Every delivery of material or purchased parts arrives with paperwork: material certificates, inspection certificates, declarations and test reports. Behind them sit supplier contracts and quality agreements that define what must be delivered and under what conditions. Together they are the evidence that what was received conforms to what was required.
In most organizations this evidence is filed, not used. Certificates are scanned and attached to receipts, contracts are stored with procurement, and checking one against the other is a manual reading exercise. When a question arrives later, such as which products contain material from a given heat, or which supplier obligations expire this quarter, answering it means opening documents one at a time.
This piece describes how to turn certificates and supplier contracts into queryable, auditable data: extract the facts, link them to parts and suppliers, and keep the source page attached to every value.
What the documents contain
The facts that matter are specific and checkable
Certificates and contracts are dense, but the facts that procurement and quality rely on are well defined.
A material certificate typically identifies the supplier, the product, the material grade, the heat or batch number and the dimensions, and reports the results of chemical analysis and mechanical tests, along with the requirements the material was tested against and the details of the issuing party. An inspection certificate records what was inspected, against which requirements and with what results. Each of these values can be checked against a specification.
Supplier contracts and quality agreements contain a different kind of fact: obligations. Delivery terms, required documentation, notification duties, change control commitments, validity periods and the specifications that apply. These obligations are what certificates are supposed to demonstrate, yet the two are rarely connected in a way a system can check.
The difficulty is format. Certificates arrive as scans, as PDFs generated by many different systems and as tables laid out differently by every supplier. Contracts are long prose with clauses that reference annexes and other documents. Generic text search finds the document but not the value, and AI that reads the text without structure can confuse a required value with a measured one.
Extract and link
From paper evidence to linked records
The approach is the same for certificates and contracts: define the facts, extract them with their source, and link them to the entities they concern.
Extraction follows a schema agreed with quality and procurement. For certificates: supplier, material grade, heat or batch, test results with their units and requirements, and validity. For contracts: parties, scope, obligations, dates, referenced specifications and required documents. Tables of test results are read as tables, so each value stays attached to its property and unit. Every value keeps its source page and region, and values that conformity decisions depend on can be confirmed by a person before they are used.
Linking is what makes the data useful. A certificate is linked to the supplier that issued it, the purchase order and receipt it accompanied, the parts or lots made from the material, and the specification it must meet. A contract obligation is linked to the supplier, the parts it covers and the documents it requires. In a governed knowledge graph, these links carry lineage and access rules, so the evidence for a part can be followed in either direction.
With certificates and requirements in the same graph, comparison becomes possible: measured values against required ranges, delivered grades against specified grades, received documents against the documents a contract requires. Discrepancies can be flagged for review instead of being discovered during an audit or after a failure.
Auditable by design
Keep the source page attached
A conformity record is only as good as the evidence behind it.
When a value is extracted from a certificate, the page and region it came from must stay attached. An auditor, a customer or an internal reviewer asking why a lot was accepted should be able to follow the chain: the part, the lot, the heat, the certificate, the test result and the page where it is printed. The record and the original document should never be separated.
Review status belongs on the record as well. Some values are verified automatically against clear rules; others are confirmed by a person. Knowing which is which lets reviewers focus on what matters, and lets the organization show how each value was established.
Traceability in the other direction matters just as much. When a supplier reports a problem with a heat or a batch, the graph can return every part, lot and product linked to it, together with the certificates involved. That question is hard to answer from filed documents and straightforward to answer from linked records.
Where AI fits
Assistants over conformity data, not over PDFs
Private AI becomes useful for conformity work once it can query records instead of reading documents.
Once certificates and contracts are structured and linked, a private AI assistant can answer conformity questions by querying records: which certificates for this part are missing, which obligations apply to this supplier, which lots used material from this heat. Each answer lists the records it used, with links to the source pages.
The assistant can also help with the parts of the work that are mostly reading and writing: summarizing a long quality agreement, drafting a request to a supplier for missing documents, or explaining a discrepancy flagged by a comparison. A person reviews and sends; the assistant prepares.
What it should not do is decide conformity on its own. Acceptance, rejection and concessions stay with qualified people, the assistant shows the evidence behind every suggestion, and the audit trail records what the assistant contributed and who made the decision.
How sDEN approaches it
Conformity evidence as governed data
sDEN Solutions extracts verified facts from certificates and contracts, and sDEN Foundation links them to parts, suppliers and specifications in a governed knowledge graph.
Within this scope
A schema agreed with your experts
The facts to extract, from grades and heats to obligations and validity, are defined with quality and procurement around the checks and decisions they support.
Within this scope
Source and review on every value
Each extracted value keeps its source page and region and its review status, with human review configured where conformity decisions depend on it.
Within this scope
Linked in both directions
Certificates, contracts, parts, lots and suppliers are linked, so evidence can be followed from a part to its source page, and from a supplier issue to every affected item.
What good looks like
Conformity you can enumerate
The aim is to answer conformity questions from data, with the source document one click away.
Procurement and quality can see, for a supplier or a part, which certificates were received, which values were checked, which obligations apply and which documents are missing. Supplier packages can be checked for completeness, and specifications compared with what was delivered.
When a question comes from an auditor or a customer, the answer is a set of linked records with their sources, not a search through archives. When an issue is reported upstream, the affected parts and lots can be identified from the graph.
The data stays in your environment and enriches the systems you already use, from ERP and the quality management system to analytics and private AI assistants that cite their sources.
Questions
Manufacturing knowledge, answered.
What can be extracted from a material certificate?
Typically the supplier, product, material grade, heat or batch number, dimensions, chemical and mechanical test results with their units, the requirements tested against and the issuing details. The exact fields are defined in a schema agreed with your quality and procurement teams.
Our suppliers all use different certificate layouts. Is that a problem?
Varied layouts are the normal case. Extraction combines OCR, private open-weight models and deterministic methods chosen per content, reads tables as tables, and keeps the source location of every value so any result can be checked against the original.
Can extracted values be compared with our specifications?
Yes, once certificates and specifications are both structured and linked to the same parts. Measured values can be compared with required values, and discrepancies flagged for review by a qualified person.
How are supplier contracts handled?
Contracts and quality agreements are extracted into obligations, dates, referenced specifications and required documents, then linked to the supplier and the parts they cover. That turns obligations into records you can enumerate, audit and act on.
Where does the data end up?
In a governed knowledge graph inside your environment, and in the systems your teams already use, such as ERP, the quality management system, analytics and private AI. Documents stay in their existing repositories.
