Etorial AI Platform

The Problem

Most conversational AI tools in high-stakes industries share a fundamental flaw: they let large language models generate answers from training data. In domains like aviation parts, automotive inventory, or municipal services, a single hallucinated part number, price, or regulation can break trust permanently.

I saw an opportunity to build a system where the AI never guesses. The architecture would guarantee that every response is grounded in verified, real-time data, and that the language model's only job is formatting, never sourcing.

What I Chose and Why

• 1

  Deterministic code searches, not AI retrieval

  Every data lookup is handled by structured code querying Firestore directly. The LLM never touches the database. This is the zero-hallucination guarantee. It limits flexibility but eliminates the failure mode that matters most: giving someone a wrong answer with confidence.

• 2

  Claude as formatter, never as source

  Claude receives verified data and structures it into natural language. It does not retrieve, infer, or fill gaps. If the data is not in Firestore, the system says so. This means the AI sounds human without ever fabricating content.

• 3

  MCP server as the control layer

  The Model Context Protocol server on Cloud Run acts as the bridge between the conversation layer and the data layer. It enforces what the AI can and cannot access, creating a hard boundary between language generation and data retrieval.

• 4

  Industry-specific agents, not a general chatbot

  Each deployment is purpose-built for its vertical. Aviation gets parts lookup and quoting. Automotive gets inventory and lead capture. Municipal gets service routing. Sharing a codebase but not a personality means each agent earns trust in its own domain.

• 5

  4,000+ QA scenarios before any client goes live

  I built a testing framework that runs thousands of adversarial and edge-case prompts against every deployment. No agent launches until it hits 100% effective accuracy across the full scenario set. The testing is the product as much as the AI is.

Turbana Solutions: Aviation Parts

Turbana Solutions is a Tampa-based aviation parts distributor. Their sales team was handling quote requests manually, with response times averaging 24 to 48 hours. Inventory lived in spreadsheets. Buyers moved on before quotes arrived.

I deployed a full Etorial agent to turbanasolutions.com in February 2026. The system handles real-time inventory search, instant quoting, lead capture, and conversation transcripts for the sales team. It went from first conversation to live deployment in under six weeks.

Sales Dashboard: The Turbana team sees every conversation, captured lead, and inventory query in real time. No context is lost between the AI agent and the human sales team.

Lead Capture

Conversation Transcripts

Inventory Search: The agent queries live Firestore data so buyers get real-time availability, pricing, and part details without waiting for a human lookup.

Expanding the Model

The architecture is designed to scale across verticals. The same trust-first data layer, MCP control boundary, and QA framework adapt to any domain where accuracy is non-negotiable.