RAG & LLM Architecture
Done Right
The difference between a demo and a production RAG system is enormous. We help engineering teams design retrieval pipelines, select models, tune chunking strategies, and build evaluation frameworks that actually work at scale.
Architecture Deliverables
Technical artefacts your engineering team can implement immediately.
System Architecture Document
End-to-end design covering ingestion, chunking, embedding, vector storage, retrieval, reranking, and generation layers.
Chunking & Embedding Strategy
Optimal chunk sizes, overlap ratios, and embedding model selection benchmarked against your specific document corpus.
Vector Store Selection Guide
Comparative analysis of Pinecone, Weaviate, Qdrant, pgvector, and others — with a recommendation for your scale and budget.
Retrieval Pipeline Design
Hybrid search configuration combining dense and sparse retrieval with reranking logic and fallback strategies.
Evaluation Framework
Automated test harnesses measuring retrieval precision, answer faithfulness, and hallucination rates with golden datasets.
Cost Modelling Workbook
Token-level cost projections across different LLM providers with recommendations for caching, batching, and model routing.
Our Advisory Process
Corpus Analysis
We analyse your document types, volumes, update frequencies, and access patterns to inform every downstream design decision.
Prototype & Benchmark
We build a lightweight prototype to test chunking strategies, embedding models, and retrieval configurations against your real data.
Architecture Design
Based on benchmark results, we design the production architecture with clear technology choices and trade-off documentation.
Evaluation Setup
We create golden test sets and automated evaluation pipelines so your team can measure quality continuously after handoff.
Implementation Handoff
Detailed design docs, reference code, and a backlog of engineering tickets ready for your team to execute.
Building a RAG System? Get the Architecture Right First.
A few weeks of design advisory can save months of rework. Let us review your approach or design one from scratch.
Schedule Free ConsultationWho This Is For
Engineering teams building knowledge-intensive AI applications.
FinTech Platforms
Build compliant Q&A systems over regulatory documents, policy manuals, and customer agreements.
Legal Tech Teams
Design retrieval systems for case law, contracts, and compliance documentation with citation accuracy.
Health Tech Builders
Architect RAG systems over clinical guidelines, drug databases, and patient records with strict accuracy requirements.
EdTech & Knowledge Platforms
Create intelligent tutoring and knowledge retrieval systems that surface accurate, contextual answers.
Why OpenMalo for RAG & LLM
We have built enough RAG systems to know where they break — and how to prevent it.
Get Expert RAG Architecture Guidance
Tell us about your use case and document corpus. We will assess whether RAG is the right approach and how to design it.
RAG System Cuts Compliance Query Time by 80%
Payment Processor Builds Regulatory Q&A System
A payment processing company needed their compliance team to query thousands of regulatory documents instantly instead of manually searching PDFs for hours.
The Challenge
The compliance team spent hours searching through regulatory PDFs and internal policy documents to answer routine queries.
Our Approach: We redesigned the chunking strategy to preserve document structure, implemented hybrid search with BM25 and dense retrieval, added a reranking layer, and built an evaluation harness with 500 golden question-answer pairs. Accuracy jumped from 61% to 94.2%.
Frequently Asked Questions
Our core offering is architecture advisory and design. We can build a working prototype during the engagement, and offer implementation support as an add-on if your team needs hands-on help.
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