When Search Makes Decisions with AI
Enterprise RAG systems increasingly influence customer answers, policy interpretations, recommendations, and business actions. But a fluent, cited answer can still be unsafe if it relies on stale policy, unauthorized data, a superseded document, or an incomplete view of the truth.
This interactive session reframes RAG as an evidence-and-decision system. Participants examine a realistic stale-answer failure and redesign the retrieval path using source authority, freshness clocks, entitlement-aware access, lexical and semantic search, Graph RAG, structured data, citations, decision receipts, and safe refusal rules.
Most RAG talks focus on embeddings, chunking, vector databases, and prompts. Those are necessary—but not enough when AI influences real customers or business outcomes. The hard enterprise question is not “Did we retrieve something relevant?” It is:
“Was this evidence authorized, current, applicable, and strong enough for the AI to make this claim or recommendation?”
Without these controls, a system can look healthy—fast responses, good relevance, attractive citations—while exposing private data, applying an expired policy, or making promises the organization cannot defend.
Value for participants:
- Learn how to prevent stale, superseded, or unauthorized evidence from reaching AI context.
- Understand when to use BM25, semantic retrieval, Graph RAG, and structured system-of-record data together.
- Design a practical evidence graph that captures ownership, policy versions, scope, conflicts, and applicability.
- Separate the AI’s role in synthesizing evidence from policy’s role in allowing a decision.
- Build citations that can be checked, and decision receipts that explain why the AI was allowed to respond.
- Define when the system should answer, ask for clarification, defer, refuse, or escalate to a human.
- Take away reusable architecture artifacts: Source Authority Map, - Freshness Clocks, Admissibility Contract, Retrieval Ensemble Map, Citation and Decision Receipt, and Refusal/Stop-Authority Ladder.
About Rohit Bhardwaj
Rohit Bhardwaj is a Director of Architecture working at Salesforce. Rohit has extensive experience architecting multi-tenant cloud-native solutions in Resilient Microservices Service-Oriented architectures using AWS Stack. In addition, Rohit has a proven ability in designing solutions and executing and delivering transformational programs that reduce costs and increase efficiencies.
As a trusted advisor, leader, and collaborator, Rohit applies problem resolution, analytical, and operational skills to all initiatives and develops strategic requirements and solution analysis through all stages of the project life cycle and product readiness to execution.
Rohit excels in designing scalable cloud microservice architectures using Spring Boot and Netflix OSS technologies using AWS and Google clouds. As a Security Ninja, Rohit looks for ways to resolve application security vulnerabilities using ethical hacking and threat modeling. Rohit is excited about architecting cloud technologies using Dockers, REDIS, NGINX, RightScale, RabbitMQ, Apigee, Azul Zing, Actuate BIRT reporting, Chef, Splunk, Rest-Assured, SoapUI, Dynatrace, and EnterpriseDB. In addition, Rohit has developed lambda architecture solutions using Apache Spark, Cassandra, and Camel for real-time analytics and integration projects.
Rohit has done MBA from Babson College in Corporate Entrepreneurship, Masters in Computer Science from Boston University and Harvard University. Rohit is a regular speaker at No Fluff Just Stuff, UberConf, RichWeb, GIDS, and other international conferences.
Rohit loves to connect on http://www.productivecloudinnovation.com.
http://linkedin.com/in/rohit-bhardwaj-cloud or using Twitter at rbhardwaj1.