Find where financial leakage and compliance gaps hide—faster and at scale.

Foretale.ai is an AI-powered platform that helps enterprises analyse, investigate, and monitor financial leakage and identify policy and compliance gaps — faster and at a scale manual analytics can't match.

It finds financial leakage, compliance gaps, and control weaknesses buried in enterprise ERP data—across accounts payable, revenue, GL, procurement, expenses, controls, and regulatory risk—without weeks of manual analytics.

Analyze. Investigate. Monitor. The platform follows Understand → Plan → Execute → Explain: it learns your business context, plans analytics, runs risk tests at scale, and returns evidence-backed findings your experts can review.

Finance, audit, risk, and compliance teams that need to find financial leakage and compliance gaps faster and at a scale manual analytics can't match.

Yes. The Understand step maps enterprise data, relationships, policies, manuals, feedback, and business context automatically—so teams do not need to build or maintain manual data models before analysis can start.

It ingests and interprets ERP and related transaction datasets in a dedicated workspace, learns how entities and processes relate, and keeps analytics aligned to that context as you assess risk across large volumes of enterprise data.

You can execute hundreds of risk analytics across millions of transactions and generate customized analytics as business needs evolve—without writing scripts or rebuilding a new pipeline for every test.

Yes. Results surface in executive dashboards and audit-ready reports. Custom visualizations can be configured to match how your team reviews risk and reports to leadership.

Yes. Findings move from detection into investigation workflows so teams can validate, escalate, track, and close cases with evidence—replacing fragmented BI, copilot, case management, and reporting tool stacks.

Yes. Teams can ask questions of their data in plain language—for example to find duplicate payments or explain a top case—and receive evidence-backed answers tied to the underlying transactions.

Yes. Every finding includes supporting evidence and lineage so experts can trace how a conclusion was reached and produce audit-ready reporting.

foretale.ai runs on an AWS-native stack with encryption in transit and at rest, dedicated workspace isolation, least-privilege access, and security controls aligned to our Information Security and Access Control policies.

foretale.ai runs as managed SaaS by default. Enterprise deployments can also use private cloud or on-prem so data residency and architecture match your security requirements.

No. Customer content is not used to train third-party foundation models. AI features use only data authorized for the authenticated user and project, with session- and organization-scoped context.

AI assists professional judgment—it does not replace it. We apply human oversight, Bedrock Guardrails, bias and safety reviews, and documented limitations. Details are in our Responsible AI Policy and AI Transparency Statement in the Trust Center.

Yes. Findings are explainable with supporting evidence. Users can inspect reasoning and sources where available, and high-impact actions require human authorization before changing customer data.

Access uses authenticated accounts (Amazon Cognito), role-based authorization, and least privilege. Organizations control who can see which projects and data within their dedicated workspace.

Our Trust Center publishes policies on information security, access control, incident response, data retention, responsible AI, AI transparency, vulnerability disclosure, and architecture at foretale.ai/trust-center and hexango.com/trust-center.

Personal data is handled under our Privacy Policy. Retention and secure deletion follow project and contractual settings described in the Data Retention Policy—customers control how long operational data is kept.