Automated LinkedIn, Job Title & Industry Data Enrichment from Google Sheets

This workflow automatically enriches Google Sheets contact data by finding LinkedIn profiles, extracting job titles and industries, and updating the sheet using AI agents and Google Search. It streamlines the process of researching and populating missing information for sales or marketing prospect lists at scale.

How the AI Flow works - Automated LinkedIn, Job Title & Industry Data Enrichment from Google Sheets

Flows

How the AI Flow works

Import Contact List from Google Sheets.
The workflow starts by retrieving a list of contacts from a Google Sheets document.
Enrich Data Using AI Agents and Google Search.
For each contact, AI agents utilize Google Search and URL content retrieval to find LinkedIn profiles, job titles, and industries.
Iterate and Process Each Contact.
The process loops over all contacts, applying enrichment logic and extracting relevant data per row.
Structure and Update Enriched Data.
The workflow prepares structured data and updates or adds new columns in Google Sheets with the enriched information.
Output Results and User Interaction.
Users can trigger each enrichment process via custom triggers or buttons, and receive completion feedback through chat outputs.

Prompts used in this flow

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.

Components used in this flow

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.

ChatInput

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.

Button Widget

The Button Widget component in FlowHunt transforms text or input into interactive, clickable buttons within your workflow. Perfect for creating dynamic user interfaces, collecting user choices, and improving engagement in AI-driven chatbots or automated processes.

Chat Opened Trigger

The Chat Opened Trigger component detects when a chat session starts, enabling workflows to respond instantly as soon as a user opens the chat. It initiates flows with the initial chat message, making it essential for building responsive, interactive chatbots.

Google Sheets Retriever

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.

Run Flow

The Run Flow component in FlowHunt lets you trigger and execute another workflow within your current flow. Pass inputs, variables, and control how flows interact, enabling modular and reusable automation. Ideal for chaining workflows or using flows as tools.

Iterator

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.

Generator

Explore the Generator component in FlowHunt—powerful AI-driven text generation using your chosen LLM model. Effortlessly create dynamic chatbot responses by combining prompts, optional system instructions, and even images as input, making it a core tool for building intelligent, conversational workflows.

Prompt Component in FlowHunt

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.

Tool Calling Agent

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.

AI Agent

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.

GoogleSearch Component

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.

URL Retriever

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.

Add Column in Google Sheets

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.

Update Row in Google Sheets

Effortlessly update specific rows in your Google Sheets directly within your FlowHunt workflow. This component connects your flow to Google Sheets, letting you modify data in real-time by specifying the target spreadsheet and row. Perfect for automating updates, syncing records, and keeping your data organized across processes.

Create Data

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.

Custom Trigger

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.

Chat Output

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.

Flow description

Purpose and benefits

Overview

This workflow is designed to automate the enrichment of a Google Sheets document containing a list of people, by programmatically finding and adding information such as their LinkedIn profiles, job titles, and industries. The flow leverages a combination of user interactions, AI agents, Google Search, URL content extraction, and Google Sheets automation to process and enrich data at scale.

The automation is modular, supports parallel processing (for scalability), and provides user-friendly feedback at each step. This makes it an ideal solution for organizations or individuals needing to augment contact databases, enhance lead lists, or keep large datasets up-to-date with minimal manual effort.


Key Workflow Steps

1. User Onboarding and Guidance

  • When the workflow is triggered, the user is greeted via a Button Widget with a localized welcome message explaining the process, and an actionable button to start.
  • Notes are included in the workflow to provide extra guidance, such as how to link the necessary Google Sheets document through advanced settings.

2. Retrieving Data from Google Sheets

  • The flow connects to a specified Google Sheets document (using the provided link) and retrieves data from a selected sheet.
  • This data forms the basis for all subsequent enrichment steps.

3. Ensuring Sheet Structure

  • An AI-powered Tool Calling Agent checks if the necessary columns (“LinkedIn”, “Job Title”, “Industry”) exist in the sheet.
  • If any are missing, the workflow can add them by invoking a component that creates new columns in the Google Sheet.

4. Enrichment Automation (Parallel Processing)

For each person (row) in the sheet, the flow carries out the following parallelized steps, allowing for scalable, fast processing:

A. LinkedIn Profile Enrichment

  1. Trigger: A custom trigger (alt_gen) starts the process for LinkedIn enrichment.
  2. AI Agent: An agent with the goal “Find the person’s LinkedIn and output the LinkedIn page link” is invoked.
  3. Tools Provided to the Agent:
    • Google Search (restricted by country/language).
    • URL Content Retriever (to fetch and parse web pages for more context).
    • Access to the Google Sheet as a tool for context.
  4. Data Construction: The agent’s output is structured into a data object with a “LinkedIn” field.
  5. Google Sheets Update: The new LinkedIn link is written back to the correct row in the Google Sheet.
  6. Feedback: The user receives confirmation in the chat output.

B. Job Title Enrichment

  1. Trigger: A custom trigger (job_title) starts the job title enrichment process.
  2. AI Agent: An agent with the goal “Find the person’s job title based on their LinkedIn and output that” is invoked, using similar tools as above.
  3. Data Construction: Output is structured into a data object with a “Job_Title” field.
  4. Google Sheets Update: The job title is updated in the sheet.
  5. Feedback: Confirmation is displayed to the user.

C. Industry Enrichment

  1. Trigger: A custom trigger (industry) initiates the industry lookup.
  2. AI Agent: The agent’s goal is to determine the person’s industry using all available tools.
  3. Data Construction: Output is structured into a data object with an “Industry” field.
  4. Google Sheets Update: The industry information is added to the sheet.
  5. Feedback: User is notified of the update.

Supporting Components

- Iterators

  • The workflow uses iterator components to loop over all rows in the Google Sheet, enabling batch/parallel processing for scalability.

- Prompt Templates & Generators

  • Prompt templates and LLM generators are included to dynamically create prompts or process context when needed (e.g., for generating alternative text or instructions).

- User Guidance

  • Notes and chat outputs are strategically placed to guide the user and provide actionable status updates at each key step.

Technical Flow Summary

StepDescriptionAutomation Benefit
User onboardingWelcomes user, provides instructions, and starts the processUser-friendly, reduces confusion
Sheet retrievalConnects to Google Sheets and fetches dataRemoves need for manual exports
Sheet structure validationEnsures required columns exist, adds them if missingData consistency
Parallel row processingEnriches each row for LinkedIn, job title, and industry in parallelMassive time savings
AI-powered enrichmentUses generative AI and search tools to find and extract information for each personHigh accuracy, less manual work
Data structuring & writingFormats found info and writes back to the correct rows in Google SheetsReliable, automated data updates
User notificationsKeeps user informed of progress and resultsTransparency, trust

Why This Workflow Is Useful for Scaling and Automation

  • Massively Reduces Manual Work: By automating the tedious task of searching for LinkedIn profiles, job titles, and industries, the workflow can process hundreds or thousands of records with minimal user intervention.
  • High Scalability: Through use of iterators and parallelism, the enrichment process is much faster than any manual operation.
  • Consistency & Accuracy: Ensures every record is checked and enriched in the same way, minimizing human error.
  • Integrates with Cloud Data: Direct connectivity to Google Sheets means no manual exports/imports are required.
  • Extensible: The modular design allows for adding other enrichment steps (e.g., company, email, etc.) in the future.
  • User Friendly: Well-placed instructions, feedback, and triggers make the process clear and easy to operate, even for non-technical users.

Example Use Cases

  • Sales & Lead Generation: Automatically enrich lead lists with up-to-date LinkedIn and job information.
  • HR & Recruitment: Quickly build detailed candidate profiles.
  • Market Research: Aggregate and enhance large datasets of professionals.
  • Event Planning: Gather and update information on attendees or speakers.

Visual Flow (High-Level)

flowchart TD
    Start(Welcome & Button) --> SheetFetch[Fetch Google Sheet]
    SheetFetch --> StructureCheck[Check/Add Columns]
    StructureCheck --> ForEachRow[For Each Row (Parallelized)]
    ForEachRow --> LinkedIn[Find LinkedIn Profile]
    ForEachRow --> JobTitle[Find Job Title]
    ForEachRow --> Industry[Find Industry]
    LinkedIn --> WriteLinkedIn[Write to Sheet]
    JobTitle --> WriteJobTitle[Write to Sheet]
    Industry --> WriteIndustry[Write to Sheet]
    WriteLinkedIn --> NotifyUser
    WriteJobTitle --> NotifyUser
    WriteIndustry --> NotifyUser
    NotifyUser((User Notified))

In Summary

This workflow is a powerful automation for data enrichment in Google Sheets, leveraging AI and web search to collect, process, and update person-related information at scale. It streamlines what would otherwise be a highly repetitive and error-prone task, making it ideal for teams and organizations that depend on accurate, up-to-date contact or professional data.

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