DataLife
LLM Bots

AI Assistants Built on Your Data

I build RAG-powered chatbots trained on your documents, products, and processes — so your bot gives accurate, on-brand answers, not generic AI responses.

What you get

Answers grounded in your actual documents
Qualifies leads 24/7 without a human
Guardrails so it stays on-brand, always
Full conversation logs you own and control

What's included

Everything needed for a production-grade AI assistant

From knowledge base ingestion to live deployment — I handle the full stack so you get a bot that actually works for your business.

RAG Pipelines

Retrieval-Augmented Generation connects the LLM to your documents, databases, and knowledge base so answers are grounded in your actual data — not hallucinations.

Custom Knowledge Base

I ingest your PDFs, docs, Notion pages, or databases into a ChromaDB or Pinecone vector store — keeping the bot accurate and up-to-date.

Multi-Turn Conversations

Context-aware dialogue that remembers what was said earlier in the conversation — enabling natural, helpful exchanges rather than one-shot Q&A.

Lead Qualification

A conversational funnel that identifies project type, budget, and urgency — then saves qualified leads to Supabase and fires a Slack alert.

CRM Integration

Bot actions wired to HubSpot, Salesforce, or any CRM via webhook or API — so every qualified conversation automatically updates your records.

Guardrails & Safety

System-prompt hardening, topic restrictions, and prompt-injection defence so the bot stays on-brand and never leaks sensitive information.

How it works

From use-case to live AI assistant

1

Use-Case Scoping

We define exactly what the bot should do, what it should never do, and what a successful conversation looks like. This shapes everything that follows.

2

Knowledge Base Prep

Your source documents are cleaned, chunked, embedded, and loaded into a vector store. I audit coverage so the bot can actually answer the questions it will receive.

3

Bot Build & Testing

System prompt engineering, RAG pipeline wiring, and iterative red-teaming to catch hallucinations, off-topic responses, and edge cases before launch.

4

Deployment & Monitoring

The bot is embedded in your site or app, wired to your CRM, and connected to a Supabase log so you can review every conversation and improve over time.

What you get

RAG pipeline + vector store setup
System prompt + guardrails documentation
Embedded chat widget (Next.js)
Lead capture → Supabase + Slack
Conversation log dashboard
30-day monitoring & support

Tech stack

Tools I use for LLM Bots

Best-in-class LLM infrastructure — from embedding and retrieval to streaming chat and production deployment.

LangChainOpenAI APIAnthropic APIChromaDBPineconeNext.jsSupabaseVercel AI SDK

Pricing

Transparent, fixed-price plans

Priced per project — not per API call. You own the code and the data.

Starter

$600 – $1,200

A focused AI assistant for one specific use case.

  • Single-purpose RAG bot
  • Up to 50 source documents
  • Embedded chat widget
  • Basic lead capture
  • 1 revision round
Get Started
Most Popular

Growth

$2,000 – $4,000

A full-featured AI assistant with CRM and lead qualification.

  • Multi-topic RAG pipeline
  • Unlimited source documents
  • Lead qualification + CRM integration
  • Conversation log dashboard
  • Guardrails + red-team testing
Get Started

Enterprise

Custom

Organisation-wide AI assistant with ongoing content updates.

  • Multiple bot personas / departments
  • Custom fine-tuning option
  • SSO + access controls
  • Dedicated Slack channel
  • Monthly knowledge-base refresh
Let's Talk

Ready to get started?

Tell me what you want the bot to do and I'll design a solution around your existing content and stack.

Get a Free QuoteBook a Discovery Call