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I’m Jesús Núñez, an artist and the owner of J. Nunez Gallery, an independent contemporary art gallery with locations in Summit and Millburn, New Jersey. Founded in 2020, the gallery presents original abstract and figurative art, landscapes, still life, and photography. We help people discover artwork for their homes and work with interior designers on residential projects. Alongside running the gallery, I’m exploring how ChatGPT can help a small business improve its operations, marketing, and decision-making. I manage the business myself, so the practical question is how to turn existing information into useful, completed work. The workflow starts with real source material: website content, spreadsheets, analytics exports, screenshots, and the gallery’s business rules. I use focused conversations to examine one area, prepare concrete changes, and review the results. The work includes website improvements, artwork catalog organization, marketing analysis, audience segmentation, and outreach preparation. For example, AI-assisted troubleshooting helped identify duplicate website page-view tracking and guided its correction, which we checked against live analytics. That is an operational improvement; it is not evidence of increased sales. Another small-business owner can recreate the approach as follows. Step-by-step: 1. Record the business context: customers, products, geographic market, budget, brand preferences, and operating constraints. 2. Choose one specific task and define what completion means. Provide the relevant source material. 3. Require the AI to distinguish verified facts, interpretations, missing information, and proposed actions. It should not guess prices, contact details, or results. 4. Ask for a concrete deliverable, such as revised website copy, a classification table, implementation instructions, or an outreach draft. 5. Review the work and verify the actual result after implementation. Keep preparation, implementation, verification, and blocked work clearly identified. 6. Preserve approved decisions and completed actions in a current record so later sessions can continue from the existing work. 7. Measure qualified inquiries, appointments, proposals, and sales alongside marketing activity. This is an ongoing project. AI can repeat recommendations or lose track of previous decisions, so human review and continuity remain essential. I have not established a measured revenue increase or time-saving figure. I’m testing whether this approach can build a stronger business while allowing more attention for art, artists, and clients. If this example makes you curious about the gallery, you can browse available artwork and find information about our Summit and Millburn locations at https://www.jnunezgallery.com. You are welcome to explore the collection or contact us through the website for help choosing art. Jesús Núñez Artist and Owner, J. Nunez Gallery Summit and Millburn, New Jersey https://www.jnunezgallery.com

I feed the workflow product pictures, and it creates an entire studio photoshoot with on-model catalogue images at scale. 1. I provide the workflow with product pictures. 2. The workflow generates studio photoshoot images featuring the products on models.

As a business owner, I used to check performance data across Google Analytics 4, Google Search Console, and Google Merchant Center by logging into three separate portals, navigating complex submenus, and exporting raw CSVs. To solve this, I built an automated workflow that connects my AI coding assistant, Google Antigravity, directly to all three Google platforms through a Google Cloud Service Account. Instead of manually clicking through dashboards, I can ask natural-language questions in plain English, such as "Did yesterday's email send generate traffic or phone bookings?" or "Are any products disapproved in Google Merchant Center?" I then receive instant, cross-platform analysis in seconds. Step-by-step: 1. I created a Google Cloud Service Account by going to Google Cloud Console (console.cloud.google.com) > IAM & Admin > Service Accounts > Create Service Account. I named it `ai-marketing-assistant`, copied the generated service account email address, such as `ai-marketing-assistant@<project-id>.iam.gserviceaccount.com`, and downloaded the JSON private key file to my local project folder. 2. In Google Cloud Console > APIs & Services > Library, I enabled the three required APIs: - Google Analytics Data API - Google Search Console API - Content API for Shopping (Google Merchant Center) 3. In Google Analytics > Admin > Property Access Management, I added the Service Account email address with "Viewer" permissions and copied my numerical GA4 Property ID. 4. In Google Search Console > Settings > Users and permissions > Add User, I added the Service Account email address with "Full" or "Restricted" access. 5. In Google Merchant Center > Settings & Tools > Account Access > Add User, I added the Service Account email address with "Standard" or "Admin" access and ensured that "Content API access" was enabled. 6. In my local AI environment, such as Google Antigravity or a custom Python environment, I authenticated using standard Google client libraries (`google-oauth2`, `googleapiclient`, `google-analytics-data`) connected to the downloaded JSON key file. 7. Anytime I launch a marketing campaign, want an SEO audit, or need to check product feed health, I ask my AI assistant a plain-English question. 8. The AI executes live API queries against GA4, Search Console, and Merchant Center, cross-references website traffic with bookings, and delivers an instant report.

I used AI (Perplexity Computer) to buy jeans for myself because I hate online shopping. It hunted for deals on the brands I like and even double checked each link was in stock for my size. Great success. Step-by-step: 1. I told Perplexity Computer the brands I preferred and the exact size I needed. 2. I asked it to search for jeans that matched those preferences and look for active deals. 3. I had it open the promising product pages instead of relying only on search-result snippets. 4. I asked it to verify that each recommended link actually had my size in stock. 5. I compared the final in-stock options and bought from the shortlist.

I used Claude to help my brother-in-law build a Shopify website for his e-commerce store. By sharing screenshots, design references, and business requirements, I had Claude generate Liquid code, troubleshoot theme issues, modify page layouts, and explain exactly where changes needed to be made within Shopify. Instead of digging through forums and documentation for every issue, Claude acted like an on-demand Shopify developer, helping with everything. It sped up development, allowing the site to move from concept to launch much faster. Step-by-step: 1. I shared screenshots, design references, and the business requirements for the Shopify store with Claude. 2. I asked it to generate the Liquid code needed for the theme and explain where each change belonged in Shopify. 3. I used it to modify page layouts and translate visual feedback into specific code changes. 4. When theme issues appeared, I brought the errors and screenshots back to Claude for troubleshooting. 5. I repeated that loop until the store moved from concept to a working launch.

I use AI agents across roughly half of my startup, including search, security testing, development, and affiliate integrations. - Smart Shopping Deals search: It never relies on a single AI provider. I use a chain of backup providers for LLM-based search and embeddings with pgvector. If one provider fails or times out, the system silently retries with the next. A slow provider never hangs the request, and users never see the failure. In the worst case, the system falls back to plain search. - Security testing: I run the autonomous AI security agent Strix against my live preview after every deploy. It actively attacks the app the way a hacker would. - Development workflow: My real “team” is a four-layer testing rule enforced by the AI itself. Every feature has to ship with backend tests, component tests, full browser end-to-end tests, and test-data setup, all in the same commit. Claude Code doesn’t consider a feature “done” until all four exist—not just the code. - Affiliate parsing: One parser handles every affiliate network. I paste in the raw ad code from any network, and the same parser automatically extracts the banner link, image, and destination URL. No network-specific code is needed.

Each day my OpenClaw checks the local inventory of Toyota dealerships within 250 miles for black or white RAV-4's with heated seats because my wife wants to buy the most popular car in the world and they don't stay on the lots very long. Step-by-step: 1. I defined the exact vehicle requirements: a black or white RAV-4 with heated seats. 2. I set a search radius of 250 miles around the local area. 3. I configured OpenClaw to check Toyota dealership inventory every day. 4. I had it filter out vehicles that did not match the color and feature requirements. 5. I used the recurring results to catch suitable inventory before popular configurations disappeared from the lot.