Ground Your AI in Real Knowledge with RAG Systems
We build retrieval-augmented generation pipelines that connect large language models to your proprietary data — delivering accurate, cited, hallucination-free answers your teams and customers can trust.
Trusted by innovative teams worldwide
Certified to Build Enterprise RAG
Our engineers are certified across the vector database and LLM ecosystems that power modern RAG.
End-to-End RAG Engineering
From document ingestion to production-grade retrieval — every layer of your RAG stack, purpose-built.
Document Ingestion & Parsing
We build robust pipelines that ingest PDFs, HTML, databases, Slack threads, and structured data — chunking and cleaning content for optimal retrieval accuracy.
Embedding Strategy & Optimization
Custom embedding models selected and fine-tuned for your domain. We benchmark OpenAI, Cohere, and open-source models to find the best fit for your data.
Vector Store Architecture
Purpose-built vector database architecture using Pinecone, Weaviate, or Qdrant — optimized for query speed, cost, and scale with hybrid search capabilities.
Retrieval Pipeline Engineering
Multi-stage retrieval with re-ranking, metadata filtering, and semantic routing to ensure the right context reaches the LLM every time.
Citation & Source Tracking
Every answer includes traceable citations back to source documents — critical for compliance, auditing, and building user trust in regulated industries.
RAG Evaluation & Monitoring
Continuous evaluation of retrieval precision, answer faithfulness, and hallucination rates using RAGAS and custom metrics dashboards.
Stop Hallucinations. Start Grounded AI.
Book a free RAG architecture review — we'll map your data to a retrieval strategy.
Your data becomes your AI's greatest advantage.
RAG transforms proprietary knowledge into a competitive moat. We engineer systems where every LLM response is grounded in your actual documents, policies, and data.
RAG Done Right for Regulated Industries
In fintech and healthcare, wrong answers cost money and trust. Our RAG systems are engineered for accuracy, auditability, and compliance from day one.
Why Teams Choose Us for RAG Development
We've built RAG systems for regulated industries where accuracy isn't optional — it's existential.
Describe Your RAG Use Case
Share your data landscape and we'll respond with an architecture sketch and timeline within 24 hours.
Our Engagement Process
Data Audit
We catalog your knowledge sources — documents, databases, wikis, APIs — and assess quality, volume, and access patterns.
Embedding & Chunking Design
Optimal chunk sizes, overlap strategies, and embedding model selection benchmarked against your actual queries.
Pipeline Build
Vector store setup, retrieval chain engineering, re-ranking integration, and LLM prompt tuning — tested end-to-end.
Evaluation & Hardening
RAGAS benchmarking, adversarial testing, edge case handling, and hallucination guardrails validated before launch.
Deploy & Monitor
Production deployment with observability dashboards, alerting on retrieval drift, and scheduled re-indexing.
What Our Clients Say
“Our compliance team was spending 4 hours per query searching regulatory documents. OpenMalo built us a RAG system that answers in under 3 seconds with full citations. It's completely changed how we work.
“We tested three RAG vendors before OpenMalo. They were the only team that actually benchmarked retrieval accuracy on our data before proposing an architecture. The result speaks for itself — 94% accuracy out the gate.
“OpenMalo's citation tracking feature was the deciding factor for us. Every answer our internal chatbot gives links back to the exact paragraph in our policy docs. Our auditors love it.
94% Retrieval Accuracy on 12,000 Regulatory Documents
RAG-Powered Compliance Assistant for NovaPay
How we built a retrieval-augmented compliance assistant that searches 12,000+ regulatory documents and returns cited, accurate answers in under 2 seconds — replacing 4-hour manual searches.
Compliance team drowning in regulatory documents
NovaPay's compliance officers were manually searching through thousands of RBI, SEBI, and PCI-DSS documents to answer internal queries — a process that took hours and still missed relevant sections.
Our Approach: Domain-specific embedding model fine-tuned on financial regulation, hybrid search with BM25 + semantic retrieval, 3-stage re-ranking pipeline, and full citation tracking — deployed in 5 weeks.
Read Full Case StudyFrequently Asked Questions
RAG (Retrieval-Augmented Generation) retrieves relevant documents at query time and feeds them to the LLM as context. Unlike fine-tuning, RAG doesn't modify the model itself — it grounds responses in your latest data, making it ideal for fast-changing knowledge bases like regulatory documents.
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