RAG Chatbot Quickstart
Ship a production-ready RAG chatbot in 2 weeks—on your data, in your cloud.
Overview
A Working System, Not Another Slide Deck
No strategy decks. No throwaway POCs. We build a production-ready RAG chatbot on your data and deploy it to your cloud in exactly 2 weeks. You own the code, the infrastructure, and the system. We build once; you own it.
The Challenge
Why Teams Struggle with RAG
If you have internal knowledge you want searchable—docs, wikis, knowledge bases—or customer-facing support needs, you've likely considered building an AI chatbot. But most teams hit the same walls:
POC Purgatory
Teams build impressive demos that never make it to production. The gap between "it works on my laptop" and "it works in production at scale" is enormous—and that's where most chatbot projects die.
Scope Creep
What starts as a 2-week project becomes a 6-month initiative. Without fixed scope and clear boundaries, RAG projects expand to consume whatever time and budget is available.
No Production Guardrails
Most tutorials and guides skip the hard parts: observability, cost controls, content filtering, error handling, and operational runbooks. These aren't optional in production.
What You Get
Production-Ready from Day One
Fixed Scope, Fixed Timeline
We deliver in exactly 2 weeks. The scope is defined upfront with clear milestones and daily check-ins. No surprises, no scope creep, no moving goalposts. You know exactly what you're getting and when.
Production-Grade Architecture
This isn't a throwaway prototype. We build with observability (logging, metrics, tracing), cost controls (token budgets, rate limiting), content guardrails, and error handling baked in from the start.
Your Infrastructure, Your Code
We deploy to your AWS (or approved cloud). You own the Infrastructure as Code (Terraform/CloudFormation) and the entire application codebase. No vendor lock-in, no managed service dependency.
Operational Runbooks
We deliver comprehensive runbooks for re-ingestion, scaling, troubleshooting, and cost monitoring. Your team can operate and maintain the system independently from day one.
Built to Extend
Clean, well-documented code with clear extension points. Your team can add new data sources, adjust prompts, tune retrieval parameters, and scale the system without starting over.
Knowledge Transfer
We don't just hand over code—we walk your team through the architecture, design decisions, and operational procedures so they're fully equipped to own the system going forward.
How It Works
The 2-Week Process
Week 1: Foundation & Ingestion
- Scope definition and architecture design
- Infrastructure provisioning (IaC)
- Data ingestion pipeline setup
- Vector store configuration and indexing
- Initial retrieval pipeline with evaluation
Week 2: Production & Handoff
- Chat interface and API integration
- Observability, logging, and metrics
- Cost controls and guardrails
- Operational runbooks and documentation
- Knowledge transfer and team walkthrough
Who It's For
Is This Right for You?
The RAG Chatbot Quickstart is designed for teams that need to ship something real—fast.
- You have internal docs, wikis, or knowledge bases you want to make searchable via AI
- You need a customer-facing support chatbot grounded in your actual documentation
- You've been delaying because of bandwidth constraints or uncertainty about the right approach
- You want to demo a working AI feature to stakeholders or customers in weeks, not months
- You need production quality—not a prototype that falls apart under real usage
Ready to ship? Contact us or email info@techsight.dev to get started.