AI knowledge infrastructure
You want AI that actually delivers. Not applications running on scattered data, sources that don't add up or knowledge that never comes together. But a knowledge layer that holds up: current, structured and set up for the way AI works. An AI knowledge infrastructure gives you the foundation on which your AI applications deliver value for good.
Documents, systems, knowledge bases, reports. Many organisations have more data than they think. But that data sits in different places, isn't aligned and is barely usable for an AI model. Models get let loose on data nobody can trace or date any more. The result: AI applications that give answers nobody trusts.
The problem isn't the AI model, it's the knowledge infrastructure underneath it. Without a structured, AI-ready layer for models to draw on, every AI initiative stays stuck in pilots and proofs of concept that never make it to production.
AI knowledge infrastructure solves that, by building the knowledge layer your AI applications can run on. Dependable, auditable and scalable.
What does AI knowledge infrastructure cover?
RAG implementation & vector database
Give your AI access to the knowledge your organisation already has. From your own documents, knowledge bases and data sources. We build knowledge infrastructures in which the AI system always shows where an answer comes from. Which document, which passage, which source. So knowledge stays auditable and can always be traced back. For users, for administrators and for regulators. Explainable AI isn't an optional extra, it is a standard part of how we build.
Enterprise Knowledge Graph
Your organisation knows more than separate documents and data sources show. An Enterprise Knowledge Graph brings out the connections: between customers, contracts, processes and people. So your AI understands not only what something says, but how everything hangs together. The foundation for knowledge assistants and decision support that understand how your organisation fits together.
AI Knowledge Infrastructure
An AI knowledge infrastructure takes more than separate components. It takes a coherent platform on which vector databases, knowledge layers and AI models work together securely and at scale. Indicia builds and manages that infrastructure as one whole, matched to your knowledge landscape and ready to scale with you.
What does an AI knowledge infrastructure deliver?
Your AI works with reliable, current knowledge from your own organisation. Not generic answers, but answers that are right for your context.
Every answer can be traced back to its source. Users, administrators and regulators always know what an AI outcome is based on.
AI initiatives that are stuck in pilots get the technical base they need to become production-ready.
You roll out new AI applications faster, because the knowledge foundation is already there and built to scale.
Knowledge doesn't walk out of the door when people leave. The knowledge infrastructure holds on to what your organisation knows.
Why Indicia?
Indicia is a tech, data and AI partner that helps clients stay ahead in the AI era. We think strategically with you, build what is needed and support the people who work with it. For results in weeks, not months. With privacy and security as the foundation. Driven by senior experts who are always right beside you.
Building knowledge infrastructures for AI takes more than technical expertise. It takes an understanding of how knowledge flows through organisations, who works with it and which requirements apply around auditability and data management. We bring that combination to every new assignment.
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Proven expertise
30+ years in data, AI and knowledge architecture
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End-to-end
From knowledge analysis and architecture to build, implementation and further development
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Dependable
ISO 27001 certificate and ISAE 3000A Type II report
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A vector database stores text, documents and other information as mathematical representations. That means an AI model searches by meaning rather than by keyword. A question about customer churn will also find relevant documents about customer retention or cancellations, even when those exact words do not appear.
A vector database is strong at retrieving relevant information semantically from large amounts of text. An Enterprise Knowledge Graph also records the relationships between concepts, entities and processes explicitly. Together they make a powerful knowledge infrastructure: the vector database retrieves, the knowledge graph understands how things connect.
RAG stands for Retrieval Augmented Generation. It lets an AI model pull relevant information from your own knowledge sources while it generates an answer. So your AI answers from what your organisation knows, not from a generically trained model.
No. If data is scattered or unstructured, that is exactly the starting point. We begin with an analysis of your knowledge landscape and decide together where the biggest opportunities are.
We build knowledge infrastructures where every answer can be traced back to its source. We also embed the agreements about data management, access rights and auditability in the infrastructure itself. That way control does not depend on who happens to be working that day.
Yes. An AI knowledge infrastructure is especially valuable as a starting point. You build the basis that future AI applications can run on straight away, without having to set everything up again later.