Ashish Kumar Swain
Hi, I’m Ashish — an automation architect and AI enthusiast helping people work smarter. I build AI-powered workflows that save time, boost productivity, and create real-world impact. Passionate about no-code, startups, and smart systems that scale. Let’s automate success, one workflow at a time! 🚀
Templates by Ashish Kumar Swain
Smart job search: resume scoring & tailoring with OpenAI, Apify, and Airtable
Who is this for? This workflow is designed for job seekers who want to automate their job application research and resume optimization. It's ideal for professionals who want to match their CVs to new job postings daily, improving the chance of landing interviews without manual work. Use case Problem: Manually searching for jobs, matching resumes, and updating application records is time-consuming and inefficient. Use Case: Automatically fetches new job listings based on user preferences, scores them against the user's existing CV, generates a revamped CV tailored for each job, and stores everything neatly into an Airtable database for easy tracking. What this workflow does? Fetches user job preferences from Google Sheets daily. Searches for jobs matching those preferences using Apify’s scraping. Filters job posts that are fresh (posted within 24-48 hours). Scores each job against the user’s current CV using an OpenAI agent. Generates a revamped CV tailored to each job. Stores the job listing, compatibility score, match reason, and revamped CV into Airtable for future use. API Credentials Required Google Sheets API Credentials — for reading user-defined job preferences. Apify API Key — to scrape job postings (e.g., Indeed Scraper Actor). OpenAI API Key — for AI scoring and CV enhancement. Airtable API Key — for job listing and tracking. Setup Google Sheets: Store your job preferences (like titles, locations, etc.). Apify API: Set up a scraper for LinkedIn, Indeed, or other job boards. OpenAI API: Provide access to a GPT model (ideally GPT-4 Turbo) to handle CV scoring and revamping. Airtable: Create two tables: One for archived jobs (old jobs >48 hours). One for current processed jobs with AI scores and revamped CVs. Columns for Airtable: jobtitle,company,location,dateposted,job_type,description,link,compatibilityScore,matcReason,revampedCV,newCompatibilityScore,newMatchReason. n8n: Deploy the full workflow with nodes for triggers, loops, API calls, parsing, and storage. How to customize it for your needs Edit Job Preferences: Add or update the fields in Google Sheets (Columns: jobtitle, joblocation) to search. Fine-tune AI Prompts: Adjust the scoring criteria (e.g., favor remote roles, leadership experience, certifications). Customize CV Style: Configure the AI to generate shorter, more detailed, or industry-specific resumes. Change Storage Destination: Replace Airtable with Notion, Google Sheets, a CRM system, or even send yourself Slack updates. Expand Job Sources: Easily swap the job scraper to pull listings from your favorite niche job boards. Why Use This Template? Saves 10+ hours/week on manual job search. Instantly tailor CVs to each application. Centralizes all data across Google Sheets and Airtable. Flexible — customize AI prompts, scoring logic, or expand to multiple users! Need Assistance? For setup guidance, customization, or business inquiries, Email: phoenixaiagentsolutions@gmail.com
By Ashish Kumar SwainAuto-label Gmail messages with custom categories using GPT-4o-mini
Why settle for Gmail’s default tabs when AI can sort your inbox your way? Who is this for? Job seekers, freelancers, and students who receive job-related emails and want them auto-sorted into labels Use case Problem: Job emails get buried.Manually reading, labeling, and tracking them in a sheet is error-prone and time-consuming. Use Case: An n8n workflow that fetches new Gmail messages on a schedule, classifies them with OpenAI, adds customised Gmail labels defined by the user. What this workflow does? Runs on a schedule (Cron) to fetch new Gmail messages. Uses OpenAI to classify each email into: Job Opportunity, Application Status, Enquiries, or Others (you can edit categories). Adds Gmail labels accordingly (auto-creates them if missing). Prerequisites n8n (Cloud or self-hosted). Credentials set up in n8n: Gmail OAuth OpenAI (API key) Setup Import the JSON (below) into n8n. Open Credentials for Gmail, OpenAI. In the OpenAI node, pick your model (e.g., gpt-4o-mini or any GPT-4 class model). In the Gmail Add Labels nodes, confirm/adjust label names: Job Opportunity, Application Status, Enquiries, Others How to customize it for your needs Add a Follow-Up Date parser (e.g., “We’ll get back in 2 weeks”) → push to Calendar. Extend categories (e.g., Interview Invite, Offer, Rejection, HR Enquiry). Threaded Gmail Draft Reply for Enquiries using a template. Troubleshooting No items flowing? Check Gmail scope/label filters and Cron timing. Labels not created? The Gmail node can create them; ensure the label names match exactly. Why Use This Template? Gmail can classify messages into Spam, Promotions, or Social, but have you ever wished you could sort emails your own way? With this AI-powered workflow, you can create custom categories that fit your needs — like Job Opportunity, Application Status, or Enquiries — so you never lose track of what matters. Need Assistance? For setup guidance, customization, or business inquiries, Email: phoenixaiagentsolutions@gmail.com
By Ashish Kumar SwainFinancial reporting AI: concise SEC 10-K/10-Q briefs via OpenRouter + Perplexity
Why skim 10-K/10-Q by hand when AI can extract what matters in minutes? Who is this for? Sales engineers, solution architects, founders, product/strategy teams, analysts, and BD reps who need fast, consistent briefs on public companies—plus a mapping to a chosen vendor’s solutions. Use case Problem: SEC filings are dense. Manually summarizing financials, spotting initiatives, and aligning them to a vendor’s offerings is slow, error-prone, and often missed by busy teams. Use Case: An n8n workflow that takes a company name / URL / ticker, analyzes the latest 10-Q/10-K with Perplexity “Deep Research” (via OpenRouter), extracts a concise financial overview and top 4–5 initiatives (tech, cost, revenue), then maps them to one vendor’s solutions (e.g., Microsoft, Google, T-Mobile) and outputs a clean brief. What this workflow does Trigger: “When chat message received” (or webhook) accepts name / URL / ticker + target vendor. AI Agent: Chat Model (OpenRouter) orchestrates the prompt and formatting. Tool: Perplexity Deep Research performs retrieval over the latest 10-K/10-Q and recent references. Output: Creates a Google Drive document from the generated text (title, summary, initiatives, vendor-solution matches, suggested contacts). (Optional) Append a row to Google Sheets for tracking companies, initiatives, and match scores. Prerequisites n8n (Cloud or self-hosted). Credentials in n8n: OpenRouter API key (with access to Perplexity’s Deep Research model). Google Drive (and Google Sheets, if you add the sheet step). Setup Import the workflow JSON into n8n. Open Credentials → connect OpenRouter and Google Drive. In the AI Agent node: Set Chat Model to your OpenRouter model. Set Tool to Perplexity’s Deep Research endpoint. Paste the provided prompt that(example): Summarize the key financial highlights and list the key strategic initiatives from Office Depot's most recent quarterly report. The company's URL is https://www.officedepot.com and its stock symbol is ODP. After gathering this information, compare Office Depot's initiatives with the solutions offered by T-Mobile for Business (URL: https://www.t-mobile.com/business, stock symbol: TMUS). Finally, provide specific recommendations on T-Mobile solutions that can help Office Depot achieve its initiatives. Explain how each recommended solution would help. Present the final response with clear headings for each section. How to customize it for your needs Ticker/Name disambiguation: Add a guardrail step that confirms the exchange + CIK before analysis. EDGAR fetch (advanced): Pull the exact 10-K/10-Q document/link and pass it to the model for grounded citations. Multi-vendor mapping: Loop over a list (e.g., Microsoft, Google Cloud, AWS) and produce a comparison table. Contact enrichment: Add your preferred enrichment step to suggest roles (IT, Network, Data, Finance). Scoring: Compute initiative ↔ solution fit scores and prioritize must-explore actions. Alerts: Send the brief to Slack/Telegram/Email for your team. Troubleshooting Wrong company matched? Add a pre-check that resolves ticker → legal name and require confirmation. Generic web summary? Tighten the prompt: “Use the latest 10-Q/10-K; cite sections; list initiatives with evidence.” Empty Drive file? Verify the AI Agent’s {{$json}} mappings flow into the Drive node’s content. No citations? Require bullet-level references; if missing, loop once with a “citations-only” follow-up prompt. Why Use This Template? Turn hours of filing review into a repeatable, shareable brief. You’ll get: A clean financial snapshot, The company’s top initiatives, A vendor-aligned solution map you can act on immediately—great for prospecting, QBRs, and strategic planning. Expected Outcome: Need Assistance? For setup guidance, customization, or business inquiries, Email: phoenixaiagentsolutions@gmail.com
By Ashish Kumar Swain