Marketing & Solution Company · Seoul / New York
Relationships, freight, people in transit. Three different fields, one problem — scattered points sitting still, unaware of one another. b.plat closes the space between them with a single personalization inference engine, turning points that were stuck into flow.
Partnership & investor enquiriesEach business connects a different set of points. All three run on the same engine.
btwn — between you and me
Recommend a place you've been to a friend: your friend gets a discount, you get a referral fee. On top of that we built a personalized menu. Dishes containing an allergen never appear at all. Returning guests see new items and bundles built around what they already liked. First-time guests see the signature dish.
Live on iOS and Android with merchants in New York
TMS & multimodal ticketing
Freight or people, anything in motion breaks at the handover. b.plat takes on the seams. With small and mid-size carriers in Korea we're building order and delivery-tracking systems, and within that we own the layer matching cargo type and delivery method to each shipper. The aim is to standardize how large shippers work with smaller carriers, so more transactions run down the same road.
On the passenger side we add personalized payment — passes, season tickets, coupons — on top of each operator's systems. Air to rail, sea to subway, bus to taxi to shared bikes and scooters. The next transfer to solve is autonomy. The moment robotaxis arrive the last mile is redrawn, and what's needed then isn't vehicles: it's the layer binding every mode into one ticket and one payment. That's the layer we're building now.
Collaboration underway with a Chungcheong-region carrier and major Korean shippers · exploring a Jeju public bus project
TESRIS — scattered pieces into one block
Government records sit on one side and residents' proposals on the other, each piling up without knowing the other exists. TESRIS weaves council minutes and open data into a single fabric. Officials are alerted to new matters as they surface, and can keep decisions consistent with what has already been resolved. Only then do the scattered pieces become one block you can act on.
On top of that, small local voices are gathered into proposals that reach the neighbourhood, then the district, then the statute book. De-identification is a strength of the engine — handling personal data at scale is what it was built for. Personalization was long deferred because it cost too much; it now costs little and happens immediately. Technology should be pointed at making people better off.
Proposals submitted to municipal governments in the Seoul metropolitan area, under review
b.plat's asset isn't a business line, it's the engine. Our personalization algorithm — a machine-learning inference engine — reads who wants what, which cargo should move which way, and where a passenger will change lines, all in the same way. The domains differ. Because it was built on personal data at scale, de-identification is native to it rather than bolted on. The question never does: where is the next point to connect?
Personalization algorithm · Machine-learning inference engine
Incorporated in Korea and the United States.
Shippers who need standardization. Transit operators who want their operating data put to work. Investors who want to see how far a personalization engine goes. Whichever you are, write to us.
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