Ömürcan Özcan
Selected work

SYSTEM 04 / INFORMATION SYSTEMS · AI-ENABLED PROJECT

Evidence-Grounded Regulatory Retrieval

A useful answer to a regulatory question needs a path back to the source.

My scope
Python information pipeline · Ingestion · Retrieval · Ranking · Orchestration
  • Python
  • RAG
  • LLM integration
  • Embeddings
  • Reranking
  • Document ingestion

Conceptual architecture

What reaches the model—and where did it come from?

  1. Documents & metadata
  2. Retrieval & reranking
  3. Source-grounded answers

Embeddings and metadata-aware retrieval select regulatory material. Reranking orders the evidence passed into answer generation, retaining the connection to the source.

Documents+ METADATA
IndexRetrieveRerank
EvidenceSOURCE-GROUNDED ANSWERS

OPERATIONAL CONTEXT

The answer needs its evidence.

Regulatory questions need relevant documents and source context. This RAG platform retrieves supporting material from ingested documents and uses it to ground answers.

MY RESPONSIBILITY

My contribution and scope

I worked on the Python pipeline around the LLM: document ingestion, metadata-aware retrieval, embeddings, reranking and multi-step orchestration.

DECISIONS & RATIONALE

Design the information system around the model.

01

Context enters at ingestion

Document ingestion and metadata-aware retrieval preserve context in the pipeline. Metadata complements embeddings when selecting regulatory material for an answer.

02

Retrieval is a sequence

The platform combines embeddings with metadata-aware retrieval, then reranks retrieved material. These stages select and order the evidence passed into answer generation.

03

Evidence stays visible

Multi-step orchestration connects processing, retrieval and generation. Answers remain grounded in the retrieved regulatory material.

Let’s talk about your system.

Building retrieval over a document collection? Let’s discuss the sources and the answers you need.