AI That Answers From Your Data,
Not Training Weights
Document chunking, embedding pipelines, hybrid search, reranking, and source citation — we build RAG systems that give LLMs accurate, traceable access to your proprietary knowledge.
What we build
End-to-end RAG pipeline services
From raw document ingestion to production monitoring — every layer of a reliable, citation-first retrieval system covered.
Document Ingestion & Chunking
Format-aware ingestion for PDFs, Word, HTML, Notion, Confluence, and more — with chunking strategies (fixed, semantic, recursive, parent-document) chosen per corpus to maximise retrieval precision.
Embedding Pipelines
Embedding generation, versioning, and incremental update pipelines — so your index stays current as documents change without full re-embedding. Model selection benchmarked on your actual content.
Vector Database Architecture
Index design and deployment on Pinecone, pgvector, Weaviate, or Qdrant — with metadata schemas, namespace strategies, and filtering logic that matches your access control requirements.
Hybrid Search (Semantic + Keyword)
Reciprocal rank fusion of dense vector search and BM25 keyword search — combining semantic understanding with exact-match precision so neither query type is underserved.
Reranking & Retrieval Quality
Cross-encoder reranking that re-scores the top-k retrieved chunks against the query before they reach the LLM — lifting answer accuracy without widening the context window or increasing cost.
Source Citation & Traceability
Every answer linked back to the exact document, section, and page that grounded it — so users can verify responses, auditors can trace decisions, and hallucinations are immediately visible.
RAG Pipeline Orchestration
Full pipeline orchestration with LlamaIndex, LangChain, or custom routing — including query routing to the right index, multi-hop retrieval for complex questions, and graceful no-result handling.
Evaluation & Quality Measurement
Automated evaluation covering retrieval recall, context relevance, faithfulness, and answer correctness — scored against a held-out test set so every change is validated before shipping.
RAG Ops & Monitoring
Retrieval hit-rate, latency-by-query-type, cost dashboards, and staleness alerts for indexed content — full operational visibility so degradation is caught long before users notice.
Where RAG drives value
Accurate answers across every knowledge-intensive team
RAG turns document silos into a queryable, auditable knowledge layer that every function can trust.
Legal & Compliance
Instant answers from contracts, regulations, and policy documents — with the exact clause, page, and document cited in every response.
Customer Support
Knowledge-base Q&A that deflects repetitive tickets with accurate, source-linked answers drawn directly from your product documentation.
Internal Knowledge
Company-wide search across wikis, runbooks, and SOPs — so institutional knowledge is findable by everyone, not just the person who wrote it.
Research & Intel
Synthesise competitive intelligence, analyst reports, and literature reviews from large document collections in seconds rather than days.
Engineering
Codebase-grounded Q&A, API documentation search, and architecture decision record lookup embedded in your developer tooling.
Finance & Reporting
Q&A over financial filings, earnings transcripts, and internal reports — with exact figures cited and linked back to the source table.
90%+
Reduction in hallucination rate
<500ms
End-to-end retrieval latency
100M+
Documents indexed in production
5+
Vector databases supported
Document Audit & Corpus Mapping
Step 01Inventory your document types, formats, update frequency, and access control rules — the inputs that determine every downstream architecture decision.
Chunking & Embedding Strategy
Step 02Select chunking approach and embedding model benchmarked against your actual queries and content — not defaults that work on toy datasets.
Index Architecture & Hybrid Search
Step 03Design the vector store schema, BM25 index, metadata filters, and namespace strategy — then build the retrieval pipeline that fuses them.
Reranking & Retrieval Tuning
Step 04Measure retrieval recall and precision with an eval harness, then tune chunk size, top-k, and reranking threshold until the numbers prove quality.
Citation Layer & Production Hardening
Step 05Wire source attribution into every response, add staleness alerts for indexed content, and deploy monitoring before the first real user hits the system.
Ready to ground your AI in your own knowledge?
Tell us your document types and use case — we'll scope a RAG architecture and have a working retrieval prototype in front of you within two weeks.
Technologies
Our RAG technology stack
Pinecone
Vector DB
pgvector
Vector DB
Weaviate
Vector DB
Qdrant
Vector DB
LlamaIndex
RAG
LangChain
RAG
Cohere
Reranking
OpenAI
Embeddings
Elasticsearch
Keyword
Python
Language
TypeScript
Language
FastAPI
Backend
Why mabzone RAG
What makes our RAG systems different
Retrieval quality and citation traceability — not just a pipeline that runs but one that proves it's working.
Retrieval quality over retrieval speed
We tune for answer accuracy first — chunk size, top-k, reranking threshold, and hybrid weights are all measured against a test set, not left at defaults.
Every answer is traceable by design
Source citation is an architectural requirement, not a UI feature. We wire document provenance into the retrieval layer so attribution is always available, not reconstructed after the fact.
Any document format, any corpus scale
PDF, Word, HTML, Notion, Confluence, structured databases — we handle heterogeneous corpora with format-specific parsing that doesn't lose tables, headers, or footnotes.
Evaluation-first development
We build the eval harness before the pipeline — retrieval recall, context relevance, faithfulness, and answer correctness scored continuously so quality is measured, never assumed.
Hybrid search on every deployment
Pure vector search misses exact-match queries; pure keyword search misses semantic ones. We run both and fuse the results — so neither query type is a second-class citizen.
Compliance & Standards
Compliance Standards That Shape Our RAG Practice
We build retrieval systems within the data privacy, security, and AI accountability frameworks that regulated industries require — so your knowledge base is accurate, auditable, and access-controlled.
GDPR (EU)
Lawful data ingestion, storage, and retrieval for EU personal data
EU AI Act
Transparency and traceability requirements for AI-generated answers
OWASP LLM Top 10
Prompt injection prevention and retrieval input/output validation
NIST AI RMF
Risk management framework for trustworthy RAG deployments
ISO/IEC 42001
AI management system standard for RAG governance and accountability
SOC 2 Type II
Security and availability controls for document indexes and pipelines
HIPAA
Protected health information controls for healthcare knowledge bases
ISO 27001
Information security management for document ingestion infrastructure
CCPA
Consumer data rights for RAG systems serving California users
C2PA
Content provenance and authenticity for AI-retrieved and generated content
RAG Systems FAQs
Common questions about building retrieval-augmented generation systems for production.
Let's Build Together
Which document silo should your AI be able to answer from?
Tell us your document types and use case — we'll have a working retrieval prototype in front of you within two weeks.