Event-sourced architecture
Systems preserve decision history, replay behavior, and make operational state inspectable.
Technology stack
Intelligence is architecture, not a model choice.
Prospairity combines data infrastructure, application engineering, AI orchestration, and governance so product surfaces can be trusted in production.
Governed AI orchestration
LLM routing, agents, memory, and calibration are designed as controlled software, not one-off prompts.
Data products that hold up
Point-in-time financial data, hiring proof, market loops, asset records, and logistics signals become reliable decision surfaces.
Production delivery
Cloud-native services, portals, observability, and integration paths are part of the first architecture pass.
Architecture in the portfolio
Proof points from built systems.
A deterministic macro intelligence system that separates measured facts from modeled estimates with point-in-time discipline.
Operating problem
Read liquidity and capital-flow regimes without lookahead bias, silent revisions, or hidden model uncertainty.
Point-in-Time DataProvenanceRegime AnalyticsBacktesting
Vintage-aware data pipeline, point-in-time availability semantics, reproducible build IDs, and audit-ready provenance.
No-lookahead analytics and reproducible build IDs
Measured-versus-modeled separation
Liquidity tide and capital-flow current framework
FoundersCIOsMacro investorsRisk leaders
A multi-portal logistics operating system for shippers, carriers, and platform operators with governed marketplace workflows.
Operating problem
Move freight pricing, contract terms, settlement, compliance, and exception work out of spreadsheets and disconnected portals.
FastAPIState MachinesPortalsContract-as-Code
FastAPI core, state-transition services, identity/PDP patterns, event/outbox workflows, and shipper/carrier/platform portals.
Shipper, carrier, and platform control surfaces
Contract-as-Code rate and settlement workflows
Marketplace clearing, compliance, and audit posture
ShippersCarriersPlatform operators
A reusable agent foundation for deterministic orchestration, memory, provider routing, calibration, DLP, and observable execution.
Operating problem
Use AI agents inside controlled workflows with memory, routing, budgets, DLP, and auditability instead of loose chatbot behavior.
AgentsMemoryDLPProvider Routing
FastAPI orchestrator, schema validation, semantic memory, provider routing, budget controls, DLP redaction, and audit metrics.
Schema-governed agent execution
Memory, calibration, and provider routing
DLP, budgets, audit logs, and operating metrics
FoundersEngineering leadersOperations teams
Responsible AI
Controls that make AI usable.
Privacy-first
Data minimization, access control, and audit posture are handled before launch.
Explainability
Outputs need source context, reasoning paths, and confidence signals where the workflow requires them.
Governance
Policies, approval gates, and monitoring are designed into the system instead of bolted on later.
Have a stack. Need an intelligence architecture?
The first conversation maps the existing systems, integration constraints, governance needs, and first production increment.
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