Problem

Target benchmarking and peer research in due diligence is repetitive, manual and inconsistent across teams.

Approach

AI agents in n8n that orchestrate LLM reasoning with live web search and return structured, source-linked outputs.

Impact

Standardised benchmarking outputs and saved analyst hours on every study.

Separate workflows handle target screening, peer identification and metric collection. Each agent decomposes the research question, runs searches in parallel, validates findings against multiple sources and writes results into a consistent template — so two teams researching the same market no longer produce two different answers.

Problem

Teams answer long lists of recurring questions from hundreds of pages of project documents — slow and error-prone.

Approach

A Copilot Studio agent: upload an Excel question list and PDF sources, the agent processes them in batches and writes answers back row by row.

Impact

Cut manual document review to a fraction; reusable across new and existing projects.

The agent generates SharePoint upload links, monitors the repository for new files, batch-processes question sets against the source documents and produces per-batch summaries. Progress is visible in real time through the OneDrive-synced Excel file, so users watch answers appear as the agent works.

Problem

Years of RFP and proposal knowledge sit in scattered files; teams rebuild content from scratch and ask around for precedents.

Approach

A central repository with LLM-based metadata extraction and a parent/child agent pipeline for retrieval, surfaced through a chat interface.

Impact

MVP live with the team, with past proposal content found in seconds instead of hours.

The MVP runs on Copilot Studio with SharePoint storage and automated notifications. I'm now redesigning it as a VS Code extension built on GitHub Copilot Chat with Claude as the LLM and the Microsoft Graph API for semantic search over per-team repositories — removing the MVP's file-size, OCR and access-control limits.

Problem

M&A datasets are full of messy company names and unclear ownership structures; manual cleansing eats analyst time.

Approach

A modular pipeline combining deterministic rules, ML matching and targeted web search to cleanse names and map parent–child relationships.

Impact

Reduced manual effort and improved consistency of entity data across M&A analytics.

Deterministic rules catch the common normalisation cases cheaply, an ML matcher resolves near-duplicates, and web search handles the long tail of ambiguous entities — each stage only escalates what it can't resolve, keeping the pipeline fast and auditable. The same architecture supports follow-on benchmarking and peer-research workflows.

Problem

Applying well takes hours per role, and good postings slip past while you're busy tailoring the last one.

Approach

A two-agent pipeline. A discovery agent scans new postings and scores each one against a structured archive of my experience, writing ranked leads into an Airtable tracker. An application agent then takes a queued lead and produces a tailored CV, cover letter and company one-pager, files everything into a dated application folder and updates the tracker.

Impact

Every application is genuinely tailored, nothing falls through the cracks, and the whole pipeline is visible on one dashboard. Running daily in my own search.

Built as Claude agent skills with Airtable as the system of record: a Searches table feeds the discovery agent, scored leads land in a review queue, and moving a lead to "Queue" triggers the application workflow. If you're a recruiter reading a tailored application from me, there's a decent chance this system drafted the first version.

Problem

Recruiters skim. They have one specific question — "has he done X?" — and a static page makes them dig for it.

Approach

The chat widget in the corner of this page: an n8n workflow answers questions over my full experience corpus, with a zero-dependency keyword-matched fallback baked into the page so it degrades gracefully if the backend is unreachable.

Impact

Specific answers in seconds instead of scrolling. Try it — bottom-right corner.

The front end is dependency-free JavaScript with typo-tolerant fuzzy matching over a local knowledge base; when the webhook is configured it upgrades transparently to the n8n-hosted LLM workflow. Yes, the portfolio demos itself.

Problem

My due-diligence agents live inside a firm's walls, so I can't show you the code. This one you'll be able to run yourself.

Approach

Give it a target company; it identifies peers, collects comparable metrics with live web search, validates across sources and emits a sourced comparison table plus a short benchmarking memo.

Status

Scoped and in build — repo link lands here the moment it ships (July 2026).

Problem

Data-room Q&A tools that can't cite the page they got an answer from aren't usable in diligence.

Approach

Drop in PDFs and a question list (CSV/Excel); it answers each question with page-level citations and flags the ones it can't ground in the documents — refusing beats hallucinating.

Status

Scoped and in build — repo link lands here the moment it ships (July 2026).

Problem

LLMs will happily hand you a market size with no way to check the maths.

Approach

Describe a market; the agent builds a top-down and bottom-up sizing as an explicit assumption tree — every number sourced or flagged as an assumption you can edit, with the estimate recomputing from your inputs.

Status

Scoped and in build — repo link lands here the moment it ships (July 2026).