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Enterprise RAG AI Knowledge Assistant for B2B Support Operations

Deploying a zero-hallucination RAG vector database engine that resolved 82% of incoming support tickets automatically for an enterprise SaaS provider.

5 min technical read Production-Grade Architecture Sub-100ms Latency Standards
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Architecture & Engineering Specifications

Client Challenge

A B2B software vendor was overwhelmed by 4,000+ monthly customer support tickets, leading to 14-hour average response times and agent burnout.

Software Engineering Solution

- Built a multi-stage RAG knowledge pipeline ingesting 15,000+ internal docs, API specifications, and historic ticket resolutions into Qdrant. - Implemented Cohere Rerank and semantic fallback logic to route complex issues directly to human engineering tier.

Measurable ROI & Results

  • Automated Ticket Resolution: 82% of inquiries resolved without human intervention.
  • Response Time: Instant response time under 2.5 seconds.
  • Operational Savings: Saved over $140,000 annually in support overhead.

    • Measurable Outcomes & Deliverables

    Performance Benchmark

    Engineered for sub-100ms API response latencies and 99.99% system availability SLAs.

    Code Ownership

    Complete clean source code transfer with automated CI/CD pipelines and IaC setup.

    Security & Compliance

    SOC2 Type II compliance audit ready, zero-trust OAuth2/JWT auth, and data isolation.

    Ongoing SLA Retainer

    Dedicated principal software engineers maintaining production performance 24/7.

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