NeigborDrop is building an AI-powered local logistics network that turns everyday human movement into infrastructure.

Traditional delivery and ride platforms solve each request by creating a new trip and finding a dedicated driver. NeigborDrop starts with a different question:

Who is already going there—and can AI coordinate that existing trip to help someone nearby?

That simple shift—from dispatching more vehicles to intelligently coordinating movement that already exists—is the foundation of NeigborDrop.

1. 27,000+ registered users
2. 12,900+ completed neighborhood fulfillments
3. $350K+ in cumulative GMV
4. 61% repeat transaction rate
5. 85% fulfillment rate
6. Proven in Toronto and now expanding into multiple U.S. cities
7. Zero-fleet model: we coordinate people already in motion rather than building a dedicated driver fleet

We are starting with store runs, rides, and everyday errands. Over time, we believe the same coordination layer can connect people, businesses, and autonomous AI agents with trusted local human execution.

Every day, people are already driving to grocery stores, pharmacies, workplaces, airports, and thousands of other destinations. At the same time, nearby neighbors need groceries picked up, rides completed, packages moved, and everyday errands handled.

Those two realities rarely connect. Instead, the dominant model dispatches a dedicated driver for each new request—adding cost, empty miles, and operational dependence on a continuously available driver pool.

The capacity already exists. The missing layer is intelligent coordination.

NeigborDrop coordinates neighbors who are already heading out with people nearby who need something delivered, picked up, or completed along the way.

If David is already driving to Walmart and Sara needs milk, NeigborDrop can connect them:

1. David earns money on a trip he was already taking.
2. Sara gets what she needs without creating a dedicated delivery trip.
3. The neighborhood gains a more efficient local fulfillment network.

Our AI coordination layer is being built to reason across the variables that determine whether a local task can be completed successfully: route, destination, timing, proximity, task requirements, availability, and trust.

No owned fleet. No unnecessary trip. Coordination instead of dispatch.

Since launching earlier this year, NeigborDrop has grown to:

1. 27,000+ registered users
2. 12,900+ completed neighborhood fulfillments
3. $350K+ in cumulative GMV

Acquisition alone does not prove a local logistics network. Completed activity does. Our users have already coordinated more than 12,900 real neighborhood tasks representing more than $350K in GMV.

Two operating metrics are especially important:

1. 61% repeat transaction rate: people who experience NeigborDrop are returning to use it again.
2. 85% fulfillment rate: the network is successfully completing the large majority of requests placed through it.

We believe this combination—repeat usage plus reliable fulfillment—is early evidence that NeigborDrop is solving a recurring need, not generating one-time curiosity.

A NeigborDrop user is not locked into a single role. The same person can request help today and provide help tomorrow. That creates a flexible local supply network instead of a fixed pool of dedicated drivers.

As participation grows within a neighborhood, our coordination system gains more possible routes, timing windows, destinations, and trusted participants to work with.

Greater local density can create a reinforcing loop:

1. More people and trips create more possible matches.
2. Better matching improves speed and fulfillment reliability.
3. Better experiences encourage repeat activity and trust.
4. More repeat activity strengthens neighborhood density further.

This is the core network advantage we are building—one neighborhood at a time.

NeigborDrop is more than a directory of people willing to help. The product must coordinate a constantly changing physical world.

Over time, every completed task can help our system better understand local routes, timing patterns, task complexity, reliability, and the conditions that produce successful fulfillment.

Our long-term defensibility comes from the combination of:

1. Local network density built neighborhood by neighborhood
2. Coordination intelligence across route, timing, task, and trust signals
3. Behavior and reliability data generated through completed real-world activity
4. Community trust earned through repeated local fulfillment
5. A zero-fleet operating model designed around movement that already exists

Consumers expect fast, convenient local services, while delivery and ride-hailing costs continue to rise. Meanwhile, millions of everyday trips happen with unused capacity.

AI makes it possible to coordinate that fragmented movement with far greater intelligence—reasoning across intent, route, timing, proximity, trust, and task requirements.

We believe the next evolution of local logistics will not come from putting more dedicated drivers on the road. It will come from making existing movement useful.

We proved the model in Toronto and are now expanding into multiple cities in the United States.

Our rollout is intentionally focused:

1. Launch in targeted neighborhood clusters.
2. Build enough local participation to improve coordination and reliability.
3. Encourage repeat behavior from both requesters and helpers.
4. Apply what we learn to the next cluster and city.

This approach lets us expand while preserving the local density and trust that make the network useful.

Today, NeigborDrop helps people coordinate store runs, rides, and everyday errands. The larger opportunity is to become an execution layer connecting digital intelligence to the physical world.

As autonomous AI agents become capable of planning and initiating more tasks, they will still need trusted people to complete many actions in the real world. NeigborDrop is building toward a future where an AI agent can identify a local need and coordinate trusted human execution through people already moving nearby.

People, businesses, and AI agents—all coordinating through the movement already happening around them.

Sunill Kumaar, Co-Founder & CEO — Repeat founder with 20+ years building products and startups. Previously helped scale an early-stage startup from approximately $50K to $10M+ ARR in under three years and grew a consumer app to 500K+ users in under six months.

Claude Houle, Co-Founder & CTO — 20+ years building scalable software. Built one of the first industrial visionOS apps, was selected by Apple for a Vision Pro Developer Kit, and now leads NeigborDrop’s AI and platform engineering.

Aditi Jhaveri, Co-Founder & Product Head — 7+ years building consumer products. Engineered a custom Unity–Swift runtime supporting 15,000+ active users with zero stability issues and now leads product experience at NeigborDrop.

Our team combines consumer product growth, scalable engineering, AI development, and the operating experience required to build a trusted local network.

NeigborDrop has moved beyond an idea. Thousands of people have already used the network to complete real tasks, users are returning, and the large majority of requests are being fulfilled.

We are now at the stage where greater neighborhood density, stronger AI coordination, and expansion across U.S. cities can compound what we have already proven.

Our vision is to turn everyday human movement into the infrastructure that powers local commerce—and eventually, trusted real-world execution for the AI economy.

We’re just getting started.