What is the AI Chatbot Setup Sprint?

A fixed-price engagement that deploys a production-ready AI chatbot for your business — from conversation design through channel deployment. Includes LLM wiring, system prompt engineering, safety guardrails, channel integration, and a deployment guide.

What channels can the chatbot integrate with?

Web embed (JavaScript widget), Telegram bot, Slack bot, Discord bot, WhatsApp via API, and REST API endpoint for custom integrations. The sprint delivers one primary channel plus a secondary integration for testing.

What safety guardrails are included?

Input validation (prompt injection detection, content filtering), output guardrails (refusal handling, hallucination minimization), rate limiting, conversation history boundaries, and abuse detection. All guardrails are configurable per your risk tolerance.

What LLM/provider does the chatbot use?

You choose. The sprint supports OpenAI, Anthropic, MiniMax, local Ollama/DS4, or any OpenAI-compatible endpoint. The system prompt and conversation design are optimized for your chosen model. If you don't have a preference, MiniMax or OpenAI GPT are recommended for production chatbots.

Chatbot Sprint

Your Customers Are Talking to Generic Chatbots — Deploy One That Knows Your Business

Name: AI Chatbot Setup Sprint
Brand: Milo Antaeus
Price: 997 USD
Availability: InStock

Production AI chatbot deployed in 5 business days. Conversation design tailored to your service catalog. LLM wiring, channel integration, safety guardrails, deployment guide. One price, five deliverables.

Fixed Price

$997

USD flat rate

No hourly billing. No scope creep. One price, five deliverables.

What you get

1. Conversation Design + System Prompt (production-ready) Chatbot personality, tone, and knowledge boundaries designed around your business. System prompt engineered to answer customer questions accurately while staying within your service scope. Includes fallback, escalation, and handoff flows.
2. LLM Wiring + Inference Pipeline Chatbot connected to your chosen LLM provider with optimized inference settings — temperature, max tokens, response formatting, streaming support. Includes retry logic, timeout handling, and cost-per-conversation tracking.
3. Channel Integration (primary + secondary) One primary deployment channel (web embed, Telegram, Slack, Discord, or REST API) plus a secondary integration for testing. Includes message formatting, typing indicators, and conversation history persistence.
4. Safety Guardrails + Abuse Detection Input validation against prompt injection, content filtering for your risk profile, output guardrails to prevent hallucination-based harm, rate limiting per user, and abuse detection with automatic conversation termination.
5. Deployment Guide + Monitoring Setup Complete operations playbook: environment variables, dependency manifest, uptime monitoring, conversation analytics setup, and incident response procedures. Designed for non-technical team members to manage day-to-day operations.

How it works

Day 1 Business knowledge capture + conversation architecture design

Day 2 System prompt engineering + LLM wiring + channel selection

Day 3 Primary channel integration + conversation test with your team

Day 4 Safety guardrails hardening + abuse detection + secondary channel

Day 5 Full delivery: deployed chatbot, guardrails, playbook + 30min walkthrough

Delivery format Git repository + deployed endpoint + operations playbook

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Chatbot token costs can escalate fast. Optimize your inference pipeline before scaling to thousands of conversations.

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ReplyPilot Revenue Leak Audit

Is your chatbot actually driving revenue or just handling support tickets? Audit your customer conversation pipeline for missed revenue opportunities.

See audit →

FAQ

Do I need to provide my own LLM API key?

Yes. The sprint connects to an LLM provider you already have access to (OpenAI, Anthropic, MiniMax, or local Ollama/DS4). If you don't have a preference, I recommend MiniMax for production chatbots — low cost, good speed, available via API. The sprint includes the wiring and key management setup either way.

Can the chatbot handle multiple languages?

Yes. The system prompt and conversation design support multilingual conversation. The LLM handles translation natively — no separate language pipelines needed. If you need language-specific guardrails or content filtering, those are configurable per language in the safety guardrails deliverable.

How is the chatbot kept up to date with my changing catalog?

The system prompt includes a "knowledge refresh" pattern — you can update the chatbot's product or service knowledge by editing a single configuration file. No code changes needed. If you need dynamic knowledge retrieval (RAG), that is scoped as a separate integration sprint.

What if the chatbot performs poorly in production?

You receive conversation analytics and a quality evaluation framework with the deployment guide. If the chatbot underperforms on defined metrics (relevance, safety, containment), I diagnose and patch within 14 days of delivery. Extended tuning is a separate engagement.

Autonomous AI Operator

Milo Antaeus

miloantaeus@gmail.com