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Core-AI

Document Intelligence

Table of Contents
Turn unstructured documents into a searchable, AI-ready knowledge base. Core-AI builds end-to-end Retrieval-Augmented Generation (RAG) pipelines that transform PDFs, wikis, and databases into structured retrieval systems your LLMs and agents can reason over.

What We Build
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Most enterprise content lives in formats LLMs can’t consume directly — scanned PDFs, version-locked Confluence wikis, SharePoint silos, ticketing exports. We build the pipeline that bridges that gap:

  1. Ingestion — connectors for the systems where your documents already live.
  2. Processing — OCR, layout-aware parsing, chunking strategies tuned to your content type.
  3. Embedding — generate dense vector representations using open-weight embedding models.
  4. Storage — a private vector database (Qdrant, Weaviate, pgvector) sized to your corpus and query volume.
  5. Retrieval — hybrid search (semantic + keyword + metadata filters) for accurate top-k results.

Everything runs on your infrastructure. Your documents and embeddings stay inside your network.

Outcomes Our Clients Realize
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  • Faster knowledge discovery — Reduce the time spent searching for information by up to 90%.
  • Automated report generation — Summarize and synthesize raw documentation into briefs, contracts, and audit reports.
  • Semantic search across silos — Move beyond keyword matching to true conceptual understanding of your content.
  • Compliance-grade traceability — Every retrieved chunk is auditable back to its source document, page, and section.

Processing Pipeline
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graph LR
  Docs((Docs)) --> Proc((Processing))
  Proc --> Emb((Embeddings))
  Emb --> Vect((Vector
DB))
  Vect --> Resp((Response))

  classDef n1 fill:#3b82f6,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n2 fill:#6366f1,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n3 fill:#8b5cf6,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n4 fill:#a855f7,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n5 fill:#c084fc,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;

  class Docs n1;
  class Proc n2;
  class Emb n3;
  class Vect n4;
  class Resp n5;

Content Types We Handle
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  • PDFs — including scanned documents (OCR), forms, technical manuals, and contracts.
  • Wikis & docs — Confluence, Notion, SharePoint, internal Markdown repositories.
  • Code & technical content — Git repositories, API documentation, runbooks.
  • Structured data — CSV, JSON, SQL exports — chunked and embedded for hybrid retrieval.
  • Email & messages — archived correspondence indexed for compliance lookups.

Related Services #

Frequently Asked Questions
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What document formats do you support for ingestion?
We ingest PDFs (including scanned documents via OCR), Microsoft Office files (Word, Excel, PowerPoint), Confluence and Notion pages, SharePoint content, Markdown repositories, CSV and JSON exports, and archived email. If your content lives somewhere, we build a connector for it.
What is RAG and why does it matter for enterprise document search?
Retrieval-Augmented Generation (RAG) grounds an LLM’s responses in your actual documents rather than its training data. Instead of relying on the model’s memory — which may be outdated or hallucinate — RAG retrieves relevant passages from your knowledge base in real time and uses them as context. The result is accurate, up-to-date answers with traceable sources.
How do you ensure retrieved information is accurate and not hallucinated?
RAG architectures reduce hallucination by constraining the model to retrieved content. We also implement confidence scoring, source attribution (every answer cites its source document and section), and evaluation frameworks that measure retrieval precision and answer faithfulness before launch.
Can the system handle documents in multiple languages?
Yes. We configure multilingual embedding models that represent documents across languages in a shared semantic space, enabling cross-lingual retrieval. Users can query in one language and retrieve relevant content from documents in another.
Discuss Your RAG Project