03 / AI ENGINEERING · RESEARCH & DEVELOPMENT
AI & Data Intelligence
An AI engineering exploration into intelligent data profiling, anomaly detection, and discrepancy investigation. This is a proposed prototype, not a confirmed production deployment.
Portfolio concept / proposed prototype · Exploration & researchTHE CONTEXT
A clear starting point.
This exploration examines how AI and LLM-powered workflows could support enterprise data engineering. Relevant technologies include Python, LLMs, RAG, LangGraph, Snowflake, and cloud data platforms. These are proposed tools, not a claim of a deployed agentic system.
THE CHALLENGE
What needs to work.
Enterprise data investigation requires evidence and context. An assistant should distinguish a measured data issue from a suggested explanation, and make its reasoning inspectable without exposing confidential data.
THE APPROACH
Inside the decisions.
Profile and validate data
Explore intelligent profiling and data-quality checks to describe datasets and identify possible anomalies. Deterministic validation would establish the evidence before model interpretation.
Retrieve relevant context
Investigate RAG as a way to connect questions with approved documentation, dataset definitions, and validation results.
Guide discrepancy investigation
Prototype a LangGraph-based workflow for AI-assisted reconciliation and root-cause analysis, with explicit steps for gathering evidence and reporting uncertainty.
Evaluate before adoption
A proposed evaluation would assess evidence grounding, false positives, and usefulness to reviewers. Human review and scoped data access would remain part of the design.
THE TOOLKIT
OUTCOMES & VALIDATION
Evidence over assumptions.
The current outcome is a defined exploration direction and proposed prototype approach. No production implementation, benchmark, or deployment outcome is claimed.
AI can suggest where to look. Evidence and human judgment should decide what a discrepancy means.