The problem
People waste money and time, because they can't turn unused items and services into value.
No direct match
You offer dog walking, you need tax help. The accountant doesn't need a dog walker. Trade dead.
Passive listings
Facebook Marketplace and Craigslist show you what exists. They don't connect the dots across people.
Value sits idle
Excess inventory, spare capacity, unused skills. Real value with no way to deploy it.
The solution: Naibour
AI finds multi-party trades across your entire neighbourhood. Six steps, fully automated.
Post in plain English
Say what you need and what you offer. No forms, no categories.
AI parses your input
NLP extracts skills, categories, and confidence from free text.
Score every connection
Each offer is scored against every need: taxonomy, semantics, trust, AI refinement.
Build the graph
A directed weighted graph links all participants. Each edge is a viable offer-to-need match.
Find the rings
DFS cycle detection finds trade rings of 2-4 people where everyone gives and everyone receives.
Propose and execute
Top rings are proposed. All participants must accept. Decline triggers re-search.
Three ways to trade
Direct swaps, barter offers, or AI-discovered multi-party rings.
Direct swap
See a match? Accept instantly. Both sides defined upfront.
InstantBarter offer
Propose a counter-offer on any listing. They review, they decide.
NegotiableMulti-party trade ring
Every morning at 6 AM, the AI finds cycles where everyone gives and everyone receives.
AI-discoveredWho participates
Anyone with underused value. Individuals, freelancers, and businesses alike.
Beyond native listings
Aggregate fragmented supply and demand from across the web.
Public listing ingestion
Normalize and index relevant listings from fragmented environments where technically and operationally appropriate.
Marketplace aggregation
Aggregate supply and demand signals across sources like classified sites, community boards, and marketplace platforms.
Listing normalization
Standardize unstructured listings into Naibour's schema so they can participate in graph-based matching.
Partner and API integration
Where available, integrate through official APIs and partnership agreements rather than unilateral data collection.
Trust engine
A weighted confidence score, not a checklist. Three pillars, 7.5 max.
Identity verification (KYC)
Government ID verification. Up to 2.0 points based on completeness.
Social and digital reputation
Linked social accounts and professional profiles. Up to 1.5 points.
Trust deposit / auth hold
A Stripe card hold creates real financial accountability. The strongest signal. Up to 4.0 points.
How your trust score is used
Higher trust = the AI picks you first when building trade rings.
Higher trust = your listings appear more prominently.
Some high-value trades require a minimum trust score.
Your score is visible everywhere. Build trust once, benefit always.
Privacy by design
Progressive identity disclosure. You control what you reveal and when.
Pseudonymous by default
Participate with a display name only. No real name or contact info required.
Trust through platform signals
Verification, social proof, and financial commitment build credibility without revealing identity.
Progressive reveal when ready
Full identity shared only after both parties accept a trade and are ready to coordinate.
Why this is fintech
Financial infrastructure for non-cash value exchange.
Multilateral clearing and netting
Trade rings net obligations across participants. Each person delivers to one and receives from another. Same structure as financial clearing.
Non-cash value exchange
Skills, goods, services, and capacity are tradeable value. Compatibility scores provide the pricing signal, ring consensus provides settlement.
Liquidity for underused assets
Excess inventory, spare capacity, unused skills are illiquid assets. Naibour surfaces counterparties that traditional markets cannot.
Financial inclusion
No money required to participate. Anyone with a skill, service, or good can trade. Economic participation beyond cash or credit.
Business model
Build trust and liquidity first. Monetize later.
Free to use
Free for all participants. Priority: prove the matching engine, build trust, achieve network density.
Strategic optionality
At ~10K active users: transaction fees, premium business tiers, priority matching, enterprise integrations.
Value creation at scale
A coordination layer for underused value across individuals, businesses, and geographies. Infrastructure compounds.
Trade rings in action
Nobody swaps directly. The AI connects a chain where everyone wins.
The graph at work
Nine participants, dozens of potential connections. The algorithm finds the rings hidden inside.
Frequently asked questions
Why not just use Craigslist or Facebook groups?+
What if someone no-shows or fails to deliver?+
How does the trust score work?+
Can I stay anonymous at first?+
Why use an auth hold instead of escrow from day one?+
Is Naibour only for individuals?+
What kinds of goods and services work best?+
Can Naibour use listings from other marketplaces?+
How does the AI actually find matches?+
What happens after the 7-day hold expires?+
How might Naibour make money in the future?+
Can businesses participate?+
Turn what you have
into what you need
Everyone has something to offer. Post your skills, your goods, your capacity. Let the AI and your neighbours do the rest.