LLM-Integrated Conversational AI Platform with RAG Architecture
• Single-layer chatbot logic failed on complex intent, context carryover, and transactional safety. • Built a confidence-gated AI stack with deterministic routing, NLP layers, RAG grounding, and bounded LLM use. • Improved first-response intent accuracy by 45-60% and reduced hard-failure paths by 80%+.
Executive Snapshot
RoleSystems Architect & Lead Fullstack Engineer
DurationEnterprise Production System
TeamSystems Architect + Cross-functional Bot & Ops Support
Hosting:AWS LambdaDigitalOcean Droplet
CI/CD & Infra:GitHub ActionsDocker
Backend:FastAPI (RAG Orchestration)Node.js (Webhook Router)PHP (Commerce & Payment APIs)
Frontend:React.js
Databases:Redis (Session & Cache)MongoDB (Conversation Logs)MySQL (Commerce Data)Qdrant (Vector DB)
AI Stack:Lavoisier Deterministic LookupWit.aiLlamaOpenAI GPTLangChainPyTorch (Sentiment)RAG Pipeline
Analytics & Security:Looker StudioCloudflare Turnstile
Designing a confidence-gated, multi-model AI architecture that balances deterministic logic, NLP, and LLM inference while enforcing strict data governance and transactional integrity.
Features at a Glance
Problems It Solved
Business Impacts
Engineering Challenges
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Note: This is a conceptual representation of an enterprise revenue governance platform. All branding, data, and identifiers have been modified for confidentiality purposes.
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