---
title: "Enterprise Search, RAG & Knowledge Retrieval"
canonical_url: "https://cognilium.ai/services/rag-knowledge-retrieval"
slug: "services/rag-knowledge-retrieval"
section: "services"
page_type: "pillar"
date_modified: "2026-09-29"
description: "Ask in plain English, get sourced answers from your own documents and data. RAG, GraphRAG, NL2SQL and hybrid search, with every answer traceable to its source."
publisher: "Cognilium AI"
author: "Mudassir Marwat"
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answers:
  - "Four retrieval systems, four different hard parts"
  - "This audience rewards specificity"
  - "Retrieval quality is a discipline, not a vibe"
  - "The systems built on this practice"
entities: []
related:
---

# Enterprise Search, RAG & Knowledge Retrieval

**A temporal knowledge graph with evidence-based answers** For a family-office platform: a graph with valid-from and valid-to on its edges, so a partner can ask what the portfolio looked like on any past date. Every extracted value carries its exact source quote and page, and confidence comes from verifying that the quote exists — not from the model grading itself.


## In short

**A temporal knowledge graph with evidence-based answers** For a family-office platform: a graph with valid-from and valid-to on its edges, so a partner can ask what the portfolio looked like on any past date. Every extracted value carries its exact source quote and page, and confidence comes from verifying that the quote exists — not from the model grading itself.

**Hybrid retrieval engineered to the decimal** For a K-12 publisher: dense embeddings fused with keyword ranking by reciprocal-rank fusion, a candidate pool of fifty narrowed to ten, progressive filter relaxation across trait, mode and grade, and a five-category source hierarchy so the client's own books always outrank reference material.

**Natural language to SQL and API, delivered** For a parts manufacturer: plain-English questions answered from the ERP, sales channels, meeting recordings and scanned documents at once — multi-modal GraphRAG, in the chat tool the executives already used.

**Retrieval at support scale** In VORTA, our own support product: hybrid retrieval over knowledge bases, multi-turn and multilingual, where the cost of a wrong answer is a customer rather than a report.

**Hybrid search** Dense retrieval catches meaning; keyword retrieval catches the part number. Choosing one is choosing which half of your questions to answer badly.

**Reciprocal-rank fusion** The two result lists are merged by rank rather than by score, because their scores are not on the same scale and pretending otherwise quietly favours one.

**Progressive relaxation** Filters loosen in a defined order when nothing matches, so a narrow question degrades to a useful answer instead of an empty one.

**Temporal edges** Relationships carry validity dates, so the graph can answer what was true then, not only what is true now.

**Reranking and semantic caching** A reranker promotes the passage that actually answers the question; caching keeps the repeat questions cheap.

**An evaluation harness** Hand-written retrieval test questions, run on every change. Without them, retrieval quality is an opinion.

Service · Enterprise search & retrieval

A team asks a question and gets an accurate answer from its own documents, databases and knowledge.

Every answer traceable to where it came from, behind the access controls you already run.

Confidence comes from verifying the evidence, not from the model grading itself.

retrieval systems in production behind this practice


## Four retrieval systems, four different hard parts

Retrieval is not one problem. A portfolio question, a lesson plan, an ERP query and a support conversation each break in a different place.


## This audience rewards specificity

So here are the parts we actually tune, rather than a claim that we tune them.


## Retrieval quality is a discipline, not a vibe

On one platform: full classification accuracy on the test set and roughly 97% quote accuracy, with a multimodal change taking scanned-document extraction from about 12% to about 94%.

Those are project test results, not a promise about your corpus. What transfers is the harness that produced them: on another build, 2,092 automated checks run in under ten seconds on every change, with 106 live acceptance checks behind them. If retrieval is not measured, nobody can tell you whether a change made it better.


## The systems built on this practice

**Enterprise RAG search** One search interface replacing five, with a citation on every answer.

**Knowledge graphs and GraphRAG** When the answer is a traversal and similarity alone cannot reach it.

**Document intelligence** Where the corpus is documents nobody has structured yet.

Bring twenty questions your team cannot answer today

The top twenty questions are how we design retrieval: they decide the schema, the filters and the evaluation set. Bring them and we will tell you which are reachable from your data as it stands, and which need something fixed first.


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