Gmail to Jobber Request Automation System

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in API Integration, PHP, GraphQL, Node.js, Python As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@vishal1145

Your Gmail-to-Jobber pipeline will fail silently if you don't implement idempotency keys — duplicate emails will create multiple Jobber requests during network retries, and your team won't catch it until clients complain about redundant quotes. Quick questions - are you planning to use Jobber's GraphQL API or REST endpoints for request creation? And what's your tolerance for processing latency between email receipt and Jobber record creation? Here is the architectural approach: - GMAIL API: Build a push notification webhook using Pub/Sub rather than polling to eliminate the 5-minute delay inherent in IMAP-based solutions and ensure sub-60-second processing. - AI MODEL INTEGRATION: Use GPT-4 with function calling to extract structured client data and work details, then validate outputs against Jobber's schema before mutation to prevent malformed requests. - GRAPHQL + OAUTH 2.0: Implement token refresh logic with exponential backoff and store refresh tokens in encrypted storage to maintain 24/7 uptime without manual re-authentication. I've built similar AI-powered integrations for 2 field service companies that process 500+ work orders weekly without manual intervention. Let's schedule a 20-minute technical call to map your error-handling requirements before implementation.

@shayankhan333

Forwarded emails are where most Gmail-to-CRM automations fail, the parser locks onto the forwarder as the client instead of the original sender. I'll run a Node service with Gmail push notifications into an LLM extraction step with a strict JSON schema, then match or create the Jobber client via GraphQL and attach the PDF snapshot plus photos through their upload mutation. You can see my past work and reviews here: https://www.freelancer.com/u/shayankhan333 1) Is the shared inbox a Google Workspace account where I can enable Pub/Sub, or a standard Gmail? 2) Should duplicate detection key off sender plus subject, or the parsed job address? Cheers Shayan

@Eliteinno

Hey, For this workflow, reliability depends on treating each email as a traceable job from Gmail through AI extraction to the final Jobber Request. Forwarded messages, duplicate deliveries, missing attachments, and partial API failures need explicit state handling; otherwise a simple Gmail-to-Jobber automation can silently create duplicate clients or missed requests. I propose a production workflow using Gmail/Google Workspace APIs, structured AI extraction, PDF/image generation, attachment processing, Jobber API/GraphQL operations, and OAuth 2.0. I’ll structure the workflow around idempotency and persistent processing state, with validation before creating clients or requests, retries for transient failures, detailed logs, failure alerts, and a recover

@ashinnd84

Hello there, we are a team of automation developers and we can do this project in no time. Please, send me a message to discuss the work. Thanks Ashish Kumar.

@AndrewJSMAN

I've built production-grade email-to-Jobber automation systems before, so this is right in my wheelhouse. I'll set up a Node.js service with Gmail API polling, process emails with AI (LLM) for structured data extraction, generate PDF snapshots with Puppeteer, and handle file attachments via Jobber's multipart upload API. Error handling will include retry logic, duplicate detection, and Slack/email alerts for failures. I'll deploy on a reliable cloud host with monitoring and logging in place. I can start immediately. Thanks, Andrii.

@MSaadJaved

Hi, I’ll build a Gmail-to-Jobber workflow that reliably detects new work orders, extracts the correct client/request from forwarded threads, and creates fully populated Jobber Requests with attachments, email snapshots, and duplicate protection. The main technical risk is misidentifying the real job source in long forwarded chains, so I’ll use structured LLM extraction plus deterministic parsing and validation rules. I recently built an email-to-CRM automation that parsed messy inbox threads, matched contacts, and pushed structured records with file handling and retry logic. For your project, I’d begin with Gmail/Jobber API mapping and sample-email analysis, then complete extraction, client matching, request creation, logging, alerts, and end-to-end testing. Would you like the workflow to prioritize Jobber client matching by email, phone, or company name when several fields conflict? Best regards, NexLoom

@pasion0128

★•══•★ Hi client ★•══•★ I have recently solved a similar React navigation issue where mobile breakpoints caused menu elements to disappear. I’d trace the component/CSS behavior, verify react-router links and dropdowns, then apply a clean responsive fix without affecting desktop behavior. Can I inspect the navigation and get this mobile flow working correctly? Best regards, Rico

@jonasdedzo

Hello, I can build this as a production-ready Gmail → AI extraction → Jobber workflow, with reliability and duplicate protection as the priority. I’d use Gmail API/OAuth to monitor the shared inbox, identify the real client even when messages are forwarded, then use an LLM with structured JSON validation to extract the title, client, work overview and notes. The workflow would generate an original-email PDF/image snapshot, process photos/PDFs, match or create the correct Jobber client, and create the Request through Jobber’s GraphQL API with the relevant attachments. I’d add message/thread IDs and processing records for idempotency, retries, failure queues, structured logs, monitoring and alerts so orders are not silently missed or duplicated. Node.js or Python with n8n where useful would keep the system maintainable and ready for long-term expansion. Looking forward to work with you. Thanks

@dataspro

Nice to meet you ,The requirements of your project match my areas of work and skills, to introduce myself. My name is Anthony Muñoz and i am the lead engineer for DS Pro IT agency. I have worked for over 10 years as a Full-Stack and software development engineer and have successfully done multiple jobs. It will be a pleasure to work together to make your project. Feel free to discuss about the project with me, greetings.

@XRProConnect

Hi, Aashiq (Ash) here from Cape Town, South Africa. This project instantly caught my eye, so I had to reach out. I see you’re looking for someone to automate the process of converting Gmail work orders into Jobber Requests, streamlining the workflow and improving efficiency. That's a game-changer for any business. With years of experience in API integrations and automation, I’ve helped businesses minimize manual data entry through reliable systems. I have a solid track record with Gmail APIs, Jobber integrations, and AI-driven data extraction. I’d love to share examples of my successful projects that align with your needs. Based on what you mentioned, here is how we would approach the project: - Set up 24/7 monitoring of the Gmail inbox. - Use AI to extract and format data efficiently. - Implement robust error handling and logging. - Ensure seamless API interactions with Jobber. You can expect clear communication throughout the process, and I will deliver a user-focused solution optimized for performance. Best Regards, Aashiq

@SherozKhalid9

Hi, I’ve built AI-powered API automation where unstructured emails are converted into structured records and pushed into third-party systems. For your workflow, I’ll build a reliable Gmail → AI → Jobber pipeline that understands forwarded work orders, identifies the real customer, extracts job details, and creates the correct Jobber Request. The AI will clean the email/thread, identify the client and request, extract Title, Client, Work Overview, and Notes using structured output, validate the data, and flag uncertain results before creating a job. For duplicate protection, I’ll store each Gmail message/thread ID and check it before creating a Jobber Request, with safe retry handling to prevent duplicate jobs. Delivery plan: Gmail + AI extraction → Jobber integration → client/request creation → attachments → duplicate protection, logging, alerts, testing, and 24/7 deployment. I have hands-on experience with AI/LLM integrations, APIs, OAuth, webhooks, Node.js/Python, and production automation. I’ll build this for reliability, not just as a basic automation. Share the Jobber API documentation and Gmail setup, and I can start immediately. Regards, Khalid

@invoketech

Hello, I understand the main challenge is turning unpredictable Gmail work-order emails, including forwarded messages and attachments, into accurate Jobber Requests automatically, without duplicate, missed, or incorrectly assigned jobs. I can build a production-ready workflow using Gmail/Google Workspace APIs, Node.js or Python, AI/LLMs, and Jobber’s GraphQL API with OAuth 2.0. The system will identify the actual client and request, extract structured details, generate the original-email snapshot, process photos/PDFs, find or create the correct Jobber client, and create the Request with all relevant attachments. I’ll also build the reliability layer around it, including duplicate protection, retries, error handling, detailed logging, monitoring, and alerts so the workflow can run continuously with minimal manual intervention. The AI extraction will use structured outputs and validation rather than simply passing raw email text through an LLM. I have 10+ years of experience with API integrations, automation, AI/LLM workflows, and production systems. Let’s jump on a quick call to review your Gmail and Jobber flow and discuss the best implementation approach. Thanks, Invoke Tech

@rofeald

Hi, I can build the Gmail-to-Jobber automation that watches the shared inbox, parses forwarded work orders, extracts structured request data with an LLM, and creates complete Jobber Requests with attachments. I’d implement this in Python or Node.js with Gmail API, Jobber API/GraphQL, OAuth 2.0, and webhook-driven processing. The workflow would detect new emails 24/7, identify duplicates, generate a PDF/image snapshot of the source email, move attachments, match or create the Jobber client, then post the Request through API mutations with the extracted title, overview, and notes. For maintainability, I’d keep parsing, extraction, file handling, and Jobber sync as separate modules so the system is easier to update as email formats change. Logging, retry logic, and alerting would be built in for missed orders, API failures, and attachment issues. I’ll validate with staged test emails, attachment cases, forwarded-thread scenarios, and error-path checks before go-live. I’m available to start in Egypt timezone and can move forward with a production-ready implementation.

@manh2906

Hi, there. "GMAIL TO JOBBER AI AUTOMATION" — you need reliable order handling without manual email entry. I would build this with a queue-based workflow that separates email reading, AI extraction, and Jobber API updates, because a single failed step should not lose a customer request. I would add duplicate checks, logs, and retry handling so forwarded emails, missing files, and API delays are handled safely. I have worked on automation flows where incoming data is parsed, cleaned, and sent into external systems with validation steps. For this project, I would focus on Gmail API, OAuth, GraphQL requests, and structured AI extraction rather than fragile no-code-only flows. Do you already have the Jobber API access and preferred AI provider, or should I include the setup as part of the workflow? Looking forward to working with you.

@tamannarajput18

With extensive experience in API integration and a strong command of Node.js, I'm confident I can deliver the automation you need to streamline your Gmail to Jobber workflow. Over the years working as an Automation Specialist, I've built a reputation of turning manual tasks into efficient, automated systems that save businesses both time and money. I've developed data scraping tools, AI-powered chatbots, and workflow automations - all aimed at increasing productivity by minimizing manual labor. My skills in handling APIs and my familiarity with Python further cement my ability to build this reliable production system for you. In addition to my expertise with APIs, I have hands-on experience with Gmail API and working knowledge of GraphQL and OAuth. These are all essential in ensuring the seamless transition of work-order emails from Gmail to Jobber. Furthermore, I have exposure to AI/LLM integrations which aligns well with your aim of extracting structured data from emails. This also includes creating snapshots in PDF or image formats which I am well-versed in doing.

@mikes126

I will build a production-ready Gmail → AI → Jobber pipeline that detects forwarded work orders, extracts structured client/request data, generates an email snapshot, transfers attachments, and creates or updates the correct Jobber Request through API/GraphQL. The workflow will use Gmail API/OAuth 2.0, Python or Node.js, an LLM with structured JSON output, and Jobber API mutations, with validation before any Request is created. The main challenge is identifying the actual client when emails are forwarded or contain multiple conversations. I will parse headers, threads, quoted content and attachments, then use AI plus deterministic rules to identify the real requester and work details.

@rajeshrolen

Hi, At first glance, this looks straightforward but there’s usually one part that causes issues later. I’ve handled similar work before and can help you avoid that. Regards, Rajesh

@Chromasoftware

HELLO!! I can build your Gmail-to-Jobber AI automation to turn incoming work-order emails into properly populated Jobber Requests with minimal manual work. I’ll handle Gmail monitoring, forwarded-email parsing, AI extraction of client/request details, email snapshots, attachments, Jobber client matching/creation, Request creation through API, and reliable duplicate/error protection with logging, monitoring and alerts. I have 8+ years of software development experience with API integrations, Google Workspace, AI/LLM workflows, Node.js, Python and automation platforms such as n8n. I can build this as a production-ready service rather than a basic workflow. I’m available to start immediately and can review the Jobber API requirements and define the implementation phases. Please note that I’m based in India, so the Egyptian-applicants-only requirement would need to be flexible. Thanks! Chromasoftware