Our Services

Verification at every layer

From real-time output checking to comprehensive model validation — five service pillars designed for enterprise AI governance.

Audit trail log showing timestamped verification events and source references

Verification & Audit

Our flagship service embeds truth-checking directly into your AI pipeline. Every output is cross-referenced against verified knowledge bases before delivery to end users.

"An unaudited AI output is an unverified claim. We treat verification as a first-class infrastructure concern, not a post-hoc review step." VeritasAI Methodology Guide
  • Real-time fact validation with sub-200ms latency
  • Immutable audit trails with full prompt context logging
  • Source citation mapping for every verified claim
  • Integration with existing LLM and RAG architectures

Hallucination Reduction

Proactive detection and mitigation of AI hallucinations before they reach customers, patients, or regulatory reviewers. Our multi-layer approach combines semantic analysis with ground-truth benchmarking.

  • Pre-generation constraint enforcement
  • Post-generation claim verification and flagging
  • Confidence thresholds with automatic human escalation
  • Continuous learning from verified correction feedback

Trust Scoring

Quantified confidence metrics for every AI output, enabling risk-based routing, automated review triggers, and transparent reporting to stakeholders and regulators.

  • Multi-dimensional trust scores (source, semantic, temporal)
  • Configurable thresholds per use case and jurisdiction
  • Dashboard analytics for model performance trends
  • API endpoints for downstream decision automation

Compliance Reporting

Automated generation of regulatory documentation aligned with Singapore PDPA, MAS technology risk guidelines, and international AI governance frameworks.

"Regulators no longer accept 'the model said so' as evidence. Compliance reporting must demonstrate the verification chain." VeritasAI Regulatory Affairs, 2025
  • PDPA-aligned data handling and consent documentation
  • Model governance reports for board and audit committee review
  • Automated incident reporting for verification failures
  • Custom report templates for sector-specific requirements

Model Validation

Structured benchmarking and validation protocols for LLM deployments. Reproducible test suites, performance scorecards, and deployment readiness assessments.

  • Pre-deployment validation with domain-specific test suites
  • Continuous post-deployment monitoring and drift detection
  • Comparative benchmarking across model versions
  • Third-party audit support and documentation packages

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