Problem & context
Problem
MATHCYX applications need reusable AI capabilities without exposing provider credentials or allowing individual products to make providers, models or unbounded cost authoritative business inputs.
Intended users
Trusted MATHCYX applications that require interactive or asynchronous AI execution through normalized contracts.
Role and contributions
AI platform architecture, durable execution model and service implementation
Dataset / inputs
Validated MATHCYX identity context, generic capability requests, execution policies, cost limits and explicit domain specifications supplied by calling services.
Methods & architecture
A domain-independent control plane owns request lifecycle, routing, jobs, attempts, artifacts, policies, audit and normalized results. PostgreSQL remains durable truth while queues, caches and edge state are secondary.
Implementation
- Define trusted request and identity boundaries
- Persist AI requests and execution attempts
- Separate domain decisions from provider execution
- Introduce policy, cost and audit controls
- Support durable jobs, artifacts and normalized results
- Define explicit mathematical-response evaluation contracts
Current evidence
The current implementation verifies the project foundation below; broader outcome claims will be added after evaluation.
Limitations & next steps
The platform foundation is under active development. Provider execution and product-level outcome claims must be validated independently as those capabilities are activated.
Complete provider adapters, durable execution, reliability controls and staged integration with approved MATHCYX applications.
Attribution & licence
Private MATHCYX AI platform. The public company platform is the only linked external destination.