Agents multiply across the toolchain
Developer tools, model endpoints, internal services, and MCP servers can each add a new system for platform and security teams to understand.
Software, cloud + developer tools
AI is already inside the software delivery stack. Can you account for it?
Proxon gives engineering, platform, product, security, and finance teams one shared record of observed and registered AI systems, attributable adoption where identity is available, registered workflows, and available spend context.
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The control gap
AI delivery is moving faster than the operating record around it.
Coding assistants, model APIs, cloud services, copilots, and internal agents enter through different teams and budgets. The evidence behind them rarely arrives in one place.
Ownership changes with the code
Experiments become shared workflows while repositories, services, maintainers, and review responsibilities keep moving.
Usage and spend split apart
API charges, subscriptions, and team-level tools create separate records that make budget discussions harder to connect to the work.
The management layer
One operating view of observed and registered AI
Proxon brings the records behind observed AI adoption, registered systems and workflows, and captured or provider-reported 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.
Attributable usage across the organization
Where identity is available, compare observed adoption across people, teams, and departments without turning presence into a performance judgment.
AI spend with organizational context
Review captured and provider-reported spend by provider, model, department, team, and registered workflow when that context is available.
Registered use cases and outcome signals
Keep registered workflows and their curated or reported outcome signals alongside the systems and teams involved in the work.
Shared delivery model
One AI record across the software organization
Engineering, product, security, platform, and finance need different views of the same observed activity, registered systems, and attributable spend.
Engineering and developer experience
See where observed AI activity is entering development work and which registered systems teams rely on.
Platform and reliability
Keep providers, models, agents, MCP servers, and registered workflows in the same operating record.
Security, risk, and legal
Bring observed and registered activity into the software assurance and vendor-review processes already in place.
Finance and procurement
Review captured and provider-reported spend before budgets, licenses, and provider commitments are set.
Where AI enters software delivery
One stack, many AI-assisted workflows
Proxon keeps observed and registered systems, attributable usage, registered workflows, and available spend context together as AI moves through the delivery lifecycle.
Coding and refactoring
Code assistants, repository work, migration support, and documentation
Review and testing
Review assistance, test generation, quality checks, and release preparation
Incidents and reliability
Incident analysis, runbook assistance, summaries, and follow-up work
Product discovery
Research, specifications, prototyping, and feedback synthesis
Customer engineering
Support investigation, solution design, knowledge retrieval, and response drafting
Internal automation
Agents, MCP servers, model APIs, and back-office workflows
Proxon manages the layer around this work: observed and registered AI systems, attributable adoption where identity exists, registered workflows, and spend where context is available. Engineering and business owners remain responsible for code quality, security, releases, and customer decisions.
The operating view
Answer the questions behind every delivery review
Connect observed adoption, captured or provider-reported spend, and registered workflow signals without treating activity as proof of code quality or business impact.
Adoption
See where AI is becoming part of software delivery
Where identity exists, compare observed adoption across people, teams, and departments so enablement can focus on the parts of the organization that need it.
Cost intelligence
Give platform and finance shared spend context
Review captured and provider-reported spend by provider, model, team, and registered workflow when those dimensions are available.
Workflow outcomes
Keep delivery signals beside the workflows teams register
Review curated or reported outcome signals with adoption and cost while engineering owners retain responsibility for quality and delivery decisions.
Bring the stack into view
Bring one AI operating record to the next platform, budget, or delivery review.
See observed and registered systems, attributable adoption, registered workflows, and available spend context already shaping software delivery.
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