AI-Ready Data & RAG Enablement
Making data, documents, and knowledge bases ready for AI use cases, large language models (LLMs), retrieval-augmented generation (RAG), and agents — from proof of concept (POC) to production
AI only works if it can access data that is reliable, governed, contextual, and up to date. We prepare data, documents, and knowledge bases for LLMs, RAG, and AI agents. We check which sources are ready, put them in order, and define who can see what. Then we build the first solution that delivers value, from proof of concept to production.
The service in brief
- Service
- Preparation of data, documents, and knowledge bases for LLMs, RAG, and AI agents, and delivery of the first solution.
- When you need it
- Before you expose your data to AI: when you want to query documents, procedures, tickets, contracts, or ERP data.
- R.O.C.E. phase
- Evolve: the fourth phase of the Miriade R.O.C.E. methodology (Recover · Optimize · Control · Evolve).
- Role
- The first step of the AI path, from POC to production.
- Starting point
- The use case that delivers value, not the technology.
- Output
- Source and RAG readiness assessment, target RAG architecture, preparation pipeline, access and governance model, and a first assistant as a minimum viable product (MVP).
- Continues with
- AI in Production Enablement and Continuous AI Managed Service (MirAI), after go-live.
The problem is not the model. It is the data.
AI rarely disappoints because of the model: today, that is the easy part. The problem is almost always something else — what the AI can actually read, and how far you can trust the answer. The right question is not “Which AI should we use?” but “Which data do we let it work on, to do what, under which rules, and with what reliability?”
How it works
We do not start from the technology. We start from where AI can save you time or money, and we work backward in six steps.
Use case triage Starting point
Before touching any data, we work out what the AI must do and what it is worth: who will use it, which questions it must handle, which sources it needs, what must not be exposed, and what stage the initiative is in (POC, MVP, reuse, production). We identify high-impact use cases and rank them by priority and feasibility.
Source and data assessment
We analyze the documents, databases, knowledge bases, and systems involved. We deliver a source map with issues, priorities, gaps, risks, quick wins, a readiness rating, and initial architecture guidance. We separate what is ready, what needs cleansing, and what must be excluded.
Data and RAG architecture design
We define how sources will be prepared, indexed, queried, and governed: document pipeline, data layer, metadata, chunking, embedding, vector store, retrieval, access controls, logging, monitoring, evaluation framework, and application integration.
Progressive implementation
We proceed in controlled scopes — one knowledge base, one data domain, one operational use case, one first assistant — so we can verify value and quality on a small scope before extending it.
Testing, evaluation, and improvement
We measure retrieval relevance, answer accuracy, citation quality, permission compliance, response times, and costs. We fix recurring errors, missing content, and source drift, comparing before and after.
Ongoing governance Over time
We define how to keep sources and knowledge bases reliable over time: updates and reindexing, handling of outdated documents, metadata and policy reviews, feedback loops, periodic readiness assessments, and coordination across data, AI, business, and security teams.
What we can work on
The scope adapts to the use case and the stage, from the initial assessment to the delivery of the first solution. Some of the areas we work on:
AI-Ready Data Assessment
Mapping and evaluation of sources against AI use cases: quality, access, issues, risks, quick wins, priority sources, and a preparation roadmap.
RAG Readiness Assessment
A check of whether documents, data, and knowledge bases are ready for RAG: usable sources and sources to exclude, metadata gaps, access issues, and guidance on chunking, embedding, and retrieval.
Continuous AI Data Readiness
An ongoing process to verify that data and sources remain fit over time: baseline, quality metrics, freshness and drift checks, data observability, and periodic reports.
AI Data Reuse & Multi-use-case
An assessment of reusing the same data assets across multiple use cases, with analysis of risk, semantics, metadata, and compatibility between data, AI technique, and context.
Document preparation for RAG
Selection, deduplication, cleansing, format normalization, classification, metadata enrichment, and indexing rules, through to embedding preparation.
Structured and unstructured data integration
We connect documents, databases, data warehouses, data lakes, data platforms, APIs, legacy systems, document repositories, data catalogs, and Denodo.
RAG architecture design
Data pipelines, document processing, vector databases, embedding, retrieval strategy, prompt orchestration, context engineering, access control, logging, monitoring, and evaluation.
Metadata, catalog, and governance
A metadata model that serves AI: source classification, owner, data domain, confidentiality, lineage, freshness, access policies, and links to the data catalog.
Rich Semantics, Knowledge Graph, GraphRAG
Taxonomies, ontologies, knowledge graphs, and semantic enrichment that give models more context and improve the quality, relevance, and explainability of answers.
Feature Engineering, Labeling, Synthetic Data
For predictive or highly automated use cases: data labeling, annotation, feature engineering, synthetic data, validation datasets, and bias and drift monitoring.
Answer quality evaluation
Test datasets, evaluation questions, relevance and accuracy criteria, citation checks, hallucination mitigation, and before/after benchmarks.
What makes the Miriade approach different
Miriade does not treat RAG as simple document indexing. We connect it to the company's real data ecosystem — documents, databases, data platforms, data catalogs, Denodo, metadata, access, security, governance, and DataOps. This is where reliable, traceable, sustainable AI comes from.
Many RAG projects fail not because of the model chosen, but because the sources are not ready.
From data to production: Miriade's AI services
AI-Ready Data & RAG is the starting point. From here, the path continues through the other AI services, when the solution needs to be optimized, stabilized, and managed over time.
AI-Ready Data & RAG
We prepare data and sources for AI and build the first solution.
You are hereAI in Production
We stabilize and govern AI that is already in production.
AI Context & LLM
We optimize the context, tools, and costs of agents and LLMs.
MirAI
Continuous monthly managed service for your AI system.
Frequently asked questions
What does “AI-ready data” mean?
It is not simply correct data. It is data fit for a specific AI use case, with quality, context, access, metadata, representativeness, security, and governance consistent with the risk and with the AI technique used.
How is it different from AI in Production Enablement?
AI in Production Enablement stabilizes and governs an AI solution that is already live. This service focuses on the prerequisite: making sources, data, documents, metadata, and access fit for LLMs, RAG, and agents.
Can we start from an existing knowledge base?
Yes. We analyze an existing knowledge base and check its quality, freshness, permissions, metadata, duplicates, and suitability for retrieval.
Do you also work on unstructured documents?
Yes: PDF, Word, and Excel files, internal web pages, manuals, procedures, tickets, contracts, and regulations, if authorized and relevant to the use case.
How do you handle permissions?
Retrieval honors company permissions: we design filters, access rules, source segregation, auditing, and integration with identity and authorization systems.
How does it start in practice?
From a use case and a source map: which documents or data are needed, who uses them, who governs them, which permissions exist, and which risks must be managed. From there, we define the assessment, architecture, and roadmap.
The technologies we use
Some of the technologies and platforms we work with on this service.
Want to make data and documents truly usable by AI?
We start from real sources, documents, databases, knowledge bases, metadata, permissions, and use cases, and build a path that makes information reliable, governed, traceable, and fit for AI in production.
Tell us about your use case
A few details are enough to frame the use case and the sources involved. We will contact you for a call and define the first step together.
Privacy and security note. Do not enter credentials, passwords, or sensitive data in the form. Any documents, data, or access needed for the assessment will be handled later through agreed secure channels.
