AI adoption starts engagement by engagement
Teams adopt web tools, copilots, models, and internal assistants through local practices and delivery programs.
Professional services + BPO
AI is already inside service delivery. Can you account for it?
Proxon gives practice leaders, operations, knowledge, risk, 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.
Trusted by
- Struct
- Simply180
- Embrace
- Emax
- Ontop
- Ant International
- Influur
- Platzi
- Cobre
- Credit Ninja
- LaHaus
- Virima
- R2
- Fengate
- Peaking.ai
- Ottermon
- Edge Surgical
- Infinity Natural Resources
- Startti
- Basic Fun
The control gap
AI adoption is moving faster than the delivery model around it.
Research, drafting, agent assistance, quality review, and knowledge work can span many teams, tools, and client contexts. The operating record often stays fragmented.
Quality has many owners
Practice leaders, operations, knowledge, risk, security, and client teams each hold a different part of the review process.
Cost is separated from delivery context
Subscriptions, model usage, and shared platforms create spend records that do not automatically explain which registered work they support.
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.
AI tools, models, agents, and connected services
Bring observed activity and registered systems into a shared inventory, with providers and data sources recorded alongside them when that context exists.
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
A common record across client and operating teams
Give each function the observed activity, registered workflow, adoption, and spend context it needs while delivery leaders retain responsibility for quality and client outcomes.
Practices and client teams
Understand which registered and observed AI systems support research, drafting, analysis, and service delivery.
Service operations and quality
Review attributable adoption and registered workflow signals across delivery groups and operating programs.
Risk, security, legal, and knowledge
Bring current evidence into client, information-security, vendor, and professional-review processes.
Finance and procurement
Review available provider, model, team, and workflow spend context before commitments are renewed.
Where AI enters service work
One delivery model, many AI-assisted workflows
Professional-services and BPO teams can register the work they want to review while Proxon keeps observed systems, attributable adoption, and available spend context alongside it.
Research and analysis
Source finding, synthesis, issue framing, and briefing preparation
Drafting and production
First drafts, document review, presentation support, and formatting
Case and interaction summaries
Call notes, case summaries, handoffs, and follow-up preparation
Agent assistance
Knowledge retrieval, response support, procedural guidance, and escalation preparation
Quality review
Checklist support, sample review, coaching inputs, and exception summaries
Knowledge operations
Policy search, reusable methods, learning material, and workflow documentation
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. Engagement and operations leaders remain responsible for client advice, confidentiality, staffing, quality, and delivery decisions.
The operating view
Answer the questions behind every delivery review
Connect observed adoption, available spend context, and registered workflow signals without treating activity as proof of utilization, quality, or client impact.
Adoption
See where AI is becoming part of service delivery
Where identity exists, compare observed adoption across people, teams, practices, and departments so enablement can respond to uneven uptake.
Cost intelligence
Give operations and finance a shared view of AI spend
Review captured and provider-reported spend by provider, model, team, and registered workflow when those dimensions are available.
Workflow outcomes
Review reported delivery signals beside registered workflows
Keep curated or reported outcome signals connected to adoption and cost while service owners retain responsibility for quality and client results.
Bring delivery into view
Bring one AI operating record to the next client, quality, or budget review.
See observed and registered systems, attributable adoption, registered workflows, and available spend context across client delivery and service operations.
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