AI Generator
Generates text using an LLM model with a custom system prompt.
if there are multiple links that are the same please include one of them and let the user know that the process is completed.
This AI-driven workflow enriches lead data in Google Sheets by automatically retrieving missing LinkedIn profiles, job titles, and industries from the web using search and AI agents. It updates the sheet with enriched information, streamlining the lead enrichment process for sales and marketing teams.
Flows
Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.
Generates text using an LLM model with a custom system prompt.
if there are multiple links that are the same please include one of them and let the user know that the process is completed.
Agent system prompt for checking Google Sheet columns and updating them if needed.
use the google sheet retriever and check if the document has a "LinkedIn", "Job Title", "Industry" column if not add them to the document.
AI Agent tasked with finding and outputting a person's LinkedIn page link.
Backstory: Data enricher
Goal: You should Find the persons LinkedIn and output the LinkedIn page link.
Role: data enricher
AI Agent tasked with finding and outputting a person's job title based on their LinkedIn.
Backstory: Data enricher
Goal: You should Find the persons job title based on their LinkedIn and output that.
Role: data enricher
AI Agent tasked with finding and outputting a person's job industry using available tools.
Backstory: Data enricher
Goal: You should Find the persons job Industry based on tools at your disposal.
Role: data enricher
Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.
Integrate your Google Sheets with FlowHunt workflows using the Google Sheets Retriever component. Effortlessly fetch and utilize spreadsheet data as part of your automation, enabling dynamic data-driven processes and advanced workflow logic.
Explore the Tool Calling Agent in FlowHunt—an advanced workflow component that enables AI agents to intelligently select and use external tools to answer complex queries. Perfect for building smart AI solutions that require dynamic tool usage, iterative reasoning, and integration with multiple resources.
Easily add a new column to any Google Sheets document within your automated workflow. This component lets you specify column names and values, seamlessly updating your spreadsheet with new data—ideal for dynamic data management and integration tasks.
The Chat Input component in FlowHunt initiates user interactions by capturing messages from the Playground. It serves as the starting point for flows, enabling the workflow to process both text and file-based inputs.
Discover the Chat Output component in FlowHunt—finalize chatbot responses with flexible, multi-part outputs. Essential for seamless flow completion and creating advanced, interactive AI chatbots.
Unlock custom workflows with the Custom Trigger component in FlowHunt. This component allows users to define specific trigger points within their flow, enabling tailored actions based on custom events or inputs. Essential for building interactive and flexible automation workflows.
The AI Agent component in FlowHunt empowers your workflows with autonomous decision-making and tool-using capabilities. It leverages large language models and connects to various tools to solve tasks, follow goals, and provide intelligent responses. Ideal for building advanced automations and interactive AI solutions.
FlowHunt's GoogleSearch component enhances chatbot accuracy using Retrieval-Augmented Generation (RAG) to access up-to-date knowledge from Google. Control results with options like language, country, and query prefixes for precise and relevant outputs.
Unlock web content in your workflows with the URL Retriever component. Effortlessly extract and process the text and metadata from any list of URLs—including web articles, documents, and more. Supports advanced options like OCR for images, selective metadata extraction, and customizable caching, making it ideal for building knowledge-rich AI flows and automations.
The Create Data component enables you to dynamically generate structured data records with a customizable number of fields. Ideal for workflows that require the creation of new data objects on the fly, it supports flexible field configuration and seamless integration with other automation steps.
The Iterator component in FlowHunt automates repetitive tasks by executing a subflow or external flow for each item in a list. Ideal for batch processing, data enrichment, or applying the same logic to multiple inputs, it supports customizable concurrency and advanced options for flexible workflow automation.
Learn how FlowHunt's Prompt component lets you define your AI bot’s role and behavior, ensuring relevant, personalized responses. Customize prompts and templates for effective, context-aware chatbot flows.
Flow description
This workflow automates the enrichment of outreach data stored in a Google Sheet. It is designed to automatically find missing information such as LinkedIn profiles, job titles, and industries for contacts in your spreadsheet by leveraging AI agents, Google Search, and dynamic data manipulation. The process not only fetches this data but also updates your Google Sheet accordingly. This solution is highly valuable for scaling and automating data enrichment tasks, eliminating manual research, and ensuring your outreach lists are always up-to-date and comprehensive.
User Interaction & Initialization
Google Sheet Retrieval & Preparation
Data Enrichment Process (Iterative for Each Row)
Automation & AI Integration
Output & User Feedback
Step | Component Name | Purpose |
---|---|---|
1 | Button Widget, Chat Input/Output | User interaction and process start |
2 | Google Sheets Retriever | Fetches contact data from the provided Google Sheet |
3 | Tool Calling Agent | Checks/creates required columns (“LinkedIn,” etc.) |
4 | Iterators | Processes each row/contact individually |
5 | AI Agents + Google Search + URL Retriever | Finds LinkedIn URLs, job titles, and industry information |
6 | Create Data | Structures the new information for each contact |
7 | Google Sheets Updater | Writes enriched data back into the appropriate sheet columns |
8 | Chat Outputs, Notes | Provides feedback, instructions, and status updates |
Finding LinkedIn Profiles:
For each contact, an AI agent uses Google Search (and optionally parses web pages) to find the most likely LinkedIn URL. If multiple links are found, the agent selects the best one and notifies the user.
Extracting Job Titles:
Once a LinkedIn profile is found, the AI agent scrapes or interprets the job title from the profile page content.
Determining Industry:
The agent further determines the contact’s industry, either from LinkedIn or other publicly available sources.
Updating Google Sheet:
For each successful enrichment, the workflow writes the new data (LinkedIn, Job Title, Industry) directly into the corresponding row and column in the Google Sheet.
Below is a simplified view of the automation logic:
flowchart TD
Start([User clicks Start / uploads Sheet])
GetSheet([Retrieve Google Sheet data])
CheckColumns([Ensure LinkedIn/Job Title/Industry columns exist])
ForEachRow([For each row in Sheet])
SearchLinkedIn([AI agent finds LinkedIn URL])
ExtractJobTitle([AI agent extracts Job Title])
DetermineIndustry([AI agent determines Industry])
UpdateSheet([Update Sheet with new data])
NotifyUser([Provide feedback to user])
Start --> GetSheet --> CheckColumns --> ForEachRow
ForEachRow --> SearchLinkedIn --> ExtractJobTitle --> DetermineIndustry --> UpdateSheet
UpdateSheet --> NotifyUser
Scalability:
It enables teams to enrich thousands of contacts efficiently, making it feasible to maintain large, high-quality outreach databases.
Automation:
All research and data entry steps are automated, freeing human resources for higher-value tasks.
Consistency & Data Quality:
Ensures every contact has complete information, improving personalization and targeting in outreach efforts.
Flexibility:
The modular construction (triggers, iterators, AI agents, data creators) makes it easy to adapt or extend for other data enrichment needs.
In summary:
This workflow is a robust, scalable automation for enriching outreach data in Google Sheets with up-to-date LinkedIn profiles, job titles, and industry information. It seamlessly combines AI agents, web search, and spreadsheet automation to save time, improve outreach effectiveness, and maintain high data quality across your contact lists.
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