Production RAG Implementation
Hybrid Search, Agentic Retrieval
RAG demos are easy. Production RAG is hard. We build retrieval systems that work with messy real-world data—PDFs, images, charts, tables—with proper chunking, hybrid search, and evaluation.
See RAG in Action
How we built a hybrid agentic RAG with 12+ RAGAS evaluation metrics
End-to-End RAG Pipeline
From raw documents to accurate answers
Data Ingestion
Parse PDFs, images, tables, and charts. Extract text, metadata, and structure from complex documents.
Retrieval Strategy
Hybrid search combining semantic and keyword retrieval. Rerankers for precision.
Response Generation
Grounded responses with citations. Users know exactly where answers come from.
Evaluation & Tuning
Continuous evaluation of retrieval quality and response accuracy. No guessing.
Retrieval Strategies
Different problems need different approaches
Hybrid Search
Combine semantic (embedding) search with keyword (BM25) search. Best of both worlds for diverse queries.
Reranking
Use cross-encoders to rerank initial results. Higher precision when it matters.
Agentic Retrieval
Let agents decide retrieval strategy dynamically. Multi-step reasoning for complex questions.
Response Quality
Because wrong answers are worse than no answers
Performance & Cost
Accuracy
Retrieval recall, answer groundedness, citation accuracy—all measured.
Latency
Sub-second retrieval with caching and optimized indexes.
Cost
Token usage optimization, embedding caching, batch processing for efficiency.
Technologies We Use
FAQ
What types of documents can you process?
PDFs (including scanned), images, Word docs, HTML, Markdown, and more. We handle tables, charts, and complex layouts.
How do you handle multi-language content?
Multilingual embedding models and language-specific chunking strategies. We can build systems that work across languages.
What's the difference between your RAG and ChatGPT with files?
Control, accuracy, and cost. You control the data, we optimize for your specific use case, and production systems need proper evaluation—not just 'it seems to work'.
Can you improve our existing RAG system?
Yes. We can audit existing systems, identify improvement areas (chunking, retrieval, generation), and implement targeted improvements.
Fixed Price
Delivered in 4-6 weeks
50% upfront, 50% on delivery. Vector database costs separate.
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