Projects

Things I have built to think like an AI product manager

Five product projects across agentic AI, model routing, evaluation and strategy. Each one is live, so you can try it rather than just read about it.

How to read these. These are independent product concepts and proof of concept builds. Numbers shown inside the demos are illustrative, and none of them are production systems or affiliated with any company.
Project 02 · Model routing and cost control

RELAY

Not every request needs the most expensive model. RELAY routes simple requests to a fast, low cost tier and escalates harder ones, then shows the cost, latency and accuracy trade off with a plain language reason for every route.

  • Two tier routing between a fast model and an escalated model
  • Cost, latency and accuracy deltas for every route
  • A 'why this route' explanation for transparency
  • Product thinking on unit economics of AI features
Model routingCost optimisationAI platform
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Project 03 · Agentic location intelligence

BirdSignal

A product concept for location intelligence. It uses a retrieval pipeline over brand configuration and signals, scores its own outputs with an LLM as a judge, and routes each case to automatic action, human review or escalation.

  • RAG pipeline over brand configuration and signals
  • LLM as a judge quality metrics
  • Three way routing: Auto, Human in the loop, Escalate
  • Demo figures are illustrative, not real data
Agentic AILLM EvalsHuman in the loop
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Project 04 · Product strategy and prototype

Spotcheck

A complete product strategy for connected TV advertisers who need to build creative faster and prove which ads actually drive incremental results. It covers problems, personas, principles, scope, metrics, risks and a roadmap, and is honest about which assumptions are not yet tested with users.

  • Problems, personas, principles and scope
  • Success metrics, risks and roadmap
  • Assumptions clearly labelled as not yet user tested
  • Built on experiment based measurement
Product strategyGenAIExperimentation
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Project 05 · Recommendation engine concept

Assurance Fit

A product concept for recommending the right cover at the right moment. Deterministic eligibility rules decide what is allowed, a weighted relevance score decides what is shown, and the reasoning trace is visible, with open questions listed by function.

  • Rules and judgment kept separate by design
  • Weighted relevance scoring
  • Visible reasoning trace for every recommendation
  • Open questions listed by function
RecommendationsExplainabilityProduct concept
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Looking for the outcomes from shipped, production products? Those are in the case studies. These projects show how I explore, scope and test AI product ideas before they reach engineering.