AI-powered Logistics

This first-principles approach, helped systematically evaluating multiple directions against user value, trust, and unit economics.

Exploring how AI could reduce operational dependency while fitting naturally into existing logistics workflows. This meant evaluating multiple product directions, ranging from assistive tools to automation-driven systems against user value and economic scalability.

This structured exploration allowed us to move beyond incremental fixes and toward a focused, defensible product direction.

Explored as a quick & low-friction entry point to surface logistics information quickly.
While it improved access to answers, it remained detached from core workflows and did not meaningfully reduce effort or cost.

Outcome: Improved access to information

Limitation: Fragmented value; did not integrate into core workflows or reduce operational load

Evaluated for its potential to unlock peer-driven knowledge sharing and network effects. The model required sustained moderation and engagement investment, with returns that did not justify the operational overhead.

Outcome: Potential for peer-driven knowledge sharing

Limitation: High moderation and engagement cost; weak short-term ROI

Tested as a domain-specific search experience to accelerate insight discovery. Although useful for retrieval, the approach stopped at information delivery and lacked direct impact on task execution or workflow efficiency.

Outcome: Faster insight discovery

Limitation: Informational output without execution capability; low task completion impact

Investigated for end-to-end automation opportunities. Adoption signals were weak due to system complexity, low transparency, and limited user trust in delegating high-stakes logistics decisions.

Outcome: End-to-end automation potential

Limitation: Excessive complexity and low trust for operational users; poor adoption signals

Task-based workflows where AI augments discrete logistics actions. This approach aligned with user mental models, reduced cognitive load, preserved control, and offered a scalable path with lower operational dependency.

Outcome: Provides fragmented value and can be stitched to form end-to-end workflow. User only pay for the task the need

Limitation: Fragmented value; integrate into core workflows or reduce operational load needed set up

We converged on a task-based logistics workflow platform, where AI augments execution rather than replaces decision-making, combined with generative AI logistics-focused search engine.