AI-Powered Lead Generation with Apollo, GPT-4, and Telegram to Database
VerifiedAI processes Telegram inputs to extract and store structured data in Postgres or Supabase.
What this workflow does
The workflow receives text or voice input via Telegram, applies AI analysis with OpenAI models and memory to parse parameters, then writes verified records directly into Postgres and Supabase.
It serves sales, marketing, and recruitment teams that need automated data capture from messaging channels into reliable database storage.
Who is this for?
Sales development reps, growth marketers, and small teams running outbound campaigns who want automated lead intake from chat.
What problem it solves
Turning ad-hoc voice or text requests into clean, verified prospects stored in a database is manual and slow.
Live workflow preview
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What it automates
Voice brief to database
SDR sends a Telegram voice note with target criteria and new rows appear in Supabase within minutes.
Weekly list refresh
Marketer texts job titles and locations; workflow parses, checks duplicates, and appends only fresh verified contacts to Postgres.
Campaign kickoff
Team lead messages natural-language requirements; AI extracts parameters and populates a new outreach segment automatically.
How the workflow works
The 9 nodes in this automation, in order.
- 1Postgrespostgres
- 2Telegramtelegram
- 3Supabasesupabase
- 4Codecode
- 5AI Agent@n8n/n8n-nodes-langchain.agent
- 6OpenAI Chat Model@n8n/n8n-nodes-langchain.lmChatOpenAi
- 7Simple Memory@n8n/n8n-nodes-langchain.memoryBufferWindow
- 8Structured Output Parser@n8n/n8n-nodes-langchain.outputParserStructured
- 9OpenAI@n8n/n8n-nodes-langchain.openAi
Apps & integrations used
How to set up AI-Powered Lead Generation with Apollo, GPT-4, and Telegram to Database
- 1Add Telegram trigger node and connect your bot token
- 2Attach AI Agent node using OpenAI Chat Model and Simple Memory
- 3Insert Structured Output Parser after the agent to extract location, industry, and titles
- 4Add Supabase or Postgres node to check for duplicates before insert
- 5Connect final insert node to store new contacts with profile fields
- 6Activate workflow and test with a sample Telegram message
How to customize this workflow
- →Swap OpenAI Chat Model for a different provider supported by the AI Agent
- →Change Telegram trigger to another chat app node
- →Add an extra Supabase query step before insert for custom deduplication rules
- →Extend the Structured Output Parser schema with additional fields like company size
AI-Powered Lead Generation with Apollo, GPT-4, and Telegram to Database: pros & cons
Pros
- +Supports both voice and text input via Telegram
- +Uses memory and structured parsing for consistent extraction
- +Direct writes to Postgres or Supabase with duplicate guard
- +Intermediate complexity with reusable AI components
Cons
- –No built-in Apollo node; external scraping logic must be added separately
- –Duplicate check quality depends on your existing schema
- –Requires valid OpenAI and database credentials
Frequently asked questions
It receives Telegram voice or text, uses an AI agent to extract lead criteria, and stores verified contacts in Postgres or Supabase.
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