We use artificial intelligence to accelerate every stage of MRO data cleansing and enrichment. But every recommendation passes through an experienced data specialist before it reaches your system. AI handles the pattern recognition at scale. Humans make the decisions that matter.
A typical material master contains hundreds of thousands of records with inconsistent descriptions, missing attributes, and hidden duplicates. Manual review alone cannot keep pace with the volume or catch every pattern.
Records in a large material master. Reviewing each one manually takes years. AI pre-processes the entire catalog in hours, surfacing the items that need human attention.
Typical duplicate rate in an uncleansed MRO catalog. These duplicates hide across abbreviations, misspellings, and naming variations that are difficult to catch with simple text matching.
Throughput increase when AI pre-screens and prioritizes records for human review, compared to purely manual workflows.
MRO data has real consequences. A wrong part number can shut down a production line. A missed duplicate inflates inventory costs. AI accelerates the work, but domain experts make every final call.
Models analyze records, detect patterns, and generate recommendations — standardized descriptions, taxonomy codes, duplicate clusters, extracted attributes.
Experienced MRO data analysts evaluate every AI recommendation. They accept, refine, or override based on domain knowledge and client-specific requirements.
Every human correction feeds back into the system. The more data we process for your organization, the more accurate the AI becomes for your specific catalog.
AI is embedded at specific points in the data cleansing pipeline where pattern recognition and scale provide the most value.
The most common data quality problem in MRO catalogs is duplicate records hiding behind different descriptions. A "Gate Valve 2in 150# CS" and a "VALVE,GATE,2",CL150,CS,RF" are the same item — but simple text matching won't catch that.
Our AI models use semantic similarity, learned abbreviation patterns, and attribute-aware matching to cluster records that likely refer to the same material. Analysts then review each cluster and confirm which records are true duplicates.
Example: AI-Detected Duplicate Cluster
Example: AI-Suggested Standardization
Original Description
AI-Suggested (ISO 8000)
Free-text material descriptions are the biggest source of inconsistency in MRO data. The same item can appear as a cryptic abbreviation, a sentence fragment, or a manufacturer-specific code. ISO 8000 defines how descriptions should be structured, but rewriting thousands of records manually is painstaking.
Our NLP models parse free-text descriptions, identify the item noun, extract key attributes, and generate a standardized ISO 8000-formatted description. Analysts review the suggestion and make adjustments based on client conventions.
Classifying materials to UNSPSC, eClass, or eOTD requires understanding what an item is from its description and attributes — then mapping it to the correct node in a taxonomy with tens of thousands of categories. This is where machine learning excels.
Our classification models are trained on millions of previously classified MRO records. They suggest the most likely taxonomy code along with confidence scores. High-confidence suggestions are batched for rapid analyst confirmation. Lower-confidence items get routed for deeper review.
Example: AI Classification Suggestion
Suggested UNSPSC Classification
Example: Extracted Attributes
AI-Extracted Key-Value Pairs
| Item Noun | Pipe |
| Material | Carbon Steel |
| Specification | ASTM A106 Grade B |
| Schedule | 40 |
| Type | Seamless |
| Size | 4 inch |
| End Connection | Beveled End |
MRO descriptions often encode critical technical attributes in compressed, abbreviated text. A human reading "PIPE,CS,A106-B,SCH40,SMLS,4",BE" knows what each part means — but the ERP system stores it as a single unstructured string.
Our NLP models decompose descriptions into structured key-value attribute pairs. They recognize MRO-specific abbreviations, material grades, dimensional values, and engineering specifications. This extracted data populates the SemFact Schema attributes that drive classification and description rendering.
Not all records need the same level of attention. Some are clean and complete. Others have critical issues — incorrect manufacturer references, invalid part numbers, or data that contradicts known product specifications.
AI scores each record for completeness and consistency, flagging anomalies that need priority review. This lets analysts focus their expertise where it adds the most value rather than reviewing records linearly.
Example: Quality Score Triage
SEAL KIT FOR PUMP
No MFR, no PN, no attributes
BEARING SKF 6310 2Z
MFR found, PN mismatch with description
VALVE, BALL, 2", CL150, 316SS, RF, FBR
Complete record, verified attributes
Transparency matters. Here is what remains firmly in human hands.
No AI output is written to your data without a human approving it. Every classification, every merged duplicate, every standardized description is reviewed by a specialist.
Project management, requirements gathering, and exception handling are done by your dedicated project manager — not a chatbot.
Your organization's governance policies, naming conventions, and approval workflows are configured and enforced by human administrators.
Ambiguous items, obsolete parts, and records requiring engineering judgment are routed directly to senior specialists — AI does not guess on what it cannot determine with confidence.
The same AI technology that accelerates our cleansing workflow also powers Data Advisor — a natural language interface that lets you ask questions about your data and get instant answers on progress, quality, and trends.
Learn more about Data AdvisorTake our free data quality assessment to get a personalized health score for your material master, or contact us to discuss how our AI-enhanced workflow can work for your organization.