Data that gains
meaning and reliability
Having centralized data is not enough. For it to become truly useful, it must be understood, structured and enriched. Too often, raw data remains unusable: inconsistent formats, duplicates, missing information or data disconnected from its business context.
Rosecape transforms your data into a strategic asset. We clean it, model it according to your business reality, and enrich it with the context needed so it speaks the same language as your organization. Your data becomes consistent, traceable and robust — fully ready for artificial intelligence.
This is what we call contextual AI: intelligence grounded in your company's reality, not a statistical average of the web.
Why context changes everything
for AI
General-purpose AI models — ChatGPT, Copilot, the assistants embedded in your tools — don't know your business. They respond based on a statistical average of the web. For generic questions, that works. For business decisions, it's a risk: approximate answers, hallucinations, and no trace of which source was used.
Contextualization transforms your raw data into an asset AI can understand. We model your business entities (customers, products, contracts, operations) into a graph that describes how they relate in your specific context. We enrich each piece of data with the metadata AI needs to produce precise, traceable answers.
This is what we call contextual AI: intelligence that knows "customer" has a specific meaning at your company, that your products have specific relationships, and that your processes follow rules that aren't written on Wikipedia.
Five pillars for contextualizing
your data
From raw transformation to semantic enrichment, each step brings your data closer to its full potential.
Data transformation
Convert, normalize and harmonize your raw data into usable formats.
Data model
Structure your data using business models tailored to your industry.
Advanced AI contextualization
Enrich your data with metadata and semantic context.
Ontology
Create controlled vocabularies and shared taxonomies.
Data integrity and governance
Ensure the quality, traceability and compliance of your data.
Frequently asked questions
What does "contextualizing data" mean concretely?
Five things: clean inconsistent formats, deduplicate, model the relationships between business entities (what's a customer at your company, how do they relate to your products), enrich with metadata (creation date, source, reliability), and document the business rules that apply. The result is a structured asset AI can query precisely.
Do we need a data science team for this?
No. Rosecape provides pre-configured models for common sectors (manufacturing, services, distribution, etc.) that we adapt to your reality. Contextualization isn't a months-long project; it's a layer the platform builds progressively from your existing data.
Is the context graph locked into Rosecape?
No. The context graph is exportable in open standard formats. Your models, your ontologies, your enrichments belong to you and remain portable if you change platforms.
How does Rosecape compare to a plain "RAG" setup for AI on documents?
RAG (retrieval-augmented generation) finds similar text in your documents and passes it to the AI. Useful but limited: no model of relationships between entities, no structured reasoning. Rosecape's contextualization goes further — it adds a modeling layer that lets the AI reason about business entities, not just retrieve fragments of text.
Which data is contextualizable?
All data that describes entities or events in your business: customers, products, transactions, contracts, communications, internal documents. Purely operational data (technical logs, infrastructure metrics) benefits less from contextualization and stays in raw format.
Ready to take back control
of your data?
The Rosecape platform is designed to integrate easily with your current systems, without disrupting your operations. Within weeks, you could have a unified view of your business.