How do you handle document permissions in RAG?

Access control lists are stored with each chunk and filtered at query time using the user's identity, so the model never receives passages the user is not allowed to read.

Does RAG eliminate hallucinations?

It reduces them substantially but does not remove them. Citation requirements, refusal when evidence is weak and faithfulness checks during evaluation keep the remaining risk visible and measurable.

How long does it take to build a production RAG pipeline?

For a corpus of a few thousand documents, a measured production pipeline typically takes six to ten weeks, with document parsing quality usually driving the timeline.

Home
Services
Generative AI & LLM Engineering
Retrieval-Augmented Generation (RAG)

AI, Data & Intelligence

Retrieval-Augmented Generation (RAG)

Engineering of retrieval-augmented generation pipelines that ground language model answers in your documents, covering ingestion, chunking, hybrid retrieval, reranking, citations and systematic evaluation.

Schedule a Consultation Request a Proposal

Generative AI & LLM Engineering Service capability

Capability overview

What retrieval-augmented generation (rag) involves

Retrieval-augmented generation lets a language model answer from your own content by retrieving relevant passages at question time and passing them to the model with instructions to answer only from them. A basic RAG demo takes an afternoon. A pipeline that stays accurate across thousands of messy PDFs, spreadsheets and wiki pages takes careful engineering.

Most RAG failures are retrieval failures: the right passage was never found. We therefore invest in document parsing that preserves tables and headings, chunking aligned to document structure, hybrid keyword and vector search, reranking models and query rewriting, and we evaluate retrieval and answer quality separately using frameworks such as RAGAS.

Retrieval-Augmented Generation (RAG) delivery workshop

What is included

Pipeline components

Document ingestion

Parsing of PDFs, Office files, HTML and scanned documents with layout-aware tools, keeping tables, headings and page numbers intact.

Chunking and metadata

Content split along sections rather than fixed character counts, enriched with source, date, owner and access permissions.

Hybrid retrieval and reranking

BM25 keyword search combined with vector similarity, followed by a reranker that orders candidates by true relevance.

Grounded answering

Prompts that require citations, refuse when evidence is missing and distinguish between conflicting sources.

Evaluation harness

Question sets with expected sources measure retrieval recall, answer faithfulness and relevance on every change.

How we work

How we deliver retrieval-augmented generation (rag)

Corpus analysis

Document types, formats, volumes, update frequency and permission models reviewed.

Question set creation

Real questions collected from users and paired with the documents that contain the answers.

Baseline pipeline

A simple pipeline built and measured so every later improvement is proven with numbers.

Iterative improvement

Parsing, chunking, retrieval and prompts tuned one variable at a time against the evaluation set.

Freshness automation

Incremental indexing set up so new and changed documents are searchable within an agreed delay.

Related capabilities

Related capabilities in Generative AI & LLM Engineering

Enterprise Knowledge Assistants

An internal assistant that answers employees' questions about policies, procedures, products and past work in Microsoft Teams, Slack or the intranet, with cited sources and respect for document permissions.

Custom AI Assistants

Purpose-built AI assistants for a specific role or team, such as underwriters, relationship managers or field engineers, that combine your data, tools and procedures in ways off-the-shelf assistants cannot.

AI Copilots

AI copilots embedded inside your own software product or internal platform, helping your users complete tasks in context, with features designed, priced and governed as part of the product.

LLM-Powered Agents

LLM-Powered Agents within our generative ai & llm engineering services, scoped after a short discovery conversation.

Explore further

Related services

Generative AI & LLM Engineering

AI, data, analytics and automation applied to practical business work. Acmez supports…

Where this applies

Healthcare & Life Sciences

Technology systems for regulated environments where privacy, auditability and continuity…

Manufacturing & Industrial

Connected operations, asset, field, supply chain and industrial platforms for complex operating…

Banking, Financial Services & Insurance

Technology systems for regulated environments where privacy, auditability and continuity…

E-Commerce

Digital platforms for customer experience, operations, commerce, content, marketing and service…

Questions & answers

Questions about Retrieval-Augmented Generation (RAG)

Cannot find what you need? Our team responds to technical and commercial questions within one business day.

Ask a question

Common causes are poor PDF parsing that scrambles tables, chunks that split an answer from its context, pure vector search missing exact terms such as product codes, and no reranking. An evaluation set makes the cause visible.

Next step

Discuss retrieval-augmented generation (rag) with Acmez

Share what you need to change, build, integrate or support. We will map the practical next step.