Business analyst

Sai Kiran • Peerlist

<h3>Carbon MRV &amp; Verification Evidence</h3><ul><li>CarbonSight organizes operational and sustainability data into a traceable MRV workflow, encompassing source data, calculations, methodology, reviews, exceptions, and evidence.</li><li>It is designed for reporting and assurance teams, focusing on the traceability of source data, approved calculation methods, and the review of exceptions before an external assurer evaluates the results.</li><li>The platform allows teams to trace source data, apply approved calculation methods, surface and review exceptions, and export evidence for assurance and reporting.</li><li>CarbonSight integrates with operational and sustainability data, maintaining the origin of each figure for complete traceability.</li><li>Approved calculation methods are applied, with the method and version recorded as part of the data.</li><li>Missing or inconsistent source data is flagged as an exception for review, rather than being estimated.</li><li>Decisions made during the review process and the reviewer are recorded.</li><li>Source context, method, review, exceptions, approvals, and final results are stored in Evidence Fabric.</li><li>Evidence is exported in a format suitable for assurance and reporting, with the final assurance opinion provided by an external assurer.</li><li>CarbonSight is positioned around verification and assurance, not as a tool that certifies companies or assumes universal applicability for regulations like CSRD.</li><li>It does not invent data; all figures trace back to source data and an approved calculation method.</li><li>The platform organizes evidence for assurance and reporting; the assurance opinion is external.</li><li>When source data is missing, it is surfaced as an exception for review.</li><li>CarbonSight is part of the CloudSeals product portfolio, implementing the TrustOps operating discipline with Evidence Fabric as the shared evidence layer.</li></ul>

Carbonsight

<h3>CompliSight is an AI-powered solution designed to enhance industrial safety supervision by analyzing existing CCTV feeds. It focuses on detecting deviations from configured Personal Protective Equipment (PPE) and site-safety rules, providing actionable alerts to supervisors. The system generates a comprehensive evidence record for each detection, review, and closure, strengthening audit trails and operational oversight. CompliSight is built with a commitment to accountable AI, emphasizing narrow, configured rules, privacy controls, and human review in its workflow. It does not infer worker emotions or replace the responsibilities of safety management teams. The platform integrates with existing CCTV infrastructure, requiring no new camera installations. Key features include:<ul><li><strong>Configured Detection:</strong> Identifies review-worthy events based on agreed safety rules and camera context.</li><li><strong>Human Review:</strong> Provides supervisors with essential context before confirming or closing an incident.</li><li><strong>Evidence Record:</strong> Retains visual context, status, reviewer, and follow-up actions for auditability.</li></ul>CompliSight is deployed for site-safety and operations teams in industrial environments, supporting supervisors and auditors with robust evidence for compliance and safety management. It is part of the CloudSeals TrustOps operating discipline, leveraging Evidence Fabric for its record-keeping capabilities.</h3>

CompliSight

<h3>LedgerSight offers finance operations automation with explainable and controllable AI. It connects finance and operational data to identify exceptions, propose next actions, and maintain evidence for every decision. Designed for teams seeking enhanced automation without compromising financial control, LedgerSight focuses on core workflows such as bank reconciliation, AR reconciliation, AP exception handling, collections prioritization, cash allocation, financial close support, and forecast/exception analysis. The platform allows users to review AI recommendations, exception context, and supporting evidence before approving business actions, thereby reducing risks associated with late-surfacing reconciliation breaks, unapplied cash, and incorrect supplier payments. LedgerSight operates alongside existing finance and operational systems, ensuring that finance control is not turned into a black box. Its step-by-step process involves reading existing data, identifying exceptions, proposing actions, applying policy boundaries, human approval, and recording decisions in Evidence Fabric. LedgerSight is part of the CloudSeals product portfolio, which includes TrustOps for accountable AI discipline and Evidence Fabric for a shared evidence layer.</h3><ul><li><strong>Core Workflows:</strong> Bank reconciliation, AR reconciliation, AP exception handling, Collections prioritisation, Cash allocation, Financial close support, Forecast/exception analysis.</li><li><strong>Key Features:</strong> Connects finance and operational data, identifies exceptions, proposes next actions, maintains evidence, AI recommendations, human approval, works alongside existing systems.</li><li><strong>Risk Reduction:</strong> Minimizes late-surfacing reconciliation breaks, unapplied cash, short payments, and provides clear audit trails for decisions.</li><li><strong>Technology:</strong> Leverages AI and Evidence Fabric for accountable operations.</li><li><strong>Deployment:</strong> Generally available, works alongside existing finance and operational systems.</li></ul>