If you've been asked to evaluate a chatbot solution and your first questions are about data isolation, answer accuracy, and what happens when it goes off-script — you're asking the right questions. DFWBots was built for exactly those constraints.
// system.design — how it actually works
Most chatbot platforms route customer queries through a language model that predicts an answer. DFWBots does not. Every response is pre-coded, client-approved, and deterministic — the system pattern-matches to an approved answer, or it escalates to a human. There is no middle ground where the system improvises.
Incoming questions are matched against a library of pre-coded Q&A pairs. If there's a confident match, the approved answer is returned verbatim. If there's no match, the query routes to Room10 — it never invents.
DETERMINISTICEach deployment runs on its own isolated DigitalOcean droplet running Ubuntu 22.04 and Python 3.10. No shared infrastructure, no cross-client data access, no multi-tenant database risk.
ISOLATEDEvery Q&A pair is reviewed and approved by the client before deployment. There is no training phase, no model drift, no content that wasn't explicitly authorized. What you approve is exactly what customers see.
AUDITABLEWhen a user's question doesn't match an approved answer, they're routed to the Room10 private portal — a structured intake form that captures the request cleanly for human follow-up. No dead ends, no hallucinated responses.
STRUCTUREDBilingual responses are pre-coded in both languages — not dynamically translated by an AI at runtime. Additional languages available at $10/month, same static methodology.
STATICOne JavaScript embed code. Works on any web stack — WordPress, Squarespace, custom HTML, anything. No API credentials stored on client infrastructure. No agent installed on your servers.
LIGHTWEIGHT// risk.assessment — platform comparison
LLM-based chatbots can produce wrong answers, hallucinate policies, invent contact info, and apologize for things that didn't happen. For a support context, that's a liability. This is how DFWBots compares:
// tech.stack
Straightforward stack. No proprietary lock-in. Runs on infrastructure you can verify, understand, and audit.
Ubuntu 22.04, Python 3.10. One droplet per client — isolated, not shared.
Pattern-matching logic against a pre-coded Q&A library. No model inference at runtime.
Structured escalation intake. Custom inquiries captured privately, not exposed to third-party chat services.
Single script tag. No agent on client infrastructure. Compatible with any web stack.
Managed process uptime. Restarts automatically on failure. 24/7 availability by design.
// scope.definition
Scope clarity prevents surprises. This is a productized FAQ concierge, not an enterprise AI platform.
A scripted FAQ responder for approved Q&A pairs
A general-purpose LLM or AI assistant
An isolated, single-tenant deployment
A multi-tenant SaaS with shared infrastructure
A structured escalation path for unmatched queries
A CRM, ticketing system, or live chat platform
Fully auditable — every response is pre-approved by you
A system that generates or infers novel responses
// evaluation
The demo at dfwbots.com/demo-help-desk.html is running on production infrastructure. Test the FAQ engine. Try to make it go off-script. See how Room10 handles an unmatched query. Review what deterministic, pre-coded responses look like in a real support context.