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AI & Software

RAG (Retrieval-Augmented Generation) Applications

AI systems grounded in your own documents and data, so answers are accurate, current, and traceable to a source. That is the difference between a demo and something your team can rely on.

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Overview

Grounding answers in your own knowledge

Retrieval-augmented generation (RAG) connects a language model to your own knowledge (documents, wikis, databases, tickets) so it answers from your real information rather than from whatever it happened to memorise during training. This is what makes AI trustworthy enough to use for real work: answers are current, specific to your organisation, and traceable back to the source they came from. We build the full pipeline: ingesting and chunking your content, generating embeddings, storing them in a vector database, and retrieving the right context at query time so the model's answer is grounded. Getting RAG to work well is mostly engineering: retrieval quality, chunking strategy, handling of access permissions so users only see what they are allowed to, and evaluation to catch regressions. We handle those properly, including the data-governance and access-control concerns that come with pointing an AI system at your internal knowledge.

What's included

  • Answers grounded in your own documents and data
  • Sources cited so answers are traceable and verifiable
  • Full pipeline: ingestion, embeddings, vector store, retrieval
  • Permission-aware retrieval so users see only what they should
  • Evaluation to measure and maintain answer quality
Who it's for

Who needs answers that cite their source

  • Support and operations teams whose answers live scattered across wikis, PDFs, ticket histories and tribal knowledge
  • Professional services firms handling contracts, policies or case files where every answer must cite its source
  • Enterprises that trialled a generic AI assistant and abandoned it because it invented answers about internal processes
  • Product teams adding an in-app assistant that must answer from live product documentation, not stale training data
When you need it

Signs your knowledge is hard to find

  • New hires take months to become productive because the answers they need are buried across systems nobody has indexed
  • Your support team answers the same documented questions repeatedly, and deflection requires answers accurate enough to publish
  • Analysts spend hours locating the relevant clause or policy across thousands of documents before work can even begin
  • Documents carry different access levels, so any assistant must retrieve only what the asking user is permitted to see
  • You are comparing RAG against fine-tuning and need a system that reflects content updated weekly, not retrained quarterly
Deliverables

What gets deployed to your environment

Every engagement ends with documents and fixes your team can act on, rather than a presentation.

  • Working RAG application with ingestion pipeline, embeddings, vector store and retrieval, deployed to your environment
  • Documented chunking and retrieval strategy, including how the approach was chosen and what it trades off
  • Evaluation harness with a question-and-answer eval set so retrieval quality is measured, not assumed
  • Permission-aware retrieval wired to your identity provider, plus source citations rendered in the interface
  • Handover documentation covering re-indexing, adding sources, and monitoring for grounding failures
How it works

How we build the retrieval pipeline

The steps are the same whether the work is an assessment or a build, so you always know what happens next.

  1. 1

    Discover

    We start by understanding your systems, goals, and constraints, including scope, risk tolerance, and what success looks like, so the work is aimed at your problem rather than a generic template.

  2. 2

    Assess or build

    For security work, we test and analyse against recognised standards. For development, we build in small, reviewable increments. Either way, you see progress early and can change direction.

  3. 3

    Report or ship

    You get clear, prioritised deliverables, either a report your engineers can act on or working software shipped to your environment, with the context to understand what was done and why.

  4. 4

    Support

    We stay available after delivery: retesting fixes, iterating on the product, and answering the questions that come up once real users and real traffic arrive.

FAQ

RAG Applications: common questions

Can you review our RAG setup for free?

Yes, through the free 30-minute assessment. Most RAG systems that answer confidently wrong have a retrieval problem rather than a model problem: chunking that splits meaning apart, embeddings that do not suit the domain, no reranking, or no evaluation set to measure against. In half an hour a practitioner can usually identify which of those is costing you the most accuracy, and whether the fix is a weekend of work or a rebuild. Written findings follow.

What is retrieval-augmented generation, in practical terms?

RAG connects a language model to your own content at query time: the system searches your documents, retrieves the most relevant passages, and asks the model to answer using only those passages. The result is grounded in your real information and can cite the source. Without retrieval, a model answers from training data, which is generic and often confidently wrong about your organisation.

Should we use RAG or fine-tune a model on our data?

For answering questions about your own knowledge, RAG is usually the right choice. It handles content that changes, supports citations, and respects access permissions. Fine-tuning does none of these well, because it bakes information into weights and cannot easily forget or restrict it. Fine-tuning suits style, format and narrow classification tasks. The two complement each other.

Can a RAG system leak documents to users who should not see them?

It can, if permissions are ignored. This is the most common serious flaw we find in existing RAG builds. We enforce access control at retrieval time, filtering against your identity provider before any content reaches the model, so a user's query can only surface documents they are already entitled to. We also assess prompt injection risk in ingested content.

How do you know whether the RAG system's answers are good?

We build an evaluation set with you (real questions paired with correct answers and their source documents) and run it against the system automatically. That gives measurable retrieval and grounding scores rather than impressions, and it catches regressions when you change chunking, models or content. We do not claim a specific accuracy figure up front; we measure it on your data.
Related services

What pairs naturally with retrieval

Teams that come to Safe Tech AI for RAG applications frequently need these too.

  • AI Agents

    Multi-step, tool-using AI systems that complete tasks rather than only answering questions, designed with the guardrails, permissions, and human oversight that make autonomy safe to deploy.

    Learn more

  • Conversational AI & Bot Development

    Chatbots and workflow bots for support, sales, and internal operations. They are useful because they are grounded in your real content and connected to your real systems.

    Learn more

  • AI & LLM Security Testing

    Security testing for LLM applications, RAG systems and AI agents. We test for prompt injection, data leakage, unsafe tool use and broken access control, the ways AI features get abused in practice, and hand you reproducible findings with fixes.

    Learn more
Further reading

Guides on RAG applications

Stop letting good answers stay buried.

Bring us a knowledge source that's scattered across documents or systems, and we'll scope a RAG pipeline with citations and permission-aware retrieval.

Scope a RAG use case