Agentic Retrieval

Not a RAG wrapper. A knowledge graph that reasons.

First-wave tools paste back the closest text chunk and hope. RapidRFP builds a knowledge graph of how your company actually wins — then retrieves across vector, keyword, graph and neural reranking to ground every claim in your own evidence, with a citation on every line.

GraphRAGHybrid retrievalNeural rerankingMulti-hop reasoningInline citationsNo model training
4-way
Hybrid retrieval: vector + BM25 + graph + rerank
100%
Claims traceable to a cited source
Multi-hop
Reasoning across connected entities
Zero
Training on your proprietary data
How it works

Inside Agentic Retrieval

Built as part of one agentic system — every capability hands off cleanly to the next.

Knowledge graph

A graph of how your company actually wins

We model the entities and relationships behind your bids — people, projects, past performance, contracts, certifications, products and win themes — so answers reflect how your facts connect, not just which paragraph looked similar.

  • Entities & relationships, not loose text chunks
  • People ↔ projects ↔ past performance ↔ certifications
  • Connects evidence the same way an evaluator does
  • Improves as you bid — no library babysitting
knowledge-graph.agentrunning
Entities & relationships, not loose text chunks
People ↔ projects ↔ past performance ↔ certifications
Connects evidence the same way an evaluator does
Improves as you bid — no library babysitting
Hybrid retrieval

Four retrieval strategies, fused — not keyword guessing

Dense vector search finds meaning, BM25 catches exact terms and part numbers, graph traversal pulls in connected context, and a neural reranker orders what matters. The result is recall and precision a single-method RAG box can not reach.

  • Dense vector embeddings for semantic match
  • BM25 keyword search for exact terms & acronyms
  • Graph traversal for multi-hop context
  • Neural reranking for precision at the top
hybrid-retrieval.agentrunning
Dense vector embeddings for semantic match
BM25 keyword search for exact terms & acronyms
Graph traversal for multi-hop context
Neural reranking for precision at the top
Auditable by design

Every sentence traceable to a source

Regulated buyers demand defensible answers. RapidRFP attaches a citation to each generated claim and shows the exact passage it came from — so reviewers and SMEs can verify, not babysit.

  • Inline citations on generated text
  • Jump from any claim to its source passage
  • Confidence and coverage signals per answer
  • A clean audit trail for color teams
auditable-by-design.agentrunning
Inline citations on generated text
Jump from any claim to its source passage
Confidence and coverage signals per answer
A clean audit trail for color teams
Grounded

Grounded in your evidence — never invented

If the answer is not supported by your knowledge, the agent says so and flags the gap instead of hallucinating. Your data stays yours: encrypted, isolated, and never used to train a shared model.

  • Answers only from your grounded knowledge
  • Gaps surfaced, not papered over
  • Per-tenant isolation & encryption
  • No training on your proprietary content
grounded.agentrunning
Answers only from your grounded knowledge
Gaps surfaced, not papered over
Per-tenant isolation & encryption
No training on your proprietary content
Questions

Frequently asked questions

Common questions about agentic retrieval.

Most tools do single-shot semantic search over text chunks and paste back the nearest prior answer. RapidRFP fuses four retrieval methods over a knowledge graph and reranks the results, enabling multi-hop reasoning (e.g., “which cleared staff worked on a project like this for this agency”) that flat chunk-retrieval simply cannot answer.

See Agentic Retrieval on your next RFP

Book a 30-minute demo and watch the agents work on a real solicitation in your space.

Self-checking loops · win-rate guarantee · grounded & cited · never trains on your data