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AI Observability & ROI · Solution Datasheet

Know what your AI is doing.
And what it is worth.

AI agents are in production, and the questions have changed. Is it working? When it gets something wrong, who is accountable, the agent or the person who approved it? And is it paying back what it costs? Evolvable.ai answers all three: it traces every run end to end, attributes every decision to an agent version or a named person, and measures the value against baselines you set.

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Prompt"Refund duplicate charge on #4471"

Teams · 0.2 s

RetrievalRefund policy v3
2 chunks matched

1.2k tokens · 0.8 s

Tool calls
get_transactions
refund_payment

2 calls · 1 gated

Approvalpaused for sign-offFinance approver · Slack
Approved in 11 min

within SLA · reason logged

ReviewOps reviewer
Marked reviewed

3 min human time

ResponseRefund issued, customer notified

12.4k tokens · $0.04

Illustrative figures
The value of this run
Manual baseline45 min
Human time spent14 minapproval + review
AI cost$0.04
Net time saved31 min

Rolls up to Finance Ops › Refunds

Who is accountable for what: **The answer** · Finance Assistant v14, owner: Finance Ops. **The refund** · approved by a named finance approver, with reason. Every step in one trace.

What you get

Explain any outcome

Open one run and see every prompt, retrieval, tool call and decision in order.

Accountability for both

Every outcome traces to an agent version or a named person, never to "the system".

Spend you can defend

Hours saved, net of human review, next to what the models actually cost.

Fix it before users notice

Error rate, quality and response time tracked per agent, per model, per version.

Built for

01CIO & Chief AI OfficerOne view of every agent, its quality and its cost.
02CFO & financeA return on AI that holds up to scrutiny.
03COO & business ownersKnow which processes AI improves, and by how much.
04AI & platform teamsDebug, tune and compare agents with real evidence.

Questions you can answer in one click

  • Which agent costs the most per resolved case?
  • Did version 15 make the answers worse?
  • Who approves slowest, and is the SLA holding?
  • How often does the AI judge escalate to a person?
  • What did Finance Ops save last quarter?
  • Which model is worth its price?

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quality KPIs on every agent and model, tracked over time

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levels of drill-down: fleet, agent, single run

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kinds of accountability: the agent version and the person who decided

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ROI model, with your own baselines and hourly rates

Trace. Monitor. Account. Measure.

Visuals are illustrative, except Account (product screenshot). Figures are examples.

01Trace

Why did the agent do that?

  • End-to-end traces: prompts, retrievals, tool calls, sub-agent hand-offs and approvals, with the agent version that ran.
  • Every conversation and workflow run searchable by agent, user, channel and status.
  • Raw logs of every model call and tool call, for the engineer who needs the detail.

Any outcome explained in minutes, not days.

Conversation traceFinance Assistant · v14
Prompt"Refund the duplicate charge on #4471"
RetrievalRefund policy v3 · 2 chunks
Tool callget_transactions(#4471)
Approvalrefund_payment · approved in 11 min
ResponseRefund issued, customer notified

12.4k tokens · $0.04 · 18 s · 0 guardrail hits

02Monitor

Are agents fast, accurate and reliable?

  • Quality KPIs: response time, error rate, guardrail violation rate and eval pass rate, per agent and model.
  • Usage & tokens: conversations, messages, tool calls and token use, by model.
  • Live supervision: flagged conversations routed to supervisors, with escalation.

Problems found before users report them.

Agents breakdownLast 30 days
AgentResp.ErrorsGuardrailEval pass
Finance Assistant2.1 s0.2%1.1%98%
SOC Triage Agent9.8 s0.0%0.0%95%
Customer Support23.5 s4.8%2.3%81%

Customer Support · error rate up since v9 · open traces

03Account

Who decided: the agent or a person?

  • Every decision attributed to an agent version, or to an approver, their group and the channel used.
  • Human performance: median time to decide, decisions within SLA, AI-judge approval rate, reviews waiting.
  • Oversight that scales: veto windows and AI judges cut approval queues without removing people.

A named owner behind every outcome.

Inbox2
Expiring within 24h0of 0 waiting for a decision
Oldest review waiting1d 21h2 peer review items
Median time to decide11 minhuman decisions
Decided within SLA—no gates with an SLA set
Judge approved50%2 of 4 judged items
Running autonomously238 / 261agents · see coverage
InboxHistoryGatesGroupsSLA policiesCoverage
04Measure

Is AI paying back what it costs?

  • Your baselines: set the manual time and hourly rate per task; savings are counted net of human review.
  • Real AI spend: token counts priced with your own model rates, for every agent and workflow.
  • Roll-ups by business unit: a tag tree from department down to task, with CSV export.

A return on AI you can defend to the CFO.

ROI by business unitLast 90 days
Hours saved1,240 hnet of 86 h human review
Cost saved$62,000at your hourly rates
AI spend$3,100tokens × model pricing
Return20×
Finance operations$27,500
Security operations$21,000
Customer service$13,500

Baselines and hourly rates set by you, per task, in the configurator

Sovereign by design

Observes every model you run

  • OpenAI
  • Azure OpenAI
  • Anthropic
  • Google Gemini
  • Ollama & open-weight
  • Runs on your cloud, private cloud, on-premises or air-gapped. Traces and usage data stay in your estate.
  • Flat annual licence, unlimited users. Bring your own models and price them at your own rates.

Take it with you

The AI Observability & ROI datasheet

Share it with your CIO, your CFO and the team that runs your agents.

PDF · 2 pages · 0.8 MB

Download PDF

See what your AI is worth.
In 45 minutes.

We connect one live agent, agree a baseline with you, and walk you through the trace, the accountability trail and the ROI.

Book a demoContact us
info@evolvable.ai