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RAG & AI Data Engineering

Production-grade retrieval pipelines that give your AI systems accurate, trustworthy context.

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What You Gain

  • A production-grade vector database indexed from your actual source systems — not a demo on sample data.
  • Measurably higher retrieval precision and recall compared to your baseline, benchmarked using your own query set.
  • A chunking and embedding strategy documented and justified for your specific document types and query patterns.
  • Hybrid search and reranking configured to handle the edge cases that break naive vector-only retrieval.
  • An observability layer that surfaces retrieval quality degradation before it reaches end users.
  • A re-indexing pipeline and schedule that keeps retrieved context current as source documents change.

What We Deliver

  • RAG Architecture Design Document (vector store, chunking, embedding, retrieval, reranking choices with rationale)
  • Document Processing and Ingestion Pipeline (deployed, documented, and tested)
  • Retrieval Evaluation Report (benchmark metrics before and after tuning)
  • Hybrid Search and Reranking Configuration (with tuning notes)
  • Observability Dashboard (retrieval quality metrics, alerting thresholds)
  • Operational Runbook (re-indexing procedures, monitoring checks, incident response for pipeline failures)

This Service Is Right for You If…

You have built a RAG prototype that works well on clean test data but returns wrong or inconsistent answers on real queries.
Your AI application handles sensitive data that cannot leave your infrastructure, and you need an on-premises or Canadian-cloud vector database deployment.
Your source documents are in messy real-world formats — scanned PDFs, inconsistently structured Word documents, legacy database exports — and standard chunking approaches are failing.
You are spending engineering time debugging AI output quality and the root cause is in the retrieval layer, not the model.
Your AI system's answers are becoming less accurate over time because source documents have changed and the index has not been updated.

Frequently Asked Questions

What is RAG and why does it matter for my business?

RAG stands for Retrieval-Augmented Generation. It is the technique of giving a language model access to your organisation's specific documents and data at query time. Without RAG, an AI assistant cannot answer questions about your policies or client history. With a well-engineered pipeline, the model retrieves relevant documents from your systems and grounds its answer in your actual data.

Can we just use the built-in search feature in Microsoft Copilot or our AI platform?

Built-in connectors handle straightforward cases on clean, well-structured data. They tend to underperform on large document libraries, specialised vocabulary, or mixed document quality. If you are seeing hallucinations, inconsistent answers, or retrieval misses on queries that a human could answer correctly, the built-in retrieval layer is likely the bottleneck.

Our data is sensitive — can you build this without sending it to US servers?

Yes. Mitiksha designs RAG pipelines for Canadian data residency requirements. Options include on-premises deployment, Azure Canada Central, or AWS Canada (Central). Embedding models can be hosted locally using open-source options. Data residency requirements are assessed during the architecture phase.

What is the difference between chunking and embedding?

Chunking is how you split documents into segments that will be indexed individually. Embedding is how you convert each chunk into a numerical vector capturing its semantic meaning. Both decisions directly affect retrieval quality. Chunks that are too large dilute relevance signals; chunks that are too small lose context. Getting both right for your specific data is what distinguishes a production pipeline from a prototype.

How do we know if our RAG pipeline is actually working well?

Retrieval quality can be measured. Mitiksha uses standard metrics — context precision, context recall, and answer faithfulness — benchmarked against a query set derived from real questions your users will ask. You receive before-and-after metrics showing the improvement from tuning.

Last reviewed April 2026

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