Retrieval Augmented Generation (RAG)
Connect large language models to your proprietary data. Build AI systems that retrieve relevant knowledge, reason over it, and generate accurate, citation-backed answers your teams can trust.
Off-the-shelf LLMs know a lot but not your data. RAG bridges this gap by grounding AI responses in your actual documents, databases, and knowledge bases. The result: fewer hallucinations, verifiable answers, and AI that gets smarter as your knowledge grows. We design and deploy enterprise RAG systems that handle the full pipeline from document ingestion to production-grade retrieval and generation.
Ground LLMs in your data
Large language models are powerful but generic. They hallucinate when they do not know something, and they cannot access your internal knowledge. RAG solves both problems.
Reduce Hallucinations
Constrain the model to answer from retrieved evidence, not from its training data. When the context does not contain an answer, the system says so instead of fabricating one.
Access Proprietary Knowledge
Your policies, contracts, research, product docs, and institutional knowledge become instantly queryable. No fine-tuning required, and the system stays current as documents change.
Ensure Verifiability
Every answer comes with source citations pointing to the exact documents and passages used. Users can verify claims, building trust in AI-generated responses across the organization.
The RAG pipeline
A well-architected RAG system is more than vector search plus an LLM. Each stage of the pipeline must be designed for accuracy, scale, and maintainability.
Document Ingestion
Ingest documents from diverse sources: PDFs, wikis, databases, APIs, SharePoint, Confluence, and more. We handle format conversion, metadata extraction, and source tracking.
Chunking & Embedding
Split documents into semantically meaningful chunks. Generate dense vector embeddings using models tuned for your domain and language requirements.
Vector Storage
Store embeddings in high-performance vector databases with metadata filtering, hybrid search indexes, and efficient nearest-neighbor retrieval.
Retrieval
At query time, retrieve the most relevant chunks using semantic search, keyword matching, and re-ranking to surface the best context for the question.
Augmented Generation
Pass retrieved context alongside the user query to the LLM. Prompt engineering and guardrails ensure the model reasons over your data, not its training set.
Response
Deliver accurate, grounded answers with source citations. Users can verify claims against original documents for full transparency and trust.
What we build
End-to-end RAG capabilities from document understanding to intelligent answer generation with full traceability.
Document Intelligence
Ingest, parse, and understand documents across formats: PDFs, Word, HTML, Markdown, spreadsheets, and scanned images with OCR. Extract structure, tables, and metadata so every piece of knowledge is searchable and retrievable.
Semantic Search
Go beyond keyword matching. Our semantic search understands intent and meaning, retrieving relevant passages even when the query uses different terminology than the source documents. Hybrid search combines dense vectors with sparse retrieval for best results.
Knowledge Synthesis
Combine information from multiple documents and sources to synthesize comprehensive answers. The system reasons across your entire knowledge base, connecting related concepts and surfacing insights that span different documents.
Contextual Generation
Generate responses that are grounded in retrieved evidence. Every answer is traceable to source documents, with citation links so users can verify accuracy. Hallucination guardrails ensure the model stays within the bounds of your data.
Where RAG delivers value
RAG transforms how organizations access and use their knowledge. These are the use cases where we see the highest impact.
Internal Knowledge Base Q&A
Enable employees to ask natural language questions across company policies, procedures, handbooks, and institutional knowledge. Reduce time spent searching and ensure consistent answers across teams.
Customer Support Knowledge
Power support agents and customer-facing chatbots with accurate answers from product documentation, troubleshooting guides, and historical ticket resolutions. Reduce resolution time and improve customer satisfaction.
Regulatory Compliance Search
Search and interpret complex regulatory frameworks, compliance documents, and legal guidelines. Help compliance teams quickly find relevant requirements and understand how regulations apply to specific scenarios.
Research & Analysis
Accelerate research workflows by querying across academic papers, market reports, patents, and internal research databases. Synthesize findings and surface relevant prior work across large document collections.
Contract Intelligence
Extract and query information from contracts, agreements, and legal documents. Identify key clauses, obligations, deadlines, and risks across your entire contract portfolio without manual review.
Technical Documentation
Make technical manuals, API docs, engineering specs, and runbooks instantly queryable. Engineers and field teams get precise answers with references to the exact section and version of the documentation.
RAG technology stack
We select and configure the right combination of tools for your data, scale, and deployment requirements. No vendor lock-in.
Vector Databases
- Pinecone
- Weaviate
- Qdrant
- Milvus
- pgvector
- ChromaDB
Embedding Models
- OpenAI Embeddings
- Cohere Embed
- Sentence Transformers
- BGE
- E5
- Domain-specific fine-tuned models
Large Language Models
- GPT-4o
- Claude
- Gemini
- Llama
- Mistral
- On-premise and private deployments
Chunking Strategies
- Semantic chunking
- Recursive splitting
- Sentence-window
- Parent-child
- Agentic chunking
- Format-aware parsing
Re-ranking & Retrieval
- Cohere Rerank
- Cross-encoder re-ranking
- Hybrid search (dense + sparse)
- Metadata filtering
- MMR diversity
- Query expansion
How we help
Accurate answers grounded in your data
Every response is backed by retrieved evidence from your documents. Users get factual answers with source citations they can verify, not generic AI-generated text.
Reduced hallucinations
By constraining the LLM to reason over retrieved context rather than its training data, RAG dramatically reduces fabricated or incorrect responses. Guardrails detect and flag low-confidence answers.
Works with your proprietary data
No need to fine-tune or retrain models. RAG connects LLMs to your existing documents, databases, and knowledge bases. When your data updates, the system reflects changes immediately.
Scales with your knowledge
Add new documents, data sources, and domains without rebuilding the system. The architecture handles growing knowledge bases while maintaining retrieval speed and answer quality.
Frequently Asked Questions
Build AI That Knows Your Business
From document ingestion to production-grade retrieval and generation, we help you deploy RAG systems that deliver accurate, trusted answers from your knowledge base.