Overview
Large language models are powerful, but they do not know your business. Without access to your policies, contracts, research, or internal data, they guess — and guessing in regulated industries is unacceptable. Retrieval-Augmented Generation solves this by connecting LLMs to your knowledge sources at query time, so every answer is grounded in the documents and data you trust. We design, build, and optimise RAG pipelines end-to-end — from document ingestion and chunking strategy through retrieval ranking to response generation and evaluation. The result is AI that knows your business and can prove it.
How It Works with a21
Knowledge Audit & Ingestion Design
Inventory your knowledge sources — documents, databases, APIs, wikis. Design the ingestion pipeline including preprocessing, chunking, and metadata enrichment strategies.
Embedding & Retrieval Architecture
Select and configure embedding models and vector databases. Design the retrieval strategy — dense, sparse, or hybrid — optimised for your query patterns and accuracy requirements.
Generation, Evaluation & Iteration
Connect the retrieval layer to your LLM with optimised prompting. Run systematic evaluation against your golden dataset and iterate until accuracy targets are met.
What We Offer
Document Ingestion & Preprocessing
Handle any document format — PDFs, Word, HTML, scanned images — with preprocessing that preserves structure, tables, and relationships.
Chunking Strategy Design
Design chunking approaches — fixed, semantic, hierarchical — matched to your document types and retrieval accuracy requirements.
Embedding Model Selection & Tuning
Select and fine-tune embedding models for your domain vocabulary, including domain-adapted embeddings for technical or regulated content.
Hybrid Retrieval
Combine dense vector search with sparse keyword retrieval (BM25) for best-in-class precision and recall across diverse query types.
Reranking & Context Optimisation
Apply cross-encoder reranking and context window optimisation to maximise the quality of context provided to the LLM.
RAG Evaluation Framework
Systematic evaluation of retrieval quality and generation accuracy using RAGAS metrics — faithfulness, answer relevance, context precision — with a golden dataset.
Why Choose a21
RAG for Regulated Industries
We build RAG systems that work in compliance-sensitive environments — with source citation, audit trails, and PII handling built in.
Evaluation-First Approach
We do not ship RAG systems without systematic evaluation. Every pipeline is tested against a golden dataset with quantified accuracy targets.
Production-Grade Architecture
Our RAG pipelines are built for scale — handling millions of documents, concurrent users, and sub-second retrieval latencies.
Domain Adaptation
We have built RAG systems for highly specialised domains — clinical trial data, legal contracts, financial regulations — where generic approaches fail.
Success Stories
Problem
A global bank needed staff to quickly locate relevant policy guidance across 15,000 internal compliance documents — a process that was taking hours and producing inconsistent answers.
Solution
Built a RAG system ingesting 15,000 documents with hybrid retrieval, source citation, and an evaluation framework tracking faithfulness and relevance scores.
Problem
Medical writers at a pharma company were spending 30% of their time locating and cross-referencing data across clinical study reports to draft regulatory submissions.
Solution
Deployed a RAG pipeline over structured and unstructured clinical documents with hierarchical chunking, table extraction, and domain-adapted embeddings.
Tech Stack & Tools
Pinecone / pgvector / Weaviate / Qdrant
LangChain / LlamaIndex
OpenAI / Anthropic / Cohere Embeddings
BM25 / Elasticsearch
RAGAS
Unstructured.io / PyMuPDF
Redis
Get Started
Build RAG that your business can trust. Talk to a21 about your knowledge retrieval requirements.
