What is included in Agentic RAG Development
Every engagement covers these core deliverables. No hidden add-ons, no scope creep surprises.
Dynamic Query Decomposition & Sub-Goal Planning
SLA-backed engineering implementation of dynamic query decomposition & sub-goal planning tailored to your system architecture.
Hybrid Vector Search (BM25 + Dense Embeddings + Re-Ranking)
SLA-backed engineering implementation of hybrid vector search (bm25 + dense embeddings + re-ranking) tailored to your system architecture.
Tabular Data & PDF Structure Parsing (Unstructured & LlamaParse)
SLA-backed engineering implementation of tabular data & pdf structure parsing (unstructured & llamaparse) tailored to your system architecture.
Self-Reflective RAG (CRAG & Self-RAG Architectures)
SLA-backed engineering implementation of self-reflective rag (crag & self-rag architectures) tailored to your system architecture.
Multi-Modal RAG for Technical Diagrams & Charts
SLA-backed engineering implementation of multi-modal rag for technical diagrams & charts tailored to your system architecture.
Production Vector Database Optimization (Pinecone, Qdrant, Weaviate)
SLA-backed engineering implementation of production vector database optimization (pinecone, qdrant, weaviate) tailored to your system architecture.
From kickoff to delivery
A repeatable, transparent process we have refined across 200+ projects. No guesswork on your side.
Chunking Strategy
Hybrid Indexing
Agent Loop Engineering
Benchmark Evaluation
Ready to build your Agentic RAG Development project?
A free 30-minute call. We review your requirements, identify risks early, and give you an honest assessment of what it takes to ship this right.
What We Solve in Agentic RAG Development
Standard RAG fails when answers span multiple separate documents or complex tables.
Agentic query decomposition that breaks complex questions into multi-hop sub-retrievals.
Vector search returns irrelevant chunks due to poor embedding alignment.
Hybrid retrieval combining BM25 keyword matching, vector dense search, and Cohere re-ranking.
Inability to query structured SQL databases and unstructured documents simultaneously.
Unified Agentic RAG capable of switching dynamically between SQL generation and vector search.
Expert Guidance on Agentic RAG Development
What makes Agentic RAG superior to standard RAG?
Standard RAG retrieves documents once. Agentic RAG evaluates if the retrieved info is sufficient, re-plans if necessary, and performs multi-hop reasoning.
Can Agentic RAG process scanned PDFs and tables?
Yes. We use advanced parsing tools to preserve table geometry and optical text structures.
Ready to deploy production-grade Agentic RAG Development?
Talk directly with our senior software architects. No sales fluff, just clear engineering blueprints, cost estimates, and rapid execution.