Investigate evidence, model systemic risk, align with regulation, control actions and supervise agent workflows. Each product addresses a different part of the work.
How should this agent workflow recover and continue?
Supervised handoffs, targeted recovery and approval history.
Use a product on its own or scope the handoff between products during a pilot.
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Evidagio
Local workspace + self-serve access
See what the evidence supports.
Import supplied test results, recalculate the measurements and investigate gaps. Keep the acceptance criteria, source references and review history together in an exportable record.
The local workspace calculates in your browser. The hosted AI investigator helps interpret supplied evidence; people make the acceptance decision.
Free and paid individual subscriptions are available. Team offerings begin with a scoped pilot.
Connect a requested action to its permissions, supporting evidence and operating limits. Recheck the decision when relevant evidence changes, and keep a record of what was evaluated.
Explore action checks across twelve system examples. Inspect allow, limit, hold and reject decisions, then export a policy decision receipt.
The example simulates enforcement locally. Connecting it to a real system requires an adapter that controls the action.
You already have an orchestrator. Now assign a first chair to lead it.
Give an existing orchestrator a first chair to supervise agent handoffs. Resolve conflicting outputs, recover failed branches and bring the evidence to the person who approves the next step.
Explore an example workflow to see how that supervision fits around an existing orchestrator.
Turn red-team findings into regulatory and policy alignment.
Syntony’s proprietary LLM analyzes red-team findings against AI regulatory requirements worldwide and policy frameworks. Identify applicable requirements, explain gaps and produce source-linked alignment recommendations.
Create or revise internal policy frameworks, compare obligations across jurisdictions and connect each recommendation to controls, evidence and follow-up tests.
The walkthrough uses illustrative data. Pilot scope, access and timing are agreed with your team.
Understand how the system produces risk over time.
Systems-thinking and system-dynamics software for red-team risk mapping. Build causal maps, identify reinforcing and balancing feedback loops, examine delays and cumulative effects, and compare interventions.
Connect findings to the model’s assumptions and evidence. Use scenario comparisons to prioritize what to test or change, with uncertainty and model boundaries visible.
