Policy before output
The applicable rule, operating procedure, approval threshold or regulatory reference is applied before the model’s recommendation can become an action — not argued about after.
An accountable AI platform for regulated operations.
CloudSeals builds the evidence and control layer for accountable AI. In practice that means three things around every AI-assisted decision: a policy boundary applied before the action, a person with authority who approves it, and evidence that survives afterwards. TrustOps is the discipline; Evidence Fabric is the record; LedgerSight, CompliSight and CarbonSight are where it runs.
What “accountable” means here
Most AI failures in regulated work are not model failures. The model answers; what is missing is the boundary it answered inside, the person who accepted it, and the record that survives afterwards. An accountable AI platform supplies those three things and keeps them together. AI recommends; people and policy decide.
A person stays accountable
Recommendations are reviewed and approved, rejected or overridden by someone who holds the authority to do so. The application does not act on its own.
Evidence outlives the decision
What was proposed, on what basis, who approved it and what followed are written to Evidence Fabric as the work happens, in one record shape across every application.
Who remains responsible
The organisation sets the policy boundary; the reviewer who approves within it is responsible for the decision; the auditor or regulator checks the record. CloudSeals applications do not post ledger entries, make payments, confirm or close safety incidents, or assure carbon figures. They prepare the decision and keep the evidence.
How a policy boundary and a human approval work
The TrustOps loop is the same in every application: observe the data already in the systems in place; reason to an exception and a proposed next action with its context; apply the policy boundary; assess the risk in operational context; route to a person with authority to approve; execute only what was approved; retain the evidence. The boundary and the approval are configured for each deployment — which rules apply, who may approve what, and within which limits.
What is retained
Source context
The system, file, sensor, ledger or workflow source the AI-supported step used.
Model or agent, and purpose
Which model or agent produced the recommendation, and what the step was for.
Policy and authority context
The rule that applied and who was entitled to approve.
Outcome, approvals and overrides
What was proposed, what the person decided, and any override with its reason.
Downstream actions and final results
What was carried out after approval and what the result was.
Retention and access
Retention is configured per deployment; access runs through identity and access management and is itself recorded. See Security.
Where it runs today
LedgerSight — finance operations
Reconciliation, exceptions and approvals for finance teams in regulated operations. Recommendations are reviewed before any approved business action.
CompliSight — industrial site safety
Configured PPE and site-safety detection from approved CCTV feeds already in place; a supervisor reviews and closes every alert.
CarbonSight — carbon MRV
Source data, approved calculation method, exception review and evidence an assurer can check. The assurance opinion comes from the assurer.
All three are generally available. Deployment model, connected systems and retention are agreed per engagement and settled in a security review.
How this differs from AI governance software
AI governance platforms typically inventory an organisation’s models and agents, classify their risk, map them to frameworks such as the EU AI Act or ISO/IEC 42001, and monitor them. CloudSeals does something narrower and more operational: it runs specific finance, safety and carbon workflows with the boundary, the approval and the evidence built into each decision. TrustOps is not a governance product or a certification; it is how governance decisions reach day-to-day work. For a neutral comparison of the two kinds of platform, see Evaluating AI governance platforms.
Where CloudSeals is not a fit
A registry for all your AI
CloudSeals does not inventory or classify third-party models and agents across an enterprise.
Model testing and monitoring
It does not offer fairness or bias testing, explainability tooling or drift monitoring for models it did not build into a workflow.
A compliance guarantee
It records what happened. Whether that satisfies a given regulation is judged by your auditors and regulators.
Autonomous action
If you want AI that acts without a person approving, this is the wrong platform by design.
Current limitations
Integrations, deployment models and retention settings are agreed per engagement and are not listed publicly. CloudSeals does not publish customer references, certifications or performance statistics on this site; ask for a security review and a walkthrough on your own data. Governed digital twins remain a research direction, not a product.
Questions we get asked
What is an accountable AI platform?
At CloudSeals, an accountable AI platform is software in which AI recommends and people and policy decide: a policy boundary is applied before any action, a person with the authority to approve reviews the recommendation, and the evidence behind the decision is retained so someone who was not there can follow it later.
Is TrustOps the platform?
No. TrustOps is the operating discipline — how the work is done. The platform is the set of applications that implement it (LedgerSight, CompliSight and CarbonSight) together with Evidence Fabric, the shared evidence layer underneath them.
Who is responsible for a decision made with CloudSeals?
The person who approved it, within the policy their organisation set. CloudSeals applications do not post entries, make payments, close safety incidents or assure carbon figures on their own.
Does CloudSeals govern AI systems from other vendors?
Not today. CloudSeals applications govern the AI-assisted decisions inside their own workflows. They are not an inventory, registry or monitoring layer for third-party models and agents; buyers who need that should compare platforms built for it.
What evidence is kept?
Evidence Fabric records source context, model/agent details, purpose, policy/authority context, outcomes, approvals/overrides, downstream actions and final results for each AI-assisted decision.
Are the products available now?
LedgerSight, CompliSight and CarbonSight are generally available. Deployment model, connected systems and retention are agreed per engagement and confirmed in a security review; they are not listed publicly.