Our RAG development practice covers every pattern from basic semantic search to advanced multi-hop retrieval — all optimized for enterprise accuracy, latency, and compliance requirements.
Upload PDFs, Word docs, spreadsheets, or scanned files and get instant, accurate answers with source citations. Perfect for policy manuals, contracts, financial reports, and technical documentation.
Connect your LLM to internal wikis, Confluence spaces, Notion pages, SharePoint, and databases — creating a unified intelligent search layer across your entire organizational knowledge.
Purpose-built RAG for legal research, contract review, and regulatory compliance. Our systems retrieve relevant case law, clauses, and precedents with precise citations to support legal professionals.
HIPAA-compliant RAG pipelines for clinical decision support, medical literature search, and patient record summarization — grounded in authoritative medical sources with traceability.
Federated RAG architectures that query multiple data sources simultaneously — combining vector databases, SQL stores, APIs, and web content — and synthesize coherent, comprehensive answers.
Every answer references the exact source passage, document, and page. Our citation-aware RAG builds user trust and enables auditability for regulated industries and enterprise governance.
We use best-in-class retrieval and embedding tools, choosing the right combination for your data volume, latency requirements, and security posture.
LlamaIndex, LangChain, Haystack, LangGraph, custom pipelines
Pinecone, Weaviate, Qdrant, pgvector (PostgreSQL), Chroma, Milvus
OpenAI text-embedding-3, Cohere Embed, BGE, E5, sentence-transformers
OpenAI GPT-4o, Anthropic Claude, Google Gemini, Azure OpenAI, private LLMs
Unstructured.io, PyMuPDF, Apache Tika, AWS Textract, Azure Document Intelligence
AWS, Azure, GCP, on-premise (air-gapped for regulated industries), Docker, Kubernetes
Stop letting your institutional knowledge sit in siloed documents. Our RAG development team will have a working prototype running on your data in weeks, not months.
Book a Free AI ConsultationRAG combines a retrieval system (vector search) with a large language model. Instead of the LLM relying solely on its training data, it first fetches relevant passages from your private documents or databases, then generates an answer grounded in that retrieved context. This dramatically reduces hallucinations and keeps answers up to date without expensive retraining.
Our RAG pipelines ingest PDFs, Word/Excel/PowerPoint files, HTML, Markdown, scanned documents (via OCR), SQL databases, REST APIs, Confluence/Notion/SharePoint, Slack/Teams message archives, and custom data feeds. We handle structured, semi-structured, and unstructured data simultaneously.
We support fully private deployments — your data never leaves your infrastructure. We implement role-based access control at the retrieval layer, encryption at rest and in transit, audit logging, and optionally use on-premise LLMs for air-gapped environments. We are GDPR, HIPAA, and SOC 2 aware in our design choices.
Fine-tuning bakes knowledge into model weights — expensive, slow to update, and can degrade general reasoning. RAG keeps knowledge external and retrievable, so you can update documents without retraining the model. RAG also provides traceable citations, which fine-tuning cannot. For most enterprise knowledge use cases, RAG delivers faster results at lower cost.
Book a free 30-minute strategy call with our team. No sales pitch — just a frank conversation about your project.
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