White papers

Fini AI: RAGless Agentic AI for Enterprise Support

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Introduction

Sophie V2 represents a paradigm shift in how enterprises approach customer experience (CX) automation. Traditional LLM architectures, predominantly built on Retrieval-Augmented Generation (RAG), have failed to deliver the reliability, actionability, and compliance required for mission-critical support scenarios. Sophie introduces a supervised execution framework that cleanly separates reasoning from action.

At the core of Sophie lies an LLM Supervisor responsible for planning, state tracking, and decision-making. This is supported by deterministic Skill Modules that interface with external systems and data sources to reliably perform actions. Input/output is filtered through enterprise-grade Guardrails, and every decision, interaction, and data source is logged through a Traceability Layer. With its Feedback Engine, Sophie adapts in production using real interaction data — without the need for constant re-training or prompt tuning.

The result is an AI agent that is not only intelligent but also controllable, trustworthy, and measurable — capable of automating highly variable and policy-sensitive CX workflows with confidence.


📘 In This White Paper

  • The Operational Gaps in RAG-based AI Systems

    • Why common architectures break under enterprise CX demands

    • Failure modes in accuracy, policy enforcement, traceability, and actionability

  • Sophie V2: Architectural Overview

    • How supervised execution ensures deterministic planning and execution

    • Detailed breakdown of the Guardrail Layer, LLM Supervisor, Skill Modules, and Feedback Engine

  • RAG vs RAGless Retrieval

    • Why semantic retrieval fails in structured environments

    • How Sophie ensures structured, explainable, policy-compliant knowledge access

  • Traceability by Design

    • How every plan, decision, and skill invocation is logged and auditable

    • Examples of full execution flows across fintech, SaaS, and e-commerce

  • CXACT Benchmarking Suite

    • Our novel framework for measuring agent accuracy, policy compliance, tool invocation correctness, and trace quality

    • Comparative results validating Sophie's architecture

  • Architecture Evolution from V1 to V2

    • What broke in our early hybrid-RAG deployments

    • Why supervised execution proved significantly more scalable and reliable

  • Technical Roadmap

    • Upcoming features, SDKs, and improvements for developers, CX admins, and analysts

  • Conclusion

    • Why supervised execution is the only viable foundation for enterprise-grade AI support automation in 2025 and beyond

Ask Sophie the hardest questions and hire her for your team today

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  • hackerrank
  • bits
  • bitdefender
  • columntax
  • qogita
  • monarch money
  • monoz
  • atlas
  • found
  • cents
  • vayyar
  • training peaks