<p><b>Startup description</b></p><p>My startup is an AI shopping assistant for e-commerce brands that helps visitors quickly find the right products, answers support questions, and reduces support workload through an intelligent, conversational interface. It integrates natively with Shopify, BigCommerce, WooCommerce, and Ecwid, ingesting the store’s catalog and content to provide context‑aware recommendations and answers.</p><p>I started it after seeing merchants struggle with generic chatbots that didn’t understand products, stock, or policies. The goal was to build a vertical AI assistant tailored to e-commerce: plug into the store’s data, deploy a branded widget in minutes, and measurably improve conversion and support efficiency.</p><p><b>Key highlights</b></p><ul> <li>Annual revenue(projected): around $1,200</li> <li>Annual profit: close to breakeven; infrastructure costs are very low thanks to optimized usage and existing cloud credits.</li> <li>Lifetime revenue since launch: $570</li> <li>Live integrations across several e-commerce platforms.</li> <li>End‑to‑end RAG pipeline (documents + product catalog).</li> <li>Time‑to‑value: merchants can install, sync catalog, and deploy the assistant in under an hour.</li> </ul> <p><b>Team</b></p><p>It is currently run by a lean founder‑led team.</p><ul> <li>Founder: product, architecture, integrations, and overall strategy.</li> <li>Contractors/freelancers: occasional support on design, front‑end, or content.</li> </ul> <p>The codebase, infrastructure, and automations are all documented and can be handed over cleanly so a buyer can run it solo or fold it into an existing team.</p><p><b>Tech Stack</b></p><ul> <li>LLMs: Google Gemini for natural language understanding and generation.</li> <li>Infrastructure: fully hosted on Google Cloud.</li> <li>Google Cloud Storage for documents and unstructured data.</li> <li>MongoDB for users, accounts, and configuration.</li> <li>Qdrant for vector storage and retrieval in the RAG pipeline.</li> <li>Native Shopify app.</li> <li>BigCommerce app.</li> <li>Ecwid app.</li> <li>WooCommerce plugin.</li> <li>Application stack: Python backend, React/Next.js front‑end, REST/GraphQL APIs.</li> </ul> <p>This setup gives buyers a robust AI + RAG foundation that can be extended to more verticals or features without re‑architecting.</p><p><b>Marketing and growth</b></p><p>So far, growth has been intentionally lean and product‑driven:</p><ul> <li>App store presence: listings on Shopify, BigCommerce, and other ecommerce platforms bring in organic, high‑intent traffic.</li> <li>Content and positioning: website copy and documentation aimed at ecommerce merchants, highlighting conversion uplift and support automation.</li> <li>Direct outreach and networking: initial customers sourced via personal network, LinkedIn, and targeted outreach to ecommerce brands and agencies.</li> </ul> <p>As a result, CAC has been low, but the project is still at an early traction stage — making it a good candidate for a buyer who can invest in performance marketing, partnerships, or agency channels.</p><p><b>Return on investment</b></p><p>Given the low cost base, most new revenue drops straight to profit. A buyer who can grow MRR meaningfully can achieve payback relatively quickly.</p><p>Example:</p><ul> <li>At a purchase price of $5,000–$10,000 and MRR scaling to $500–$1,000 with focused marketing and sales, the payback period could be roughly 10–20 months.</li> <li>The main ROI lever is not cost cutting but revenue growth: more installs via app stores, partnerships with ecommerce agencies, and upselling existing users.</li> </ul> <p><b>Startup assets</b></p><ul> <li>Product &amp; IP: full ownership of the codebase, architecture, and all intellectual property.</li> <li>Domain &amp; brand.</li> <li>App store listings: existing live apps and integrations on Shopify, BigCommerce, WooCommerce, Ecwid.</li> <li>Google Cloud setup (projects, services, and configuration).</li> <li>MongoDB databases.</li> <li>Qdrant instance for vector storage.</li> <li>Billing &amp; customers: Stripe/billing setup, existing customer accounts, and historical data.</li> <li>Documentation: internal notes, integration docs, and any onboarding/support material for merchants.</li> </ul> <p><b>Risks</b></p><ul> <li>Early‑stage traction: low current MRR and limited customer base; future growth is not guaranteed.</li> <li>Competitive landscape: many AI/ecommerce tools are emerging; winning will require clear positioning and active marketing.</li> <li>Platform dependency: a large part of the value is tied to Shopify/BigCommerce and other platforms; changes in their policies or APIs could impact the apps.</li> </ul> <p><b>How to mitigate:</b></p><ul> <li>Use existing tech stack and integrations to iterate quickly on product and positioning.</li> <li>Invest in targeted marketing (e.g., app‑store optimization, agency partnerships, outreach to ecommerce brands).</li> <li>Diversify slightly across platforms and features to reduce dependency on any single ecosystem.</li> </ul> <p><b>Summary</b></p><p>My platfrom gives a buyer a ready‑made AI shopping assistant with:</p><ul> <li>A proven technical foundation (Gemini + RAG + vector search).</li> <li>Live integrations across major ecommerce platforms.</li> <li>Very low operating costs and a clean, asset‑only transfer (code, IP, domain, app listings, infra, billing).</li> </ul> <p>Instead of spending months building an AI ecommerce assistant and getting approved on app stores, a buyer can acquire it, plug it into their existing portfolio or agency offer, and focus immediately on growth and monetization.</p><p>✅ $570 in ARR</p><p>✅ 5 customers</p><p>✅ Business model: SaaS</p><p>✅ Built with Stripe, Google Cloud Storage, Shopify</p>