RAG CHATBOT DEVELOPMENT · CITED ANSWERS · SECURE DEPLOYMENT

RAG Chatbot Development — Answers Grounded in Your Business

Custom chatbots that retrieve from your documents, PDFs, policies, and knowledge bases — with citations, confidence checks, and escalation rules when source material is unclear.

Turn One Workflow Into an AI Assistant See Use Cases

Document and knowledge base indexing
Cited, grounded responses
Slack, Teams, and web chat
Human escalation rules
Multi-language support

RAG — Retrieval-Augmented Generation

Connect your AI agent to the sources your team already trusts — so every response is tied to approved company information, not generic model guesses.

Retrieval-Augmented Generation (RAG) connects your chatbot to handbooks, contracts, product docs, CRM notes, and internal wikis. Answers can include citations, confidence checks, and escalation rules when the source material is unclear.

That matters for regulated workflows, client-facing support, and internal operations where accuracy is non-negotiable. RAG is the foundation — workflow automation, tools, and approvals build on top of grounded answers.

When those answers should also carry your brand on your own domain, see custom white-label AI chatbots.

Typical knowledge sources:

Policies and SOPs — HR, compliance, operations
Product and service documentation
CRM and ticket history for context
Forms, templates, and approval packets
Internal wikis and knowledge bases
Website and marketing content

RAG Chatbot Capabilities

Production-ready retrieval systems — not generic Q&A wrappers.

Cited answers

Every response links back to source documents so users and auditors can verify accuracy.

Confidence & escalation

When retrieval confidence is low, the chatbot escalates to a human with full context instead of guessing.

Workflow-ready

RAG answers can trigger follow-up actions — create tickets, draft emails, or route approvals.

RAG Chatbot Use Cases

Where grounded knowledge makes the biggest difference.

Customer support

Answer product questions from documentation with citations. Reduce ticket volume and improve first-contact resolution.

Employee onboarding

New hires ask HR and IT questions. The chatbot answers from policies and playbooks with source links.

Sales enablement

Reps search pricing sheets, case studies, and competitive intel with instant, cited answers.

Technical documentation

Developers and support engineers find answers across API docs, runbooks, and release notes.

Legal and compliance

Staff query policies and regulations with audit-friendly citations and access controls.

Internal knowledge search

Replace scattered wikis with a single conversational interface across all company knowledge.

How It Works

One agreed production workflow, typically delivered in about 30 days once scope and access are confirmed. Discovery and the written scope come before that delivery period.

1. Discovery Call

We review your workflows, tools, data sources, and success criteria.

2. Solution Design

We define the agent, integrations, approval gates, and launch plan.

3. Build & Integrate

We connect your documents, APIs, and systems into a working prototype.

4. Test & Refine

Real users test the workflow. We tune prompts, rules, and reliability.

5. Launch & Expand

Deploy to production with audit logs, training, and a roadmap for the next automation.

One Agreed Workflow, About 30 Days After Scope

Tyon focuses on one end-to-end automation first — tested with real users, improved with feedback, and expanded step by step.

Scope a Working AI Agent

Why Teams Choose Tyon

Custom AI agents that answer, act, and automate — not generic chatbot wrappers.

Fast implementation

One agreed production workflow, typically delivered in about 30 days once scope and access are confirmed. Discovery and the written scope come before that delivery period.

Real integrations

Connect to CRM, email, documents, APIs, Slack, Teams, and browser-based systems.

Security built in

RBAC, human approval gates, action logs, and flexible deployment options.

Measurable outcomes

Agents that answer, act, and automate — with logged, reviewable actions.

Ready to Build a RAG Chatbot?

We will review your documents, define the right retrieval architecture, and plan your first production launch.

Plan Your First Workflow Try the Demo

Frequently Asked Questions

What is a RAG chatbot?

A RAG (Retrieval-Augmented Generation) chatbot retrieves relevant content from your documents before generating an answer. This grounds responses in your approved information instead of generic model knowledge.

What document formats do you support?

PDFs, Word documents, web pages, wikis, CRM notes, ticket history, spreadsheets, and most text-based knowledge sources.

How do citations work?

The chatbot links answers to specific source passages so users can verify accuracy. This is especially important for compliance and client-facing support.

Can the chatbot use our internal documents securely?

Yes. Documents stay in your environment. We support cloud, private cloud, and on-premise deployment with RBAC and audit logging.

Can RAG chatbots take actions?

Yes. Beyond answering questions, RAG-powered agents can create tickets, draft emails, update CRM records, and trigger approval workflows.

How long does RAG chatbot development take?

One agreed production workflow is typically delivered in about 30 days once scope and access are confirmed. Discovery and the written scope come before that delivery period. More complex work can take longer, and that is settled in the written scope.