Projects

Proof assets for production AI deployment patterns.

These projects demonstrate the patterns I build for clients: grounded knowledge systems, safe agents, workflow automation, evaluation, and production-ready internal tools.

Case study placeholders

Each project is framed around buyer problem, workflow, system design, and proof.

Placeholder case study

Hospitality and property operations teams

Frontline AI Virtual Assistant

Support requests are scattered across guests, tenants, operators, and knowledge bases.

Problem Workflow Guardrails Proof

AI pattern: Tenant-aware assistant with operator oversight, escalation, and grounded responses.
Stack: FastAPI, Firebase auth, RAG, operator console
Next proof: Will show tenant isolation, escalation queue, review loop, and deflection metrics.

Placeholder case study

Analysts, investment teams, and research-heavy operators

Investment Research Intelligence Platform

Filings, notes, thesis updates, and committee memos are slow to synthesize.

Problem Workflow Guardrails Proof

AI pattern: Citation-grounded research workspace for filings, annotations, and memo generation.
Stack: Document ingestion, retrieval, citation checks, collaboration threads
Next proof: Will show source-linked summaries, watchlist alerts, and hallucination checks.

Placeholder case study

Expert-led B2B teams building authority for search and answer engines

AEO Content Operations Studio

Research notes rarely become consistent articles, newsletters, and reusable snippets.

Problem Workflow Guardrails Proof

AI pattern: Human-reviewed content engine for answer-engine-ready publishing.
Stack: Topic clustering, source notes, draft workflows, distribution automation
Next proof: Will show prompt maps, review workflow, citation extraction, and visibility tracking.

In progress

Founders, COOs, CTOs, and operators

AI Workflow Readiness Scorecard

Teams know AI matters but cannot rank which workflows are worth building first.

Problem Workflow Guardrails Proof

AI pattern: Interactive diagnostic that scores workflow fit, data readiness, risk, and ROI.
Stack: Python app prototype, scoring engine, lead capture path
Next proof: Will become the lead-generation engine and public diagnostic tool.

Placeholder build

Operations, sales, support, recruiting, and finance teams

Agentic Operations Copilot

Teams need agents that suggest actions without creating uncontrolled side effects.

Problem Workflow Guardrails Proof

AI pattern: Internal copilot with approvals, tool-call traces, logging, and escalation.
Stack: Agent orchestration, task queues, Slack/email/CRM context, approval UI
Next proof: Will show safe agent design, failure handling, and human-in-the-loop controls.

Placeholder build

Teams with RAG prototypes that need production confidence

RAG Evaluation and Observability Dashboard

Knowledge assistants ship without reliable measures of retrieval quality or faithfulness.

Problem Workflow Guardrails Proof

AI pattern: Dashboard for golden questions, citation checks, costs, latency, and regressions.
Stack: Eval sets, prompt/version tracking, retrieval metrics, cost dashboard
Next proof: Will show productionization skill beyond a basic chatbot demo.

Template

Every mature case study will use the same proof structure.

01

Problem

02

Workflow before AI

03

AI system design

04

Guardrails

05

Demo

06

Business value

07

What I learned

08

Next version