Data & AI Engineering (RAG, Vector DBs)
LLMs are only as good as the context fed into them. We architect resilient data ingestion pipelines, hybrid keyword/vector search engines, hierarchical chunking protocols, and self-hosted embedding caches that turn fragmented enterprise data into instantaneous, verifiable intelligence.
Architectural Blueprint & Engineering Approach
Naive RAG implementations that chunk text blindly at 500 characters and rely purely on cosine distance consistently fail in production. They fail to understand tabular data, lose document context, and surface irrelevant noise that leads directly to model hallucination.
We build advanced enterprise RAG systems utilizing hybrid search (combining dense embeddings from models like BGE-Large with sparse lexical BM25 indexing via Qdrant or Pinecone). Every query passes through contextual document rerankers, parent-child hierarchical chunking, and strict metadata filters, guaranteeing sub-100ms retrieval and 99%+ factual precision with verbatim source citations.
4-Phase Engineering Delivery Lifecycle
Corpus Audit & Data Cleaning
Auditing data sources (Confluence, SharePoint, SQL, PDFs) to cleanse redundant, outdated, and trivial files.
Chunking & Embedding Topology
Benchmarking embedding models, establishing hierarchical chunking schemas, and testing table extraction.
Vector Clustering & Reranking
Configuring Qdrant or Pinecone vector clusters, setting up hybrid BM25 indexes, and tuning rerankers.
CDC Ingestion & Evaluation Suite
Deploying automated real-time ingestion pipelines and verifying retrieval metrics (MRR, MAP@k) on production data.
Technical Scope & System Capabilities
Hybrid search architectures (Dense Semantic Vectors + BM25 Lexical Matching)
Contextual cross-encoder reranking with Cohere Rerank 3 and BAAI/BGE models
Hierarchical semantic chunking and multi-page table reconstruction algorithms
Real-time incremental data ingestion via Change Data Capture (CDC) pipelines
Sub-100ms vector index querying across multi-million document repositories
Verbatim source attribution linking generated claims directly to underlying PDFs
Commercial & Architectural Advantages
Elimination of Hallucinations
Rigorous hybrid retrieval and cross-encoder filtering guarantee models only generate grounded facts.
Enterprise Context Preservation
Hierarchical chunking preserves table headers, section titles, and footnotes across vast documents.
Sub-Second Enterprise Search
Empower staff to query decades of institutional knowledge and receive instantaneous, cited answers.
Production Deliverables & IP Transfer
Primary Technology Stack
Fixed-Scope Sprints or Autonomous Pods
We guarantee zero junior staff, UK GDPR data sovereignty, and full transfer of all code and model artifacts.
Book Service ConsultationReady to Transition from Legacy Operations to Autonomous AI Efficiency?
Schedule a 45-minute confidential architectural consultation with our London Principal AI Architect. We will dissect your bottlenecks, assess feasibility, and outline an actionable implementation roadmap.