Six AI cases.
One production mindset.

Policies, product documentation, support history, and operational knowledge live across different systems. Keyword search misses context, while a generic LLM can answer confidently without evidence.

A retrieval-augmented generation pipeline that ingests and chunks approved sources, creates embeddings, applies metadata-aware retrieval, reranks evidence, and generates answers with citations and permission boundaries.

Designed the RAG architecture, retrieval flow, prompt and context strategy, API layer, evaluation plan, observability, and cloud delivery pattern.

A production-oriented knowledge assistant pattern that improves answer discovery while keeping evidence, permissions, and failure behavior visible.

Operations teams repeatedly collect context, check rules, update systems, and prepare responses. A single prompt cannot reliably manage these dependent actions or their failure states.

An orchestrated agent workflow with narrow tools, explicit state, structured outputs, approval gates, retries, and deterministic business rules around every model decision.

Defined agent boundaries, tool contracts, workflow state, human-in-the-loop checkpoints, error handling, audit events, and API integrations.

A repeatable automation architecture that lets AI assist with reasoning while conventional software remains responsible for permissions, transactions, and control.

Pure keyword search misses semantic intent; pure vector search can overlook exact identifiers and structured constraints. Enterprise queries often require both.

A hybrid retrieval layer combining lexical matching, dense vectors, metadata filters, query rewriting, fusion, and reranking, with relevance measured against a curated evaluation set.

Designed query understanding, retrieval composition, ranking strategy, structured filters, evaluation metrics, latency budgets, and fallback behavior.

A search architecture that balances semantic recall with exact-match precision and gives teams a measurable path for improving ranking quality.

Commerce data is spread across orders, customers, products, payments, and campaigns. Leaders need concise insights, but every claim must remain traceable to governed data.

A governed analytics layer that combines validated metrics and structured queries with an LLM explanation layer for summarisation, anomaly narratives, and guided exploration.

Supported AI-driven analytics delivery and shaped the surrounding data, integration, cloud, product, and engineering practices needed for dependable use.

A decision-support pattern that makes complex commercial data easier to explore while keeping calculations in deterministic systems of record.

Manual verification creates queues and inconsistent handling. Fully automated approval is risky when documents are unclear or rules require expert judgment.

A rules-first workflow that extracts document data, validates required fields, flags inconsistencies, routes exceptions, and presents evidence to a reviewer before final action.

Led KYC compliance automation and the engineering integration work around secure data handling, workflow control, banking requirements, and operational rollout.

Verification processing time was reduced by 50% while retaining a reviewable workflow and clear responsibility for final compliance decisions.

Security teams lose time manually correlating logs, identity events, API activity, and cloud alerts.

Agentic investigation layer across application logs, authentication, APIs, cloud telemetry, and internal runbooks.

Alert triage, incident summarization, suspicious activity analysis, evidence collection, and recommended containment actions.

LLM agents, RAG, tool use, anomaly/context analysis, and structured outputs.