, interfaces, prevention controls, and detection logic in objective design intent.
PFMEA requires documentation that connects manufacturing execution to product characteristics:
This enables accurate 4M analysis and realistic prevention and detection control identification.
All outputs are delivered in an AIAG & VDA aligned Excel structure using the harmonized 7-Step approach and Action Priority logic.
While originally developed for automotive applications, this methodology has become the de facto standard for technical risk analysis across aerospace, medical, industrial, and high-reliability industries.
Using this structure ensures that your FMEA remains review-ready, auditable, and logically consistent.
The generated output is a complete, structured FMEA draft.
Each row represents a full failure chain, connecting:
The application generates approximately the requested number of technically distinct failure modes, distributed across the system or process structure to ensure broad and balanced coverage.
The output should be treated as a high-quality engineering draft, intended to accelerate structured FMEA development rather than replace professional judgment.
The AI searches your uploaded documents to locate objective support for:
The Remarks column explicitly shows the source and reasoning used. This allows rapid validation and easy identification of assumptions or gaps.
Preventive and detection actions are generated as engineering suggestions based on the failure physics. These should be reviewed and adjusted by the engineering team to confirm:
Project management fields (responsibility, dates, status) are intentionally left blank. These must be assigned through your internal processes.
This application applies advanced self-review logic and quality gates to produce a disciplined FMEA draft. However, no automated system can fully replace engineering judgment.
The output should always be:
The application is intentionally designed to remain simple and focused. A typical workflow consists of only four steps:
No additional configuration is required.
The requested number of failure modes is the primary cost and computation driver.
The value is adjustable up to 200 failure modes and should be selected based on:
For early concept phases, fewer failure modes may be sufficient. For detailed design or manufacturing release, higher values are usually justified.
Whenever possible, PDF format should be used. PDF files preserve layout, diagrams, tables, and hierarchy, which significantly improves system understanding.
The application supports up to 100,000 tokens per analysis run, which is sufficient for most real-world projects. Token usage per document is visible to the user.
If the token limit is approached, users can:
This allows users to optimize input quality without sacrificing essential information.
FMEA generation time depends on:
In large analyses, generation can take up to one hour. This reflects the multi-stage reasoning and internal quality review process applied during analysis.
Credits are charged only when the delivered Excel file contains more than 75% of the requested failure modes.
If the system generates more complete FMEA entries than requested, no additional credits are charged.
This ensures that users only pay for usable, complete engineering output.
Document quality is the single most important factor influencing FMEA quality.
Clear, structured, technically meaningful documents lead directly to:
Poor, incomplete, or inconsistent documentation will limit output quality regardless of AI capability.
If you have questions, require guidance, or wish to share feedback, your input is always welcome and our team is available to support you.