We're a technology company. Founded in 1997 in New York City, we build the proprietary AI and software that runs parking, valet and vehicle-access operations for airports, hospitals and enterprise operators across the United States. We wrote every algorithm we ship and trained every model we deploy. When something needs fixing, there is no vendor to wait on.
We build AI that works in the physical world. Rain, dark, occlusion, bad plates, dirty cameras, in production rather than in a benchmark.
Shark Byte Systems was founded in 1997 at a time when parking management meant card readers and magnetic loops. We saw something different: that computer vision and machine learning would eventually be the right way to know what vehicle was where, when it arrived, and what it was owed.
It took years to build the training data, the models, and the infrastructure to make that true at production accuracy, 99.9% F1 on LPR in the conditions we measure SVA in: New York City garages, high-volume airport lots, and valet operations where a mismatched ticket costs real money.
Today SVA runs across 500+ sites. The engine is continuously retrained on live data. Every deployment makes it smarter. That's what it looks like when you own the technology all the way down.
Shark Byte Systems incorporated in New York City with a focus on software for the parking and property management industry. Early products focused on access control, ticketing and audit for urban garage operators.
Launch of Garagenet, the company's foundational parking management platform. POS, valet ticketing, gate control and reporting built for the specific demands of high-density NYC operations.
SBS begins internal R&D on machine learning for vehicle recognition. Early LPR experiments on NYC garage footage. First in-house neural network training pipelines established. The insight: commodity LPR consistently fails on NYC plates and conditions. We would have to build our own.
SpotShark Video Analytics achieves 99%+ F1 on LPR in production conditions. ReID tracking, which fingerprints a vehicle without reading a plate, passes the accuracy threshold for real-world frictionless operations. First SVA-powered Garagenet deployments go live.
SVA adds 3D height and length estimation from standard cameras, first used for oversize-fee automation in NYC lots. EV/PHEV/BEV classification added. SVA integration certified with Designa and Amano McGann PARCS systems. SpotShark.com launched as the driver-facing payment portal.
SVA reaches 500+ active sites across airports, hospitals and property portfolios. US-data-residency infrastructure established for contracts that require it. SolaaS (Software and LPR as a Service) via AWS launched. It replicates the on-site record to the cloud, so an operator can reach any site in a portfolio from anywhere.
SVA continues to retrain on a growing corpus of production data across all active sites. Partner program expanded for PARCS integrators and prime contractors. sbs.ai becomes the primary destination for AI, LPR and CV technology; Garagenet.com continues as the parking management product destination.
Everything SBS ships runs on SVA, the proprietary AI core. We sell the engine, the management platform and the driver portal, separately or together.
The proprietary computer-vision engine. LPR, ReID, 3D dimensioning, EV classification, APGS, vehicle metadata. Runs Garagenet and integrates into partner PARCS. 100% in-house, with no licensed AI dependencies.
Explore SVAPOS, mobile valet units, pay-on-foot kiosks, gate control, audit and reporting. The foundational parking management platform. Sold standalone, or AI-powered by SVA for frictionless operations.
Garagenet.comThe customer-facing web portal. Transient ticket payment, monthly vehicle registration, contract billing and space reservations, with no app install required. QR-code enrollment and SMS eTickets.
SpotShark.comThere is no licensed commercial CV in the stack, no OpenAI wrapper, and no black-box vendor under the hood. That has one practical consequence. When something is wrong, we fix it ourselves instead of filing a support ticket with a third party and waiting.
We train and evaluate on real footage from real sites. That means NYC garage conditions: rain, glare, construction debris on plates, folded corners, partial occlusion. The models that survive that training are the ones we ship.
Every edge case from every live deployment feeds the next training cycle. SVA gets better with scale, because more sites means more data, which means better models for every site. Off-the-shelf CV ships once and rots; SVA compounds.
Critical decisions get cross-checked by independent neural networks running in parallel. Is this a plate? Is this the same car? The same design principle used in avionics: a single model's uncertainty is caught before the output is committed.
SVA inference and the full record run on hardware at the site. What SolaaS replicates stays in US cloud infrastructure. Card payments have run end to end on P2PE-validated devices and gateways for years, so cardholder data is encrypted at the reader and never reaches our systems in the clear. We don't sell data, we don't share it, and we can demonstrate that in writing for any contract that requires it.
When SVA powers a prime contractor's bid, it wins because the numbers are real. We don't compete with our partners for operator relationships. We build the AI layer so they can win the work and deliver it.
Send us an hour of footage from your lane, or use ours. You will see what the engine actually reads, not a slide deck about it.