Embedded AI stays off the inventory
Models now arrive inside SaaS products, internal applications, copilots, and agent workflows. Each entry point adds another provider and dependency to understand.
Financial services
AI is already inside the financial stack. Can you account for it?
Proxon gives product, risk, security, finance, and operations one management layer for the AI tools, models, agents, registered workflows, observed adoption, and captured spend across the institution.
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The control gap
AI adoption is moving faster than the control environment.
AI now supports customer-facing and internal work across financial services. The systems behind that work often enter through different budgets, vendors, teams, and review processes.
Accountability fragments
Product, security, risk, compliance, procurement, and finance each hold part of the picture. Reviews slow down because the underlying records do not line up.
Spend loses business context
Model APIs, cloud services, licenses, and team experiments create separate cost trails. Finance sees the bill before it sees the work behind it.
The management layer
One operating view of the AI estate
Proxon brings the records behind AI adoption, systems, workflows, and captured spend into one product. Each function can use the view its responsibilities require.
Tools, models, agents, and MCP servers
Bring observed activity and registered systems into a shared inventory, including providers and data-source destinations that appear in the estate.
Usage by people, teams, and departments
See where AI is becoming part of daily work and where adoption remains isolated to a few individuals.
Captured spend with organizational context
Review attributable model spend by provider, model, department, team, and workflow when that context is available.
Registered use cases and outcome signals
Keep curated workflows and their reported outcome signals alongside the systems and teams involved in the work.
Shared operating model
A common record across the lines of defense
The same AI estate looks different depending on the job. Proxon gives each stakeholder current records to bring into the governance, risk, procurement, and assurance processes they already run.
Product, engineering, and operations
Understand which AI systems support customer and internal workflows, how teams use them, and where captured spend is growing.
Risk, security, and compliance
Bring a current view of registered and observed AI activity into model, technology, third-party, and operational-risk reviews.
Internal audit and assurance
Review the same inventory, adoption, workflow, and spend records used by the business and its oversight functions.
Finance and procurement
See where captured AI spend concentrates across providers, models, and teams before budgets and vendor commitments are set.
Where AI enters financial work
One estate, many material workflows
Financial institutions are applying AI across customer, control, and operating functions. Proxon keeps the systems, usage, registered workflows, and captured spend around that work visible in one management layer.
Lending and underwriting
Document review, analyst copilots, research, and decision support
Financial crime operations
Alert triage, case summaries, investigator assistance, and quality review
Customer service and complaints
Agent assistance, summarization, knowledge retrieval, and interaction review
Markets and wealth
Research, meeting preparation, communications support, and advisor workflows
Engineering and resilience
Code assistants, incident analysis, runbooks, and back-office automation
Risk and compliance
Policy search, regulatory research, control testing support, and reporting preparation
Proxon manages the layer around this work: the AI systems in use, observed adoption, registered workflows, and attributable spend. Business owners remain responsible for lending, fraud, investment, and customer decisions.
The operating view
Answer the questions behind every review
Move from separate inventories and invoices to a connected view of AI adoption, captured spend, and registered workflow outcomes.
Adoption
See where AI is becoming part of the operating model
Compare adoption across people, teams, and departments. Give enablement leaders a clearer view of where proficiency is spreading and where it has stalled.
Cost intelligence
Give finance an explainable view of AI spend
Break captured model spend down by provider, model, department, team, and registered workflow so budget discussions start with shared context.
Workflow outcomes
Review the work alongside the outcome signals teams report
Keep registered workflows and curated outcome signals connected to adoption and cost. Leaders can compare investments without turning every use case into a separate spreadsheet.
Bring the estate into view
Bring a defensible AI record to the next budget, risk, or board review.
See the tools, models, agents, registered workflows, observed adoption, and captured spend already shaping your institution.
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