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Horizon Desk

Built with a three-person team at SteelHacks XIII, Horizon Desk uses synthetic banking data and AI-assisted outreach to identify customer opportunities, with deterministic eligibility checks and human review for credit decisions.

Role
Frontend + supporting backend
Team
3 developers
Event
SteelHacks XIII
Year
2026
Status
Completed prototype
Stack
  • Next.js
  • TypeScript
  • Claude 3.5 Sonnet
  • ElevenLabs
  • Python
  • Vitest
Devpost

Problem

Retail banks can hold large balances in low- or zero-interest checking accounts, and branch bankers have limited ways to spot which customers may have surplus liquidity and reach them in a compliant, personal way.

The team prototype

Our team built Horizon Desk for SteelHacks XIII. The prototype analyzes synthetic retail-banking customer data, identifies potential surplus liquidity using deterministic financial eligibility checks, and drafts personalized AI-assisted outreach. It also simulates customer responses and outreach cooldowns, and routes credit opportunities to human bankers rather than automatically approving loans.

My contribution

I helped originate the project concept, worked primarily on the frontend, and contributed to some of the backend development.

Team project

Team

  • Frank Ncube
  • Gamuchirai Mubayiwa
  • Sumon Mondal

Horizon Desk was built by a team of three. The prototype’s functionality reflects shared team work rather than any one contributor.

SteelHacks XIII participants gathered in the auditorium before the closing ceremony.

Built at SteelHacks XIII

University of Pittsburgh · 2026

The three Horizon Desk team members standing with the event mascot in front of a large window.
The Horizon Desk team at SteelHacks XIII.

Architecture

A simplified view of the prototype’s main workflow. Customer records, responses, and outcomes are synthetic or simulated.

Engineering decisions

  1. 01

    Deterministic eligibility before AI-assisted outreach

    Potential surplus liquidity is identified with deterministic financial eligibility checks rather than a language model, so who is contacted follows explicit criteria. AI-assisted drafting comes afterwards.

  2. 02

    Human review for credit opportunities

    Credit opportunities are routed to human bankers instead of being approved automatically, which keeps lending decisions with people.

  3. 03

    Synthetic data and modeled outcomes stay labeled

    The prototype runs on synthetic customer data and simulated responses, and it is presented as modeled behavior, not as real customer activity or results.

Scope and limits

  • All customer records and financial projections in the prototype are synthetic or simulated.
  • Integration with core banking systems was a planned next step and was not implemented.