UPFRONT is Google Flights for event tickets: every site's price for the same seat, fees included, on one map. Then it goes further, with a 3D view from every section and an MCP connector that lets AI assistants find tickets for you. I'm the technical co-founder, and I built the whole thing.
Ticketmaster, StubHub, SeatGeek and six more, lined up seat for seat with fees included. Live on web and Android; iOS is in App Store review.
Sec 211 · Row 19200 Level · Madison Square Garden
3D view from your seat
$331each incl. fees
2% cheaper than similar seats
We found 5 prices
UUPFRONT$331
TTickPick$344
TTicketmaster$352
GGametime$359
Buy on UPFRONT · $331 each →
3D seat views
See the seat before you buy it
I built 3D models of 45 venues, so every section shows the real view from the seat instead of a stock photo.
3D view
UPFRONT
TickPick
Ticketmaster
How the 3D works
Every seat in the building, placed one by one
This is Yankee Stadium assembling from my venue model: 44,673 seats, row by row, then a flight to the view from one section.
AI connector · MCP · try it
Ask an AI assistant for tickets. It calls UPFRONT.
I built and shipped a public MCP server, so assistants like Claude can search events and compare live prices instead of guessing. It's listed in the Claude connector directory and the MCP Registry.
Event
Seats
I want
Proof it works
Does the connector make AI more accurate?
I asked AI assistants real ticket questions, like the one below, three different ways. Separate AI graders then scored every answer against the true prices, without knowing which setup wrote it.
“What's the cheapest pair of tickets to tonight's game, with fees?”
The hard part
Nine sites, nine names for the same game
Every site labels the same event, venue and seat differently. I built the engine that recognises they're the same thing, so prices line up seat for seat.
Trail Blazers at Knickssite A
NYK v POR · MSGsite B
New York Knicks vs. Portlandsite C
↓
One event · Madison Square Garden · Sec 211, Row 19
How I built it
I built the whole thing
I took an earlier version of the site and rebuilt it into today's product, end to end: the data engine, the web app, the iOS and Android apps and the MCP connector, on React, Django, Postgres, Redis and Swift. More than half of the work was done alongside AI coding agents.
640+ commits. I wrote the product.
Early traction
AI is already sending customers
About 5,000 organic visits a month, and the first paid orders.
5% of visits come from ChatGPT
02 · Product lead · U.S. Air Force · 2020 – 25
RHINO
I brought Air Force vehicle inspections from a 50-year-old paper form to a live picture of the whole fleet. I started as the developer who wrote the first app, then led the product from the first interviews on base to rollout at 20+ bases.
U.S. Air ForceAerial port squadrons20+ bases2023 AFIMSC Innovation Rodeo finalist
Before · pen and paper on the hoodThe switch · a Vehicle Control Officer shows RHINO above the whiteboard it replacedNow · the inspection on a tablet
01 · For 50 years
A paper form, a clipboard and a filing cabinet
Every vehicle, every day, by hand. Problems were written down and filed. Nobody saw them until something broke.
VEHICLE OPERATOR'S INSPECTION REPORTFORM NO. ____
MTWTF
OPERATOR SIGNATUREDATE
brakes soft?3rd time reportedFILED
Then it went on the pile. Decades of them at every base, and nobody was ever going to read one.
02 · The switch · try it
The same inspection, on a phone
Tick the checks and watch Truck 07 respond. Every tap is recorded and reaches the base and the fleet the moment it happens. Then submit, and watch Truck 07 roll through the check below.
Truck 07Daily check
Inspection filedTruck 07 is on its way ↓
InspectorBase VCOFleet
Live record · Truck 070 / 4 checked
Truck 07 is rolling out to the flightline ↓
Tires
Lights
Fluids
Brakes
Waiting for the first check…
03 · Now, across the Air Force
Every vehicle checked before it rolls
Thousands of Air Force vehicles a day pass through RHINO, from pickups to K-loaders. Each one is either verified good to go, or its problem is reported before it turns into a breakdown.
3,912vehicles checked today
41problems caught early
20+bases online
● Truck 07 verified · good to go
Contract value
$25M
in government contracts.
Adoption
20+
bases, from first pilot to standard practice.
Time back
44,000
labor hours a year the Air Force projected RHINO could save.
I sat with Vehicle Control Officials and inspectors and turned what they told me into inspections each base could customise, fleet dashboards, and reports in the formats the Air Force already required. I was the main technical contact for operators, base leadership and engineering, and ran the training and pilots at each base.
Airmen running RHINO on the flightlineThe fleet runs from pickups to airfield fire trucks
03 · Product owner · U.S. Air Force · 2020 – 25
Apollo
Think UPS tracking, built for military cargo. Apollo follows every pallet from the warehouse floor onto the aircraft, and tells the port how much it can move and where the bottleneck is. I owned it from the first version through pilot to 15+ bases.
The whole network, live
Thousands of pallets on the move, every one accounted for
Each dot is a pallet in transit between ports. Close a port and watch Apollo divert its cargo: click any port on the map.
2,418pallets in motion
63aircraft airborne
1,106delivered today
Close a port
All ports open
Pick one and follow it
PLT-2231Received
463L pallet · 4,180 lb · Priority 1 · 6 pieces
Estimated arrivalTomorrow · 06:40Z
Ramstein AB · Bay 4
Speed
35%
faster cargo processing.
Reach
15+
bases at full adoption.
More than tracking
It finds the bottleneck
Apollo works out how many pallets a port can move per hour and which piece of equipment is holding it back.
Technical lead on enterprise data-platform deals for Wall Street firms, including quantitative trading and large-scale analytics. I ran discovery, architecture, proofs of concept and executive demos.
New business
$9M
sourced and closed as the technical lead.
Total deal value
$150M+
supported across 15+ strategic wins, in cycles from $500K to $10M+.
Saves
4
late-stage deals rescued by resolving technical objections, worth about $800K.
Background
Sell it, deploy it, build it.
Ten years on three sides of the same problem: getting software bought, getting it adopted, and building it myself. That mix is what forward-deployed and solutions work asks for.