For AI Readiness

01

Most data in the chemical industry is fragmented across PDFs, spreadsheets and legacy systems — making it unusable by AI.

02

Even when data is available, it lacks consistent structure, taxonomy or business context — so LLMs can’t interpret it accurately.

03

Without trustworthy data, AI pilots stall, model outputs fail to gain user trust and companies can’t scale beyond experimentation.

01

Knowde harmonizes and standardizes product, supplier, and customer data across systems to create a single source of truth.

02

We apply a chemical-specific ontology that helps AI models understand relationships between products, applications and customers.

03

By building a connected knowledge graph, Knowde ensures that AI outputs are relevant, grounded and usable in real business workflows.

01

Clean, structured data shortens time to value for AI initiatives by removing months of prep work.

02

Model accuracy improves dramatically, leading to reliable recommendations and broader adoption across teams.

03

AI tools become part of daily workflows in sales, service and procurement — driving measurable business impact.

Step 1

Ingest and Harmonize Data: Consolidate and standardize product, supplier, and customer data across systems.

Step 2

Apply Ontology: Tag data with Knowde’s domain-specific taxonomy to create structure and relevance.

Step 3

Enrich Records: Extract and fill in missing attributes from TDS, SDS, and other technical documentation.

Step 4

Connect Entities: Build a knowledge graph linking materials, customers, documents, and applications.

Step 5

Enable AI Use Cases: Feed structured, connected data into LLMs, copilots, and internal tools to unlock trusted, scalable AI.