technicaL infrastructure & scaLabiLity
the core computationaL engine behind yangL.ai requires exceptionaL structuraL baLancing. operating at the boundary of microservices and cLient browser interactions means that security and high concurrency must dominate our architecturaL decisions.
- fLask microservices core: designed as a secure, distributed rest api backend capabLe of isoLating sensitive database constraints away from cLient environments.
- cryptographic vauLt encLaves: suppLier parameters are processed within mathematicaLLy protected execution spaces, ensuring that consumer-side anaLytics cannot map out automated suppLier margins through repetitive testing Loops.
- Low-Latency ingestion pipeLine: capabLe of anaLyzing compLex retaiL payLoads in reaL-time, extracting product variations, pricing tiers, and coupon structures instantLy.
pitch deck screen demo: pLatform agnostic payLoad integration
demonstrating how the fLask engine normaLizes outbound transaction metadata across varying e-commerce tech stacks.
shopify stack
get /checkout/cart.json
injected: token verified
injected: token verified
magento enterprise
post /rest/v1/carts/mine
injected: token verified
injected: token verified
custom frameworks
graphqL/mutation/appLybid
injected: token verified
injected: token verified
systems architecture topoLogy map
cLient node
extension runtime
extension runtime
↓ tLs 1.3 encrypted payLoad ↓
distributed fLask cLuster
asynchronous engine & matcher
asynchronous engine & matcher
↓ private vpc peering network ↓
isoLated suppLier vauLt
fLoor constraint ruLes
fLoor constraint ruLes
teLemetry repository
anonymized intent data
anonymized intent data