Demetra product — AI Observability Platform
From AI activity to business value
AI Observability Platform is built to give your organisation end-to-end visibility into how AI capabilities — agents, skills, plugins, models and workflows — are actually used: who uses them, what they consume, what they cost and which of them deliver value.
In development
AI spend is visible. AI value often is not.
Organisations are investing in AI across tools, models and teams faster than they can answer basic questions about what that investment does.
Fragmented usage
AI activity is spread across assistants, agents, development tools, pipelines and internal systems — no single view shows what is actually in use.
Cost without context
Token consumption and subscriptions accumulate as an invoice line, unattributed to the capabilities, teams or work that generated them.
Missing attribution
Raw model calls say little about which managed agent, skill or plugin did the work — or for whom.
Adoption without evidence
Active-user counts cannot distinguish enthusiastic experimentation from capabilities that change how work gets done.
Organisational blind spots
Usage in one team, tool or environment stays invisible to the rest of the organisation, so learning and optimisation stay local.
From telemetry to decisions
The platform captures telemetry from AI activity across the enterprise, correlates it and attributes it to the capabilities the organisation actually manages. Collection is standards-based — including OpenTelemetry-based collection where applicable — and the attribution, analytics and dashboard layers are configured around your environment rather than a fixed vendor schema.
Core analytics
Analytics are organised around the questions AI leadership actually asks — usage, cost, attribution and effect — at the level of capabilities, models and workflows rather than servers.
Usage & adoption
Which capabilities are used, by which teams, how often — and how that changes over time.
Tokens & cost
Token consumption and cost, attributed to capabilities, teams and work contexts instead of a single invoice line.
Capability attribution
Activity and cost connected to the agents, skills, plugins and other managed capabilities that produced them.
Model & workflow views
Model-, capability- and workflow-level perspectives, designed for AI questions rather than repurposed infrastructure metrics.
Quality & effectiveness
Effectiveness and optimisation signals where the underlying platforms support them.
Trends & optimisation
Adoption and spend trends that surface optimisation opportunities and inform investment decisions.
The differentiator
Capability attribution
Tracing tools show model calls; infrastructure monitoring shows servers. AI Observability Platform is built to answer the question behind both: which managed capability did the work, and what was it worth?
The attribution layer connects raw AI activity to the capabilities the organisation defines and manages — agents, skills, plugins, workflows — and from there to teams, users, repositories and work contexts, under privacy and governance controls the organisation sets.
- Which managed capability handled the activity
- Who used it, and in which team or work context
- What it consumed and what it cost
- How its adoption develops over time
- What effect and value can be demonstrated
Dashboards for every audience
The same attributed telemetry serves operational, engineering and management audiences — from portfolio-level summaries to per-capability drill-downs. The families below reflect dashboards Demetra runs over its own AI-assisted delivery today; dimensions and layouts are configured per organisation.
Executive summary
Portfolio-level adoption, spend and value indicators for AI and platform leadership.
Capability impact
Usage, cost and effect per managed capability — which agents, skills and plugins actually carry the load.
Attribution quality
How much activity is attributed, and with what confidence — so the numbers can be trusted before they steer decisions.
Cost & adoption
Token and cost analytics set against adoption curves, for optimisation and budgeting.
Fits your telemetry stack
AI Observability Platform integrates with the systems where enterprise telemetry and work context already live. Collection is standards-based — OpenTelemetry underpins it where applicable — and the integration architecture is designed to be extended, so new sources join the same attribution model.
Integration surface
- Grafana
- Elasticsearch
- GitHub
- Jira
- Splunk
Integrations are enabled per engagement, against the systems your organisation actually runs.
Adapted to your organisation
Attribution is only useful when it reflects how your organisation defines capabilities, teams and value. The platform is configured per enterprise:
- Custom attribution dimensions and capability definitions
- Business and team metadata connected to AI activity
- Dashboards per audience — operational, engineering, management
- Integrations against your telemetry and work systems
- Privacy, anonymisation and governance rules set by your organisation
Observability that fits your AI environment
AI Observability Platform is connected to the AI tools, telemetry sources and business context that matter to your organisation. Collection, attribution rules, dashboards and integrations are configured around your environment and governance requirements — observability of this kind requires access to your AI activity, and that access is engineered deliberately.
Depending on architecture and data-control requirements, the platform can be deployed into a customer-controlled environment or delivered through an agreed managed model. Exact data sources, hosting and deployment architecture are confirmed during solution discovery — we do not promise access to data your AI vendors do not expose.
Discuss your AI telemetry architecture
Tell us what AI activity you can see today and what you cannot. We will walk through the platform against your own environment and telemetry stack.
Request an observability walkthrough