Build an AI Chatbot That Actually Works
— Not a Demo
Most AI chatbots fail in production. Wrong LLM, no context management, no fallbacks, no integrations. QuickHire engineers build chatbots that handle real conversations — with a PM ensuring it ships on time.
4hr/$100 · 8hr/$200 · Sprint Pack 10 days/$1,700 · No lock-in
Why Most AI Chatbots Fail in Production
The demo works. The production chatbot doesn't. Here's why.
Wrong LLM for the Use Case
GPT-3.5 for a document chatbot that needs 100K context. Claude Haiku for a reasoning-heavy support bot. Model selection is the most consequential decision — and most teams guess.
No Context Management
Chatbots that forget the conversation after 5 messages. No memory, no session state, no ability to refer to earlier turns. This is a solvable engineering problem most teams skip.
No Fallback Logic
When the chatbot doesn't know the answer, it makes one up. No escalation path, no "I don't know," no handoff to a human. This destroys trust in the product.
AI Chatbots We Build
Across every use case — customer support, internal knowledge, sales, and domain-specific assistants.
RAG Document Chatbots
Chatbots that answer questions from your documentation, PDFs, knowledge base, and product data — with cited sources and high accuracy.
Customer Support Bots
Handle 60–80% of support tickets automatically. Integrate with Zendesk, Freshdesk, Intercom. Escalate to humans with full context.
Internal Knowledge Bots
Give your team instant access to internal SOPs, HR policies, engineering runbooks, and company knowledge — deployed in Slack or Teams.
Sales Qualification Bots
Qualify inbound leads with smart questions, integrate with HubSpot or Salesforce CRM, and book meetings via Calendly — automatically.
How QuickHire Builds Chatbots
Use Case Scoping
PM + engineer define chatbot goals, data sources, integration points, and success metrics in session 1. You leave with a clear build plan.
RAG Architecture Design
We select the embedding model, chunking strategy, vector store, and retrieval method tuned to your content type and query patterns.
LLM Integration & Prompt Engineering
System prompt design, few-shot examples, output parsing, and confidence-gating — the difference between a chatbot that works and one that hallucinates.
Integration & Deployment
We connect the chatbot to your channels (web widget, Slack, Teams, WhatsApp) and CRM, then deploy to your infrastructure with monitoring and cost alerts.
Tech Stack We Use
Pricing
Simple, Transparent Pricing
Every session includes a vetted expert + dedicated PM. Cancel anytime.
India · INR
GST Invoice · GST excluded
Starter
Best for first timers & quick tasks
/ session
GST excluded
- 1 vetted expert
- Dedicated PM included
- Cancel after session
- Tax-compliant invoice
Full Day
Most chosen for serious delivery
/ session
GST excluded
- 1 vetted expert
- Dedicated PM included
- Daily progress report
- Priority assignment
- Tax-compliant invoice
Available in 14 countries · Other currencies available at checkout
FAQ
Frequently Asked Questions
A basic FAQ chatbot with RAG on your documentation takes 1–2 sessions (4–8 hours). A full customer support chatbot with CRM integration, escalation logic, and analytics takes 2–4 weeks. A complex multi-channel bot with custom training data and A/B testing takes 6–8 weeks. Your PM scopes this precisely in session 1.
We recommend models based on your use case. Claude 3.5 Sonnet (Anthropic) for long-context document chatbots with high accuracy. GPT-4o for general-purpose customer support with function calling. Llama 3.1 or Mistral for cost-sensitive, self-hosted deployments. We benchmark options and recommend the right fit for your budget and latency requirements.
You own 100% of the code and infrastructure. We deploy to your AWS, GCP, or Azure account. The chatbot runs in your environment — we have no ongoing access after handoff unless you book maintenance sessions. No vendor lock-in to QuickHire.
Yes. We build integrations with Zendesk, Intercom, Freshdesk, HubSpot, Salesforce, and custom backends. The chatbot can create tickets, look up customer data, escalate to human agents, and sync conversation history — all in real time via webhooks and APIs.
RAG (Retrieval-Augmented Generation) is our primary defense — the chatbot answers only from your documents and data, not from the LLM's training data. We also add confidence thresholds, graceful fallbacks ("I don't know, here's a human agent"), and output filtering. Hallucination rates below 2% are achievable with proper RAG architecture.
Yes. Maintenance sessions are available on the same session-based model — 4hr/$100. We update the knowledge base, retune retrieval parameters, add new intents, and fix edge cases. Many clients book a monthly maintenance session to keep the chatbot current.
Build a Chatbot That Works in Production
AI engineer + PM assigned in 10 minutes. Start with a scoping session and leave with a real build plan.
Build My AI Chatbot →4hr/$100 · Sprint Pack 10 days/$1,700 · Cancel anytime
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