I Rebuilt My AI Portfolio to Tell a Better Story (and Rank on Google)

For a long time, my portfolio did what most portfolios do: it listed projects and hoped the right person clicked through. That works until you realize recruiters, hiring managers, and search engines all want the same thing: context. Not just what you built, but why it matters, how it is structured, and whether you can explain it clearly.

So I rebuilt draketalley.ai with a simple goal: make every serious project discoverable, readable, and runnable. If someone lands on my site from Google, LinkedIn, or a referral, they should leave knowing exactly what kind of engineer I am.

What went live

• Seven production AI systems featured front and center: AutoFlow, LangChain Enterprise Dashboard, DocuMind, SentinelAI, Google ADK Portfolio, Fraud Agent Orchestrator, and GameEdge Intelligence
• A dedicated technical article for every repo, derived from each README: architecture, capabilities, tech stack, and setup steps
• BlogPosting schema, sitemap entries, Open Graph metadata, and canonical URLs on draketalley.ai
• A visual refresh: elevated project cards, mesh gradient backgrounds, and clearer calls to action
• This journal section, for professional updates, launch notes, and stories that do not belong in a README

Why I did it this way

GitHub READMEs are great for developers who already know what they are looking at. They are less great for someone trying to answer: does this person ship production systems, or just demos? Splitting the site into project deep dives plus a professional blog lets me serve both audiences without diluting either one.

The project articles are technical and SEO-targeted. Keywords like multi-agent orchestration, fraud ML with SHAP, local-first RAG, Google ADK agents, and Temporal workflow triage map to real repos with real code. The journal posts are where I share the human side: launches, lessons, career pivots, client patterns, and whatever else is worth writing down while it is fresh.

The through-line across the work

If you scan the seven featured repos, a pattern shows up quickly. I gravitate toward systems that are auditable, local-first where it makes sense, and honest about production gaps. Multi-agent routing with explicit graphs. Policy layers above models. Explainability baked in, not bolted on. FastAPI backends with operator UIs that actually help someone run a demo or review a trace.

That is the story I want hiring teams and collaborators to see. Not a grab bag of tutorials, but a consistent point of view on how applied AI should be built when the stakes are real.

Explore the new project articles

• AutoFlow: multi-agent inquiry automation with LangGraph and Ollama
• LangChain Enterprise Dashboard: enterprise GenAI workbench with hybrid RAG
• DocuMind: local-first RAG with dual Chroma collections and citation grounding
• SentinelAI: real-time fraud scoring with XGBoost, SHAP, and drift monitoring
• Google ADK Portfolio: multi-agent GenAI with tool-grounded résumé facts
• Fraud Agent Orchestrator: policy-as-code triage with Temporal and OPA
• GameEdge Intelligence: sports analytics with sentiment analysis and customer segmentation

What comes next on this blog

I am treating this as a running professional log, not a content marketing calendar. Expect posts when something meaningful happens: a repo ships, a pattern clicks, a deployment lesson hurts, a client conversation reframes how I think about a problem. The goal is consistency over volume, and usefulness over virality.

If you are hiring for senior data science, applied AI, or MLOps, start with the project grid on the homepage, then pick whichever deep dive matches the role you are trying to fill. If you just want to follow the journey, bookmark draketalley.ai/blog and check back when I publish the next update.