Shah Khan · Human Data - Finance Lead at SpaceXAI

I build AI products and Quant systems that work.

A working record of ideas built to operate under real constraints.

What I’ve built—and what each project taught me.

The selected work spans a consumer product, a daily research engine, and two public strategy records. Open any project for the full case study.

Products

Software designed around a specific human moment.

What the build provesProduct judgment

Removing friction can matter more than adding features.

No required signup, a create-first flow, honest delivery fallbacks, and a focused emotional use case shaped the product more than feature breadth.

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Intelligence + automation

Systems that connect observation, decision, and repeatable action.

How it runsBuilt for review

Automation should show its work.

Schedules, defined data handoffs, status checks, and recovery paths make it possible to understand what ran—and what failed.

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Markets + quantitative systems

Public outcomes supported by private research and execution infrastructure.

Alpha QuantLive track record

A systematic momentum strategy made legible.

Live equity and drawdown, closed round-trips, a daily dispatch, and a 10-year backtest provide context without exposing the underlying strategy logic.

  • Live equity
  • 10-year backtest
  • Closed trades
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The investment-related projects are presented as software and research case studies, not investment advice.

Build the product. Make it reliable. Show the result.

That sequence connects the work: turn the idea into something useful, design it for real conditions, and make the outcome clear enough for others to evaluate.

01 / SURFACE

Make the idea tangible.

A working product reveals the real constraints faster than a polished theory.

02 / SYSTEM

Design for operation.

Scheduling, fallbacks, data contracts, and observability determine whether a build survives contact with reality.

03 / RECORD

Show what happened.

Live products, dated records, and honest status make the work easier to understand and evaluate.

What the projects are teaching me.

Short, attributed notes about AI systems, product judgment, reliability, and the choices behind the work.

Trustworthy AI is a systems problem.

The model matters, but I trust the system more when it has clear limits, shows its work, can recover, and someone owns the last action.

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Publishing the record changes how you build.

A live track record forces disciplined data handoffs, explicit caveats, and a clearer distinction between an outcome and a story about that outcome.

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Small products sharpen judgment.

A focused consumer product can teach more about friction, trust, and emotional context than a sprawling feature roadmap.

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I’m Shah Khan, Human Data - Finance Lead at SpaceXAI. I also build independent products across consumer software, AI-assisted systems, and quantitative research. I like building the whole loop—from the interface people touch to the automation and safeguards underneath it.

What I buildProducts, data systems, automation, and operating workflows.

How I workSmall prototypes, explicit constraints, and measurable outcomes.

What I publishShipped products, live records, case studies, and field notes.

What are you learning from the systems you’re building?

I’m interested in thoughtful conversations about useful AI, product judgment, automation, and what it takes to move from prototype to operation.

shah@shahbuilds.dev