Evidence infrastructure for AI-mediated work
Verify the work your AI agents claim to have done.
TimeToPoint creates evidence records for AI-mediated and delegated work — linking actor, authority, action, system-of-record confirmation, human review, and business outcome so claims can be reviewed, accepted, challenged, paid, or defended.
Built for CFOs, CTOs and risk teams moving AI-mediated work from controlled use to approved operation — and for the assurance partners (certifiers, insurers, advisory firms) who underwrite that move.
Evidence first. You decide the pace.
EU AI Act prohibited-practices penalty range — up to €35M or 7% of global turnover.
of leaders expect digital labour to expand workforce in 12–18 months.
The agent said "done." That doesn't mean it happened.
The most dangerous failure pattern in agentic workflows is false task completion. The agent reports completion. The transcript reads cleanly. Activity logs show events. None of it confirms the work actually happened in the system that matters — the system of record.
The vendor confirmation email was never delivered. The deployment script ran but the deployment didn't take. The case wasn't resolved — it was marked resolved. The £40,000 invoice was approved without the dual-approval workflow being triggered.
"Self-report is the enemy. Require receipts."
Observability shows how the agent behaved. Evidence shows whether the work should count.
AI observability tools — LangSmith, Galileo, Arize, Langfuse, AgentOps, Datadog LLM Observability, Fiddler — help builders debug agents. They show traces, tool calls, token usage, latency, hallucination rates, and reasoning paths. They are the right layer for engineering teams.
They are not built to answer the next question: does this action count? Should this work be accepted, paid, reviewed, challenged, or defended?
TimeToPoint sits between observability and business acceptance: turning selected traces, tool calls, human approvals, and system-of-record confirmations into reviewer-readable records that can travel to the person who actually has to decide.
From action to attribution — six layers, one record.
When AI, humans, and delegated systems do work together, six attribution questions need answers. TimeToPoint links them into one structured record.
Layer 1 — Input
What data, prompt, instruction, source, or upstream work entered the workflow?
Layer 2 — Contribution
Who contributed which part — AI draft, human edit, AI analysis, human judgement, external source, prior asset?
Layer 3 — Authority
Under whose mandate did the action happen — agent identity, user identity, delegated authority, permission scope?
Layer 4 — Review
Who accepted, edited, rejected or escalated the output — reviewer identity, timestamp, override reason?
Layer 5 — Outcome
What happened after the work was used — accepted result, downstream error, dispute, financial impact, KPI movement?
Layer 6 — Credit / Pay
How should value, credit or compensation be distributed — contribution percentages, source weighting, payout rule?
Layers 1–4 are operational. Layer 5 becomes important for outcome-based AI execution. Layer 6 becomes important when AI-human work moves from cost centre to revenue instrument.
From trust to proof.
In 2023, the question was: can we trust this AI? In 2026, the question is: can we prove what it did, why it did it, and under which guardrails?
The shift matters because trust is asserted; proof is inspected. Trust may open the boardroom conversation. Proof is what insurance underwriters, regulators, litigation counterparties, and audit committees need to inspect.
TimeToPoint is the proof layer.
Built for the people who need the evidence — and the people who underwrite them.
Direct buyers
CFOs verifying AI-assisted vendor bills, agent usage costs, and outcome-based AI execution before payment.
CTOs deploying internal AI agents and needing evidence to clear procurement, risk review, and AI assurance.
Risk, audit and assurance teams who need a record they can inspect — not a dashboard they have to interpret.
Assurance partners
ISO 42001 certifiers for whom TimeToPoint can serve as the evidence layer beneath audit work.
Insurance carriers and underwriters of AI E&O / D&O cover requiring clearer evidence packs.
Advisory and audit firms embedding TimeToPoint inside their AI assurance methodology.
Clear boundaries make the evidence stronger.
TimeToPoint is not a time tracker, LMS, webinar platform, employee-monitoring product, generic AI dashboard, engineering observability replacement, certification body, or legal-advice tool.
It does not adjudicate output quality. It does not decide whether to pay. It does not replace observability, identity, finance or workflow tools. It does not certify compliance.
It is the evidence and attribution layer for high-accountability workflows where action, authority, review and proof need to stay connected.
Frequently Asked Questions
Bring us a workflow where AI claims to have done the work — and the business needs evidence before it can trust, pay, accept, or scale it.
From £1,500 + VAT. 10 working days. One month of billing data. Your evidence stays private.