What data sources can feed a RAG chatbot?

Common sources include PDFs and Word docs, website crawls, Notion and Confluence, Google Drive, product catalogs from Shopify or databases, Zendesk and Intercom help centers, and custom APIs. We build ETL pipelines with scheduled re-embedding, PII redaction rules, and folder-level access control for enterprise teams.

How long does RAG chatbot development take?

MVP RAG chatbots (web widget plus one knowledge source) often ship in 4 to 6 weeks: discovery and architecture in week 1, ingestion and vector setup in weeks 2-3, chat UI and guardrails in weeks 4-5, QA and channel deploy in week 6. Multi-channel agentic bots with tool use may run 8 to 12 weeks.

What is an agentic chatbot with tool use?

An agentic chatbot plans multi-step actions: look up CRM records, create tickets, book meetings, or run SQL via function calling and MCP-style tool connectors. RAG supplies knowledge; tools supply actions. We add human approval for sensitive operations and rate limits per user. Agentic chatbot development pairs retrieval with orchestration, not generic Q&A only.

Do you support multimodal chatbots (documents and images)?

Yes. Users can upload PDFs or images in chat; vision models extract text and objects for retrieval or direct answers. Multimodal RAG embeds image captions and OCR text into the same vector index. Useful for warranty photos, spec sheets, and field service documentation.

How do you handle privacy and enterprise data security?

We map data flows before build: what is embedded, what is sent to LLM APIs, and retention policies. Options include regional endpoints, private VPC, no training on your data per provider terms, RBAC on document folders, and encrypted storage. GDPR and PIPEDA-aligned documentation at handoff. Your data stays yours.

What happens after our chatbot launches?

You receive 30 days of post-launch support, monitoring on retrieval quality and token cost, and admin training. Optional retainers cover embedding refreshes, new model upgrades, prompt tuning, and new channels. We cross-link AI automation and customer support services when you need workflow beyond chat.

AI Chatbot Development (RAG) ★ 4.9 · 47 reviews

AI Chatbot Development.
Trained on Your Knowledge.

Q: Can you deploy chatbots on WhatsApp, Slack, and web?

Yes. Web embeds use streaming widgets with your branding. WhatsApp Business API routes conversations through the same RAG backend with session memory. Slack bots answer in channels or DMs with thread context. One knowledge base can power all channels with shared guardrails and analytics on deflection and resolution.

We build AI chatbots that answer from your documents, help center, and product data. Not generic responses.

Get Free Chatbot Scope Call ? View Plans

80+ chatbots deployed

87% queries auto-answered

65% volume deflected

What is our refund policy for annual plans?

Query ? Embeddings ? Vector DB ? Cited Docs ? LLM

Chunks

Match

Cites

Models we work with

GPT-4o Claude Gemini Llama (open-source) Pinecone pgvector

Works with GPT-4o, Claude, Gemini, and open-source (Llama) Your data stays yours Enterprise privacy and SOC2-ready practices

87%

Queries Auto-Answered

2-Min

Setup to Answer

65%

Support Deflected

80+

Bots Deployed

RAG Chatbot Signals

Four Signals That Make RAG Chatbots Accurate and Enterprise-Ready.

Grounded Retrieval

Top-k chunk search with hybrid keyword + vector match tuned on your real user questions.

Citation UX

Inline source links: doc name, section, and confidence score in every streamed answer.

Hallucination Guardrails

Refuse when match score is low. No answer is better than a wrong policy reply.

Vector DB Ops

Scheduled re-embed jobs, dedupe, and versioned indexes after Notion or help center updates.

Build Process

From Knowledge Audit to Live RAG Bot in 4 to 6 Weeks.

Week 2

Week 6

W10

Week 10

Ongoing

Source Audit & RAG Design

Document inventory, chunk strategy, vector DB choice, and channel plan (web, WhatsApp, Slack) before build.

Ingest & Embed

ETL pipelines, embedding jobs, retrieval eval on sample queries, and citation prompt templates.

Chat UI & Guardrails

Streaming widget, hallucination rules, admin console, and GPT-4o / Claude proxy with rate limits.

Deploy & Optimize

Channel go-live, deflection analytics, monthly reindex, and cost-per-query reviews.

RAG Chatbot Plans

Choose Your RAG Chatbot Development Level.

Quote-based plans. No long-term contracts. Scoped by data sources, vector DB, channels, and agentic tool scope.

STARTER

Starter

MVP RAG ï¿½ One knowledge source

?RAG source & architecture audit
?1 knowledge source RAG MVP
?GPT / Claude integration
?Vector DB + web widget
?Streaming chat UX
?30-day reporting

Get Quote ?

What Professional RAG Chatbot Development Delivers.

Queries Auto-Answered

0-Min

Setup to Answer

Support Deflected

Bots Deployed

RAG, web, WhatsApp, Slack

Our support team pasted answers from Notion manually. Groke built a RAG chatbot on our help center and policy PDFs.

Head of Support, B2B SaaS (Canada)

FAQ

Common questions.

What is a RAG chatbot and how is it different from ChatGPT?

A RAG (Retrieval-Augmented Generation) chatbot answers from your documents, website, Notion, or database instead of model training alone. At query time it embeds the question, retrieves relevant chunks from a vector database, and sends grounded context to GPT-4o or Claude with citations.

Which vector databases do you use for RAG chatbots?

We work with Pinecone, Weaviate, Qdrant, pgvector on PostgreSQL, Supabase Vector, and Azure AI Search depending on scale, residency, and budget. Choice depends on chunk volume, hybrid search needs, and whether you need self-hosted vs managed.

Do you build GPT-4o and Claude enterprise chatbots?

Yes. We integrate OpenAI GPT-4o and GPT-4o mini, Anthropic Claude Sonnet and Haiku, Google Gemini, and open-source LLMs via API or private endpoints.

How do you prevent hallucinations in a knowledge base chatbot?

Hallucination control combines retrieval quality, prompt rules, and answer validation. We tune chunk size and top-k retrieval, require citations in the UI, set confidence thresholds to refuse low-match queries, and add human handoff when grounding is weak.

How much does AI chatbot development cost?

AI chatbot development is quote-based across Starter, Growth, and Scale plans depending on RAG data sources, vector infrastructure, channels (web, WhatsApp, Slack), and agentic tool use scope. No fixed public pricing. Written quote within 48 hours of your discovery call. Month-to-month available.

Can you deploy chatbots on WhatsApp, Slack, and web?

Yes. Web embeds use streaming widgets with your branding.

Ready to Convert More Visitors?

Get a Free Chatbot Scope Call. Map sources, channels, and timeline.

Free 30-min RAG chatbot scope call. We review your knowledge sources, vector DB options, channels (web, WhatsApp, Slack), hallucination guardrails, and timeline - and show you what a production RAG pipeline looks like before any commitment.

Free AI scope call - 30 minutes, no obligation

Full scope and pricing delivered within 48 hours

Month-to-month - no long-term contracts required

Development starts within 5 business days of sign-off

Get Free Chatbot Scope Call ? WhatsApp Us

Average response: under 4 hours ï¿½ Serving US, UK, CA, AU

? No pitch decks ? Free 30-min call ? Quote within 48hrs

Further reading: Anthropic Research Â· OpenAI Research

AI Chatbot Development.Trained on Your Knowledge.