AI & Intelligent Solutions · Service 04 of 14

RAG Knowledge Assistant Development answers from your documents, with the source shown

O Clock Software, headquartered in Chennai, India, provides RAG development services: retrieval-augmented generation assistants that answer questions from your policies, manuals, contracts, tickets and knowledge bases. Every answer shows where it came from, and people only get answers drawn from documents they are allowed to open.

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  • 2009Founded in Chennai
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What we build

RAG Knowledge Assistants: what we build

Assistants that answer from your documents and show where each answer came from. Here are the six kinds of RAG development work we are asked for most often.

Inside a RAG pipeline

How a RAG assistant works, and where it usually goes wrong

Most weak RAG assistants fail at retrieval, not generation. We tune and test each of these stages separately against real questions from your users.

  1. 01

    Ingest

    Connect sources such as SharePoint, Google Drive, Confluence, PDFs and databases, and keep them in sync.

    Fails when: Stale or duplicate documents are indexed

  2. 02

    Chunk

    Split documents into passages that keep meaning intact, respecting headings and tables.

    Fails when: An answer is split across two chunks

  3. 03

    Embed and index

    Store passages as vectors and keywords, with metadata and access rights.

    Fails when: Permissions are not indexed

  4. 04

    Retrieve

    Hybrid vector and keyword search finds candidate passages.

    Fails when: Exact codes or product names are missed

  5. 05

    Re-rank

    A re-ranking model puts the most relevant passages first.

    Fails when: The best passage sits at rank twenty

  6. 06

    Generate

    The language model answers using only the retrieved passages.

    Fails when: It fills gaps with guesses

  7. 07

    Cite and learn

    Sources are shown, and unanswered questions are logged for review.

    Fails when: Nobody reviews the gaps

How we deliver

How we deliver RAG Knowledge Assistants

  1. 01

    Source inventory

    Which documents, where they live, who may see them and how current they are.

  2. 02

    Real question set

    Collect real questions from users to test against.

  3. 03

    Build and tune retrieval

    Chunking, hybrid search and re-ranking tuned until the target is met.

  4. 04

    Pilot with one team

    Live use, feedback buttons and weekly gap reviews.

  5. 05

    Expand

    Add sources and teams, with monitoring of answer quality.

Technology

Technology for RAG Knowledge Assistants

Chosen per project for fit, maintainability and your team's skills.

Vector & search

  • pgvector
  • Pinecone
  • Qdrant
  • Weaviate
  • Elasticsearch
  • OpenSearch

Frameworks

  • LlamaIndex
  • LangChain
  • Haystack

Models

  • OpenAI GPT
  • Anthropic Claude
  • Google Gemini
  • Meta Llama
  • Cohere Rerank

Connectors

  • SharePoint
  • Google Drive
  • Confluence
  • Notion
  • Zendesk
  • Databases

Straight answers

Is RAG Knowledge Assistants right for you?

We would rather recommend less than sell you more. Here is how we judge it.

A good fit when

  • Knowledge is spread across many documents
  • The same questions are asked again and again
  • Documents are reasonably current and owned
  • Answers must be traceable to a source

  • The answer is a number in a database

    Questions such as how many orders shipped last week need a database query or dashboard, not document retrieval.

  • Documents are outdated or contradict each other

    RAG will faithfully repeat bad sources. Content clean-up comes first.

  • There are only a handful of documents

    A well-organised FAQ or search page may be enough.

Use cases by industry

RAG Knowledge Assistants by industry

The same capability, shaped by the data, rules and users of each sector.

Related work

Where we have shipped this

Platforms in production that use this capability. See all work

Related services

Often combined with RAG Knowledge Assistants

Solutions built on this service

Prefer engineers in your own team?

The full service area

FAQ

RAG Knowledge Assistants FAQ

What is retrieval-augmented generation (RAG)?

Retrieval-augmented generation is a technique where an AI system first retrieves relevant passages from a trusted set of documents and then generates an answer based only on them. It reduces made-up answers and lets the system cite its sources.

Why use RAG instead of fine-tuning a model on our documents?

RAG keeps answers current as documents change, shows sources and respects who can see what, without retraining. Fine-tuning teaches style and format rather than facts and must be repeated whenever content changes, so many systems use RAG for facts and light fine-tuning for format.

Can a RAG assistant respect document permissions?

Yes. We index access rights with each passage and filter retrieval by the user's identity, so people only receive answers from documents they could open themselves.

Which document sources can you connect?

Common sources include SharePoint, OneDrive, Google Drive, Confluence, Notion, helpdesk platforms, PDFs, Word files, web pages and databases. Sources are synced on a schedule or in real time so answers stay current.

How accurate are RAG assistants?

Accuracy depends mostly on retrieval quality and document quality. We measure it on real questions before launch, tune chunking, search and re-ranking until it meets the agreed target, and monitor unanswered or low-rated questions after launch.

Can it answer from tables, scanned PDFs and images?

Yes, with the right ingestion. We use OCR for scanned documents, table-aware parsing for spreadsheets and reports, and image descriptions where diagrams carry meaning, then test those cases specifically.

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