WHO I AM

Hi, I’m Rob.

12 years in venture capital, strategy consulting, and startup operations. My job is investing in AI companies. My passion is building agents that create value for myself, my clients, and my employer.

Things I'm Building

Things I Write About

Connect on LinkedIn

The world has changed:

AI changed the rules of the game. The new world demands a step change in agent development and orchestration skill. That only comes from rolling up your sleeves and building. This site shares what I've built, and what I'm thinking and writing about.

Things I'm Building

Three agents, three different architectures and levels of complexity

Production-grade diligence automation — an 8-pillar Digital Maturity Assessment

Digital Maturity Assessment Synthesis Agent (PE / M&A)

Problem

Diligence teams conducting a digital maturity assessment on a target company can spend weeks on synthesis: reading stakeholder interviews, mapping evidence to a rubric, scoring and sizing opportunities, preparing an executive report. The DMA synthesis agent accelerates and standardizes this process, evaluating targets across 8 pillars (e.g. Data Foundation, Tech Architecture, Process Automation).

Approach

An 8-node LangGraph pipeline takes an engagement package (i.e. structured C-suite interview notes, data profiling tables, app integration inventory) and produces a complete readout: an evidence-grounded score across all 8 pillars, targeted follow-up questions for any pillar where the evidence is too thin, and a sized, thesis-weighted list of the top value-creation opportunities.

Result

Produces 8 output files per run - full report, executive summary, scored heatmap, risk register, initiative backlog, pillar narratives, and process brief - in under 15 minutes.

Architecture

LangGraph, 8 nodes: Intake → Evidence Synthesiser → Pillar Scorer → Follow-up Generator -> Follow-up Integrator → Benefits Sizer → Output Generator → Report Assembler
Self-directed follow-up loop generates role-specific follow-up questions to address evidence gaps
LangSmith/Langfuse tracing on every run
Full eval suite confirms scorer consistency and rubric alignment against known-answer bundles

Video Walkthrough

Architecture diagram

Download Select Outputs

Multi-agent ‘research-to-publish’ system for thought leadership content

LinkedIn PoV Agent

Problem

Producing a well-researched LinkedIn article historically meant stitching together deep research, multiple cycles of critique and iteration, adjustments for tone matching the article intent / target audience - a manual workflow reliant on multiple tools and hours.

Approach

Six coordinating agents on a shared LangGraph state: an Orchestrator decomposes the topic into research elements, a Researcher and Critic run an iterative loop per element (the Critic scores depth, accuracy, and actionability, and routes weak research back for another pass), a human-in-the-loop checkpoint lets the user review research intent before it begins; a Writer and Editor integrate the diverse research components into a final piece.

Result

A streamlined article production agent. 8-12 minutes per run, producing seven output files including the final article, all research documents, and a process brief documenting every decision the pipeline made. Can either be published as is, or used as a '90% done' base, that users can tweak in their preferred LLM before publication.

Architecture

LangGraph StateGraph with a cyclic conditional edge (Researcher ⇄ Critic) - live web research per element, not vector/RAG retrieval
Human-in-the-loop interrupt - user reviews and edits research intent before the loop starts
Six agents, one shared PipelineState - each with clearly scoped read/write responsibility
Structured JSON at every hand-off; graceful degradation on rate limits

Video Walkthrough

Architecture diagram

Download Sample Outputs

Investment-grade research - theme in, ranked shortlist out

Thematic Stock Screener

Problem

Screening public markets for stocks that fit a specific thematic lane (e.g. "Physical AI / robotics") means manually pulling a candidate list and financials, iterating on your analysis and building a prioritized view - a few hours of grunt work before the actual decision-making can start.

Approach

A five-step pipeline: the user gives a theme and optional filters (market cap, region); the agent researches the space via live web search, pulls up-to-date financial data per candidate, scores and ranks every company, and writes a structured research report.

Result

Produces a ranked JSON shortlist with per-dimension scores plus a human-readable markdown report, in minutes. Tested end-to-end on multiple themes (e.g. Physical AI/robotics, Sovereign AI Infrastructure).

Architecture

Python + Anthropic SDK (tool use / function calling) + yfinance + Pydantic.
A five-step linear pipeline with three distinct tool calls: web research, financial data fetch, scoring
Every tool output schema-validated - no free-text parsing
Full cost + latency logging across the pipeline

Video Walkthrough