Document Intelligence
Ingest PDFs, Word documents, policies, contracts, and manuals into a searchable RAG system that answers questions with citations.
RAG Development
AI That Answers From Your Data, Not Its Imagination
Retrieval-Augmented Generation connects your documents, databases, and knowledge bases to a language model — so every answer is accurate, sourced, and up to date.
50+
Clients
100+
Projects
5+
Years
98%
Satisfaction
What We Do
A focused approach to real results
The biggest problem with off-the-shelf AI is hallucination — it confidently answers from training data that may be outdated, wrong, or irrelevant to your business. RAG solves this by retrieving the relevant information from your actual data sources before generating a response. Foundrex builds production RAG systems that ingest your documents, build searchable vector indexes, and deliver accurate, cited answers grounded in your proprietary knowledge.
Services Included
What's inside this service
Knowledge Base AI
Transform your internal wiki, Notion, Confluence, or SharePoint into an AI assistant that any employee can query in plain language.
Customer Support RAG
Build a support bot that answers from your actual product documentation, FAQs, and historical tickets — not hallucinated responses.
RAG Pipeline Engineering
Technical build of the full pipeline: chunking strategy, embedding model selection, vector database setup, retrieval tuning, and LLM connection.
RAG Evaluation & Optimisation
Measure and improve an existing RAG system's accuracy, retrieval precision, and answer faithfulness using automated evaluation frameworks.
Why Foundrex
Built different, for a reason
Chunking and retrieval done right
Most RAG failures come from poor chunking and naive retrieval. We design chunking strategies and hybrid search to maximise relevant retrieval.
Citation-backed answers
Every answer from our RAG systems includes source references so users can verify the response and trust the output.
Vector database expertise
We have production experience with Pinecone, Weaviate, Qdrant, and pgvector, and we select the right one for your scale and infrastructure.
Continuous improvement loop
We build evaluation pipelines that measure answer quality over time so you know if the system is improving or drifting.
How We Work
Our process, from brief to launch
01
Data audit
We assess your documents, databases, and knowledge sources — format, quality, volume, and update frequency.
02
Pipeline design
We design the ingestion pipeline, chunking strategy, embedding approach, and vector store architecture.
03
Build & index
We build the pipeline, ingest your data, and create the initial vector index.
04
Retrieval tuning
We test retrieval quality across a range of real queries and tune chunking, embedding, and search parameters.
05
Deploy & evaluate
We deploy the system, connect it to your LLM, and set up ongoing evaluation to track answer quality over time.
Common Questions
What people ask us
PDFs, Word documents, Excel files, PowerPoint presentations, HTML pages, Markdown files, Notion exports, Confluence pages, and database records.
ChatGPT's file upload is limited to single sessions and small files. A production RAG system handles millions of documents, persists across sessions, integrates with your auth system, and scales with your organisation.
We implement graceful fallback — when the retrieval step finds no relevant content, the system says so rather than hallucinating. This is a design requirement, not optional.
A focused RAG system for one document type takes 4–6 weeks. A multi-source enterprise knowledge base with access controls and evaluation tooling takes 3–4 months.
Stop Feeding Your AI Outdated Information
Book a free consultation. We'll audit your data sources and design a RAG architecture that works.
Book a Free Consultation →