RAG Strategy & Assessment
We assess your knowledge base, document types, query patterns, and accuracy requirements — designing a RAG architecture that matches the complexity of your use case and content.
Our Services
Retrieval Augmented Generation (RAG) Services
We build RAG pipelines that connect your knowledge base to a language model — enabling AI that answers accurately from your own documents, policies, and data without hallucination.
RAG Pipeline Design
Document Ingestion
Vector Search
Accurate Retrieval
Private & Secure
Overview
AI That Knows Your Business — Not Just the Internet
Large language models are extraordinarily capable — but they have a fundamental limitation: they do not know your business. They do not know your policies, your product documentation, your contracts, your internal procedures, or your proprietary data. Without this context, LLMs hallucinate, give generic answers, and fail to deliver the specific, accurate responses your users need. Retrieval Augmented Generation solves this problem. RAG connects your knowledge base to a language model — retrieving the most relevant information from your own documents at query time and providing it as context, enabling accurate, grounded, and verifiable AI responses.
At AI Consultants, we design and build production-grade RAG pipelines that ingest your content, structure it for retrieval, and connect it to the right LLM — delivering AI that genuinely knows your business and answers with accuracy you can trust.
Get In Touch →
What We Offer
RAG Services
Production-grade RAG pipelines from strategy through deployment and continuous evaluation.
Document Ingestion & Processing
We build pipelines that ingest your documents — PDFs, Word files, web pages, databases, and APIs — extracting, cleaning, and structuring content for optimal retrieval performance.
Intelligent Chunking & Indexing
We design chunking strategies that preserve semantic context within document segments — ensuring retrieval returns the most relevant content rather than arbitrary text fragments.
Vector Embedding & Search
We generate high-quality embeddings for your document chunks and build vector search infrastructure — enabling semantic retrieval that finds relevant content by meaning, not just keyword matching.
LLM Integration & Prompt Design
We connect your retrieval system to the right LLM and design prompts that ground responses in retrieved context — minimising hallucination and maximising answer accuracy and relevance.
Retrieval Quality Optimisation
We continuously evaluate and optimise retrieval quality — testing chunk sizes, embedding models, retrieval parameters, and re-ranking strategies to maximise the accuracy of retrieved context.
Private & Secure RAG Deployment
We deploy RAG systems within your security perimeter where required — using local embedding models and self-hosted vector stores to ensure your documents never leave your infrastructure.
RAG Monitoring & Evaluation
We build evaluation frameworks that continuously measure answer accuracy, retrieval relevance, and hallucination rates — ensuring your RAG system maintains high quality as your knowledge base evolves.
Why Us
Why Choose AI Consultants?
✓ Production-Grade RAG Architecture
✓ Any Document Type
✓ Retrieval Quality Obsession
✓ Private Deployment Available
RAG Architecture Depth
Building a basic RAG pipeline is straightforward. Building one that retrieves accurately at scale, handles diverse document types, and maintains quality as your knowledge base grows requires deep architectural expertise — which is exactly what we provide.
Retrieval Quality First
Most RAG failures are retrieval failures — the right answer exists in the knowledge base but the wrong chunks are retrieved. We obsess over retrieval quality, testing and optimising chunking, embedding, and ranking strategies until retrieval is consistently accurate.
Any Knowledge Base, Any Format
We ingest PDFs, Word documents, PowerPoints, web pages, databases, Notion workspaces, Confluence, SharePoint, and custom APIs — building unified knowledge bases from the diverse content formats that real organisations actually use.
Verifiable, Trustworthy Answers
Every answer our RAG systems produce includes source attribution — citing the specific document and section used to generate the response. This makes AI answers verifiable and builds the user trust that generic LLM outputs cannot achieve.
Our Stack
Technologies We Use
Vector databases, embedding models, and RAG frameworks for accurate knowledge retrieval.
Pinecone / Weaviate
Cloud vector databases
pgvector / Chroma
Embedded vector search
OpenAI Embeddings
text-embedding-3 models
Open Source Embeddings
sentence-transformers, BGE
Document Processing
Unstructured, LlamaParse, PyMuPDF
LangChain / LlamaIndex
RAG orchestration frameworks
OpenAI / Anthropic
Answer generation LLMs
RAGAs / TruLens
RAG evaluation frameworks
AWS / Azure / GCP
Scalable RAG infrastructure
How We Work
Our Process
Step 01
Knowledge Base Assessment
Audit your documents, content types, query patterns, and accuracy requirements — defining RAG architecture and retrieval strategy.
Step 02
Ingestion Pipeline Build
Build document ingestion, extraction, cleaning, chunking, and embedding pipelines for all your content types.
Step 03
Vector Store Setup & Indexing
Configure vector database, index all document chunks, and build semantic search infrastructure.
Step 04
LLM Integration & RAG Tuning
Connect retrieval to LLM, design grounding prompts, and optimise retrieval parameters for maximum answer accuracy.
Step 05
Evaluation, Deployment & Monitoring
Deploy with evaluation framework, monitor answer quality and hallucination rates, and continuously improve retrieval performance.
Engagement Models
Choose From Our Hiring Models
Select the model that best fits your RAG project needs.
Dedicated Team
Our dedicated team works independently on your RAG engagement — with project managers, engineers, and QA delivering accurate, timely solutions.
- Agile processes
- Transparent pricing
- Maximum flexibility
- Monthly payments
- Ideal for startups and product companies
Team Augmentation
Augmented specialists join your team for RAG delivery — participating in standups and scaling instantly on demand.
- Scale on-demand
- Swift and cost-effective
- Monthly payment options
- Secure from hiring hassles
- Flexible skillset alignment
Project Based
Fixed-scope RAG delivery with defined milestones — ideal for well-defined projects with clear requirements and timelines.
- Fixed scope and budget
- Milestone-based delivery
- Agile processes
- Transparent pricing
- Suitable for all company sizes
FAQ
Frequently Asked Questions
RAG connects a language model to your knowledge base at query time — retrieving relevant content and providing it as context for the answer. Fine-tuning bakes knowledge into the model weights. RAG is better for knowledge Q&A because it keeps answers grounded in your actual documents, provides source attribution, stays current as your knowledge base updates, and is far more cost-effective than retraining a model every time content changes.
Well-built RAG systems achieve 90-95%+ accuracy on domain-specific queries when the knowledge base is comprehensive and the retrieval pipeline is properly optimised. Accuracy depends heavily on chunking strategy, embedding quality, retrieval parameter tuning, and prompt design — all of which we focus on extensively.
Yes. We design private RAG deployments where documents are processed and stored entirely within your infrastructure — using local embedding models and self-hosted vector databases. LLM inference can also be handled locally using open-source models, ensuring no content leaves your security perimeter.
We ingest PDFs, Word documents, PowerPoints, Excel files, HTML web pages, plain text, Markdown, Notion databases, Confluence pages, SharePoint content, database records, and custom APIs — building a unified knowledge base from the diverse formats your organisation actually uses.
We build automated ingestion pipelines that continuously monitor your content sources for changes — re-processing and re-indexing updated documents automatically so your RAG system always reflects the current state of your knowledge base without manual intervention.