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Converge.AI
Capability

Advanced RAG Systems

Secure, hallucination-resistant retrieval over proprietary corporate data.

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Overview

We engineer Retrieval-Augmented Generation systems that hold up under real enterprise load — multi-tenant, role-aware, citation-rich, and aggressively evaluated. No demo-ware. The retrieval pipeline is the product.

Outcomes we engineer for

  • Citation-grounded answers with traceable source documents
  • Role-aware retrieval enforcing existing entitlements
  • Hybrid retrieval (lexical + dense + reranking) tuned per domain
  • Continuous evaluation with regression gates on every change

How we build it

  1. 01

    1. Source ingestion & chunking

    Document-aware ingestion with structure preservation (tables, hierarchies, footnotes). Chunking strategy is tuned per content type, not one-size-fits-all.

  2. 02

    2. Hybrid retrieval pipeline

    Lexical and dense retrieval combined with cross-encoder reranking. Domain-specific routing for technical, commercial, and policy content.

  3. 03

    3. Entitlement-aware orchestration

    Retrieval enforces the same row-level and document-level permissions your existing identity provider enforces. RAG inherits, it does not bypass.

  4. 04

    4. Eval harness & guardrails

    Golden-set evaluation, regression tests, hallucination detection, and PII/PHI guardrails wired into CI. Every prompt change is gated.