Transform the revenue engine
Design connected Lead-to-Order, Quote-to-Cash and Order-to-Cash operating models for complex SaaS, usage, software, hardware and services businesses.
Revenue transformationEnterprise finance architecture · AI controls · Product innovation
I design the systems that make enterprise AI accountable.
I help SaaS and technology companies modernize Quote-to-Cash, subscription and revenue operations—and build control layers that make AI decisions explainable, governed and audit-ready.
Oracle Fusion & RMCSQ2C / O2CSaaS monetizationAI governance
LIVE ARCHITECTUREreasoning path active
ContextCorrelated
PolicyValidated
EvidencePreserved
The positioning
Where revenue architecture, enterprise systems and AI controls converge.
I work across the full chain—from commercial intent and product configuration to billing, revenue recognition, accounting and cash. The differentiator is a product-builder’s view of controls: every intelligent action should be traceable to business context and evidence.
Control intelligent execution
Embed policy, approvals, transaction lineage, anomaly detection, reconciliation and evidence into AI-assisted finance workflows.
AI controls & assuranceProductize what works
Turn repeated enterprise problems into reusable products, implementation accelerators, patents and durable operating capabilities.
Product portfolioBuilt and incubated
Products for the layer between AI intent and enterprise action.
Two enterprise platforms anchor the portfolio: one governs how AI is adopted and executed; the other explains and evidences what happened across transactions.
Enterprise AI operating layer01
EnterpriseOS
A governed operating layer for opportunity intake, agent controls, reusable delivery playbooks, value tracking and continuous assurance.
Explore EnterpriseOSTransaction intelligence02
NexLedger
Cross-system transaction correlation, causal reconciliation and verifiable evidence for financial, operational and compliance decisions.
Explore NexLedgerVenture studio
Products and advisory work are developed through Infalytics , a product-led AI consulting company.
Selected enterprise work
Architecture that survives contact with finance operations.
Client details remain confidential. These examples show the type of problem, architecture and outcome behind the work.
Connecting commercial subscriptions to controlled financial execution
Designed the operating model and system architecture spanning CRM, order management, subscription billing, revenue recognition, accounts receivable and integration.
- Aligned commercial and financial subscription layers
- Defined controls for amendments, billing and revenue
- Structured cross-system reconciliation and cutover governance
Turning mixed monetization models into one scalable architecture
Translated subscription, usage, hardware, software, services and channel complexity into clear process boundaries and platform responsibilities.
- Mapped product and pricing patterns to operational flows
- Designed Oracle and Salesforce integration boundaries
- Improved decision clarity across finance, sales and IT
Moving reconciliation from comparison to causal explanation
Defined a transaction-intelligence approach that traces exceptions to their origin, ranks downstream impact and preserves a machine-readable evidence trail.
- Cross-system transaction lineage
- Control-aware anomaly prioritization
- Audit-ready reasoning manifests
Patent-backed innovation
New mechanisms—not recycled labels.
The invention portfolio focuses on specific technical gaps: explaining where financial discrepancies originate, preserving verifiable AI reasoning, orchestrating intent responsibly and matching people to real-world contexts.
View selected invention themesCausal Financial Reconciliation
Reconstructs a financial transaction across systems, traces backward from an observed discrepancy, identifies the earliest causal break and produces an impact-ranked explanation.
Verifiable AI Reasoning Manifests
Creates a durable manifest of the evidence, policy checks, model inputs, approvals and actions behind an autonomous or AI-assisted financial decision.
Cognitively-Gated Intent Orchestration
Coordinates commercial decisions through explicit intent, cognitive readiness, ethical constraints and decentralized participation rather than engagement maximization alone.
Current thinking
Notes from the intersection of finance, architecture and AI.
Enterprise AI needs a control layer, not another dashboard
The hard part of enterprise AI is not generating an answer. It is deciding whether that answer is grounded, permitted, executable and independently auditable.
Read perspectiveWhy Quote-to-Cash is the proving ground for agentic finance
Q2C concentrates commercial intent, customer commitments, pricing, fulfillment, billing, revenue policy and cash. That makes it the ideal place to test whether enterprise agents can operate responsibly.
Read perspectiveReconciliation should explain where the transaction broke
A variance report tells you that two numbers differ. A causal reconciliation system tells you which upstream event created the difference and what else it affected.
Read perspectiveA useful first conversation