Group travellers get split
Families and friends book together and land in different coaches. A child in S4, the parent in S9, and eleven hours to fix it.
RailXchange finds and connects the passengers who want each other's seats — on every train, every day.
Indian Railways allocates berths by algorithm and availability — not by who you're travelling with, or what your body needs. Three failures repeat on every single departure.
Families and friends book together and land in different coaches. A child in S4, the parent in S9, and eleven hours to fix it.
An elderly passenger stranded on an upper berth. A woman travelling alone on a lower berth who would rather be up top.
On every train there is an invisible web of contrasting preferences — perfectly complementary, entirely unaware of each other.
One protocol that reads every preference on a train, ranks who fits whom, and keeps searching until the best possible swap is found.
Exact matches sit at the top, near matches queue behind them — each one carrying the reason it fits you.
Reach multiple matches at once instead of one at a time. Chat or call, and settle with whoever agrees first.
A better match appears later in the journey? You're told immediately — the search runs until the train pulls in.
Perfect matches are rare.
Better seats are not.
From booking confirmation to an agreed swap — without a single call to the TTE.
The app reads it automatically from your IRCTC booking SMS. No forms, no typing out coach and berth numbers.
Move closer to your group, get a different berth type, or help someone else and earn rewards for it.
Ranked by compatibility, with the reason each one fits. Chat to agree, then simply meet on the train.
13 Crore journeys a year
where a swap would help
The problem regenerates on every departure. The market never depletes.
1 Crore 9 Lakh passengersper month — actively mismatched and reachable today
7 Crore 32 Lakh passengersper month — every reserved-class traveller on the network
Covers a preference-based, proximity-aware slot transfer protocol — the matching and negotiation layer behind every swap.
Signal processing and machine learning, paired with clinical and regulatory judgement.
An engineer who builds matching systems, and a doctor who has spent enough nights on the wrong berth to know exactly what it costs.
Co-founder
Product and operations builder with experience in healthcare-tech and AI startups.
Co-founder
Medical doctor with expertise in legal documentation, marketing strategy, and growth.
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