RAG Solutions

Build AI systems that give accurate, cited answers from your proprietary data. Our Retrieval-Augmented Generation solutions connect LLMs to your knowledge bases, documents, and databases — eliminating hallucinations and ensuring every response is grounded in real, verifiable information.

Build Your RAG System
94%
Answer Accuracy Rate
10M+
Documents Indexed
< 2s
Query Response Time
85%
Hallucination Reduction

RAG Implementation Services

From document ingestion to production deployment — we build RAG systems that actually work in the real world

Knowledge Base Ingestion

We ingest and index your documents, wikis, Confluence pages, PDFs, emails, Slack history, and databases. Our pipeline handles 50+ file formats, preserves document structure, and maintains incremental sync so your knowledge base is always current.

Semantic Search & Retrieval

Advanced retrieval beyond keyword matching: hybrid search combining vector similarity and BM25, query expansion, multi-step retrieval for complex questions, and re-ranking to surface the most relevant passages. We optimize retrieval precision and recall for your specific data.

Context Assembly & Prompting

The quality of RAG output depends heavily on how retrieved context is assembled. We design context windows that include relevant passages, metadata, conversation history, and instructions — optimized for each model's capabilities and token limits.

Citation & Source Attribution

Every AI response includes clickable citations linking back to the exact source document and passage. Users can verify any claim in seconds. We support inline citations, footnotes, and side-by-side source views for maximum trust and transparency.

Access Control & Permissions

Enterprise RAG with document-level access control. Users only get answers from documents they have permission to view. We integrate with your existing IAM system (Active Directory, Okta, Auth0) to enforce permissions at the retrieval layer.

Evaluation & Optimization

Continuous evaluation pipeline measuring answer accuracy, retrieval relevance, citation quality, and user satisfaction. We identify failure modes — wrong retrieval, missing context, poor generation — and systematically improve each component.

Why Our RAG Systems Stand Out

Built by engineers who've deployed RAG in production, not just played with demos

Verifiable Cited Answers

Hybrid Search (Vector + BM25)

Document-Level Permissions

Real-time Document Sync

Scales to Millions of Docs

Accuracy Analytics Dashboard

Internal Knowledge Base — 35,000 Employees Asking Questions Daily

A Fortune 500 company had 200,000+ internal documents spread across SharePoint, Confluence, Google Drive, and legacy file servers. Employees spent an average of 1.8 hours per day searching for information, and new hires took 3 months to become productive because of institutional knowledge scattered across systems.

We built a unified RAG system that indexes all document sources, respects existing access permissions, and provides a natural-language search interface. Employees ask questions like "What is our refund policy for enterprise customers?" and get accurate answers with links to the source policy document. Search time dropped from 1.8 hours to 12 minutes per day. New hire ramp-up time was cut by 40%.

Internal Knowledge Base — 35,000 Employees Asking Questions Daily

Customer-Facing Help Center — 60% Fewer Support Tickets

A SaaS company with 500+ help articles and extensive API documentation received 2,000 support tickets per month. Most questions were answered somewhere in their docs, but users couldn't find the right article — keyword search returned too many irrelevant results.

We deployed a RAG-powered help widget that understands user questions semantically, retrieves relevant documentation sections, and generates concise answers with links to the full article. The widget handles multi-turn conversations ("What about for enterprise plans?" follows naturally from a pricing question). Support tickets dropped 60%, and the customer satisfaction score for self-service support increased from 3.2 to 4.6 out of 5.

Customer-Facing Help Center — 60% Fewer Support Tickets

Legal Research Platform — Hours of Research in Minutes

A law firm needed their attorneys to quickly find relevant case law, statutes, and internal precedent memos. Traditional legal search tools required precise keyword queries and returned hundreds of results to wade through. Junior associates spent 3-4 hours per research question.

We built a legal RAG system that indexes their internal memo library, court filings, and subscribed legal databases. Attorneys describe their research question in plain English, and the system retrieves the most relevant authorities with explanations of why each is relevant. It highlights key passages, notes potential counterarguments, and generates a preliminary research memo. Average research time dropped from 3.5 hours to 35 minutes.

Legal Research Platform — Hours of Research in Minutes

RAG Solutions FAQ

RAG (Retrieval-Augmented Generation) is a technique that connects an LLM to your data. When a user asks a question, the system first searches your documents to find relevant information, then feeds that information to the LLM as context for generating an answer. This means the AI only responds based on your actual data — not its training data — which dramatically reduces hallucinations and ensures accuracy.
Virtually any text-based data: PDFs, Word documents, web pages, Confluence/Notion wikis, Slack messages, emails, database records, API responses, code repositories, and more. We also handle semi-structured data like spreadsheets, CSVs, and JSON. For images and diagrams within documents, we use multimodal models to extract and index the information.
Our RAG pipelines include incremental sync: when documents are added, updated, or deleted in your source systems, the index updates automatically — typically within minutes. We use change detection to re-process only modified content, not the entire corpus. For real-time use cases, we support streaming ingestion that indexes new content in near-real-time.
Significantly more accurate for domain-specific questions. Plain ChatGPT can only use its training data and often hallucinates facts about your specific business. RAG systems ground every answer in your actual documents and provide citations. In our deployments, we typically achieve 90-95% accuracy vs 40-60% for generic LLMs on domain-specific queries. The citation system also makes it easy for users to verify any answer.
Yes. We implement document-level access control that integrates with your existing identity provider (Active Directory, Okta, Google Workspace, etc.). When a user asks a question, the retrieval system only searches documents that user has permission to view. This means executives see answers from confidential documents, while general employees only see public information — automatically, with no extra configuration per query.
The core components are: a vector database for semantic search (we typically use Pinecone, Weaviate, or pgvector), document processing pipelines (can run on standard compute), and an LLM (cloud API or self-hosted). For a typical enterprise deployment with 100K-1M documents, infrastructure costs run $500-2,000/month. We can deploy on your existing cloud infrastructure (AWS, GCP, Azure) or provide a managed solution.

Ready to Unlock Your Data's Potential?

Let's build a RAG system that turns your knowledge base into an intelligent, searchable resource. Start with a free data assessment.

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