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    Evidence-First AI Stacks Retrofit and Alignment

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    A targeted retrofit that transforms AI systems into evidence-driven, traceable systems without requiring a full rebuild. It enables organizations to improve reliability and auditability of their AI systems quickly and cost-effectively.

    Overview

    This solution injects evidence capture, causal tracing, and contradiction detection into existing AI pipelines and stacks. It works as a middleware layer that intercepts inputs and outputs, enriching them with structured provenance metadata and reasoning validation. The pipelines and stacks retrofit is minimally invasive and compatible with existing architectures, including LLM-based systems and rule-based engines. It improves auditability, reduces hallucination risk, and enhances trust in outputs.

    Highlights

    • Evidence-First AI Retrofit for Traceable & Auditable Systems: Transform existing AI stacks into evidence-driven, traceable systems without a full rebuild, improving transparency and trust in outputs.
    • Reduce Hallucinations with Evidence-Driven Validation: Inject evidence capture, causal tracing, and contradiction detection into AI pipelines to minimize hallucination risk and improve reasoning reliability.
    • Seamless Middleware Integration with Existing AI Stacks: Deploy a minimally invasive retrofit compatible with LLM-based and rule-based systems, enhancing auditability and performance without disrupting current architectures.

    Details

    Delivery method

    Deployed on AWS
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    Pricing

    Custom pricing options

    Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.

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    Support

    Vendor support

    For assistance with the Evidence-First AI Stacks Retrofit, customers can contact our support team through the following channels:

    LatentSense provides dedicated support throughout the retrofit process, ensuring seamless integration, minimal disruption, and measurable improvements in AI reliability and auditability.

    Support includes:

    • Engagement Onboarding & Scoping: Alignment on existing AI architecture, integration points, and retrofit objectives
    • Implementation Support: Guidance during deployment of middleware for evidence capture, causal tracing, and contradiction detection across AI pipelines
    • Validation & Testing Support: Assistance in verifying traceability, evaluating hallucination reduction, and ensuring correct propagation of provenance metadata
    • Findings & Optimization Sessions: Review of system behavior post-retrofit, with recommendations to further improve reliability, transparency, and performance
    • Post-Deployment Support: Follow-up assistance for tuning, scaling, and maintaining evidence-driven AI capabilities

    Our team typically responds to inquiries within 1 business day, with priority support for active engagements. Expedited support is available for time-sensitive deployments. Ongoing advisory and enhancement services are available upon request.

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