Content revision ea31a3343577b30cee4337cf2fef2b0864a5741341f27598e9108766c486fb2d

## Cloud, Data & AI Consulting Intern

Experience
Source ID experience:pwc
Original https://www.henryw.me/#/experience#work-pwc

At PwC, I design and build AI and data systems for financial-services and public-sector clients. I develop solution architectures around each client’s business needs and technical constraints, then implement them through custom pipelines and integrations with existing systems.

I designed and implemented an AI workflow that analyses long reports for different types of risk. I studied the evidence each risk requires and defined how the AI should find and assess it. The system links findings to the original source pages, so reviewers can inspect the evidence and record their decisions.

I designed a system that uses AI to identify gaps in an IT assistant’s knowledge, research the missing information and keep its knowledge base up to date. I also built a browser agent that interprets service tickets and automates tasks in web applications.

I designed an AI slide-generation framework to help teams quickly explore different ways to present their ideas and iterate on slide designs. The framework uses the company’s visual assets and brand guidelines to guide image generation, then combines AI interpretation with computer-vision measurements to build editable PowerPoint slides. Teams can try new compositions while preserving original logos, icons and reusable components within a consistent visual identity.

For an AI assistant answering public enquiries using client documents, I evaluated and integrated tools into a retrieval-augmented generation (RAG) ingestion pipeline that prepares those documents for retrieval. It preserves page-spanning content, complex tables, references and flowcharts through chunking, which divides documents into searchable passages. I also developed a DeepEval framework to evaluate classification, retrieval and response quality.

To support a data migration, I built a pipeline assisted by a large language model (LLM) that reverse-engineers legacy ETL code, the logic that extracts, transforms and loads data. It recovers data lineage showing where data comes from and how it changes, together with the business rules and test scenarios needed for the migration.

I reviewed Ethereum and stablecoin infrastructure risks for a banking engagement.

## A Structure-Preserving RAG Ingestion Pipeline

Builds
Source ID projects:pwcRag
Original https://www.henryw.me/#/projects?to=pwcRag

The architectural risk appeared before retrieval began. If ingestion separated a passage from the note it refers to or reduced a flowchart to disconnected text, no later ranking or generation step could reliably restore the missing relationship.

MinerU rejoined paragraphs and tables across page breaks. Its OCR-based path recovered merged cells but missed nested tables in the tests. The built-in vision-language model recovered both as HTML. I then found that subsequent processing in the tested RAGFlow path discarded those tags, while LlamaIndex preserved the structure through indexing, retrieval and answer generation.

Cross-page footnotes and remarks needed explicit linking. I wrote a Python linker that used reference labels and document context to attach each note to its citing text before chunking. For flowchart images, I chose Mermaid to represent nodes and branches as searchable text that could be rendered back for inspection. Later research showed that MinerU’s built-in VLM already supported this conversion, bringing tables and flowcharts into the same parser and model.

A controlled five-page document let me repeat the checks across the full pipeline. The linker resolved all 13 cross-page references, and the reconstructed flowchart preserved every node, edge and label and supported correct answers to both test questions. I used this evidence alongside deployment and operating-cost analysis to develop the client recommendation, including its preference to operate fewer AI models.