VectoSolve ↗: turns images into vector files Cricut, laser and embroidery machines can read, grown with SEO
- MRR
- $1,676 Verified 2026-10-20
- Total revenue
- $21,051
- Active subscriptions
- 253
Verified by TrustMRR
In one line: A tool that converts images into vector files for Cricut, laser and embroidery machines, with a free watermarked preview and pay-per-file or monthly plans.
- Founder
- RobX ↗ · Solo
- Dates
- Launched Late 2025
Timeline
- Founded date listed on TrustMRR
- He and his co-founder launched a batch of small SaaS products, VectoSolve among them (self-reported)
- First monthly revenue on TrustMRR: $6.5 (TrustMRR)
- Revenue that month: $969 (TrustMRR)
- Revenue that month: $4,261 (TrustMRR)
- Switches pricing from credit packs and small subscriptions to 2 annual plans plus API, and lists the product for sale on TrustMRR (self-reported)
Sources: TrustMRR: VECTOSOLVE; YouTube interview; X post by @robindiehacker.
TrustMRR figures are monthly revenue as shown on 2026-10-23 (read-only sync from the payment provider).
Compared with similar products
Sources: VectoSolve pricing page; Vectorizer.AI pricing page. Vectorizer.AI figures checked 2026-10-26.
4 moves VectoSolve made
Solved his own problem.
He needed to turn AI-made logos into SVG, so he built the tool.
Built landing pages in many languages.
Pages target searches like "PNG to SVG"; users then grew fast (self-reported).
Cut the free uses.
The first payment came after fewer free uses (self-reported).
Added export formats from user feedback.
Formats for Cricut, laser, embroidery and CNC machines.
Sources: Interview 05:56; Interview 07:15; Interview 10:13; Interview 10:48.
How to copy it
Build one feature: upload a file, get a downloadable result (spec it in one MD file and let AI write it)
Write a spec of at most one page in Markdown and hand it to Claude Code to build a single Next.js page: drop in a file, call an existing conversion API, preview and download the result. No signup or payments yet. What the founder did: he solved his own need first, writing an MD spec for Claude Code and iterating, initially using Recraft's API for conversion (interview 05:56, interview 14:13).Expand ▾Collapse ▴
Write a spec of at most one page in Markdown and hand it to Claude Code to build a single Next.js page: drop in a file, call an existing conversion API, preview and download the result. No signup or payments yet. What the founder did: he solved his own need first, writing an MD spec for Claude Code and iterating, initially using Recraft's API for conversion (interview 05:56 ↗, interview 14:13 ↗).
- A working page where a dropped file can be previewed and downloaded as a result.
- You process a batch of your own real files with it and stop using other tools.
Write a one-page Markdown product spec for Claude Code: a single Next.js page where the user drops a <input format> file, the backend calls <conversion API>, and the page previews and downloads the <output format> result. Cover page elements, the API flow, file size limits, error messages, and no signup or payments. Follow official docs and mark anything uncertain "to check".
Step 1: build the whole single-page tool from the Markdown spec; the tool was built from an MD spec with Claude Code ("I make a MD and put to cloud code").
Step 1: convert PNG/JPG to SVG; it first used Recraft for SVGs, then an open-source model plus scripts.
Step 1: build the web app; the web app uses Next.js ("for web app only next JS").
See which conversion pages similar sites have, build a batch of landing pages and submit to Search Console
Open a few similar sites, copy the page names in their navigation and turn a batch into landing pages, each with the step 1 upload box above the fold and an explanation and FAQ below. Connect the site to Google Search Console and submit the sitemap. What the founder did: he studied competitors and benchmarked keywords, then had AI build a batch of "format to format" landing pages, after which users grew sharply (self-reported, interview 07:59); he finds keywords mainly from Search Console exports (interview 15:02).Expand ▾Collapse ▴
Open a few similar sites, copy the page names in their navigation and turn a batch into landing pages, each with the step 1 upload box above the fold and an explanation and FAQ below. Connect the site to Google Search Console and submit the sitemap. What the founder did: he studied competitors and benchmarked keywords, then had AI build a batch of "format to format" landing pages, after which users grew sharply (self-reported, interview 07:59 ↗); he finds keywords mainly from Search Console exports (interview 15:02 ↗).
- A batch of live landing pages and a sitemap successfully submitted in Search Console.
- These pages start getting impressions and clicks in Search Console.
Below are page names from similar sites' navigation and my Search Console query export (if any). Please: 1) merge, dedupe and group them by search intent; 2) pick the batch most worth building first and say why; 3) write an English title, description, H1 and 3 FAQs per page; 4) use only my material, no invented search volumes. Material: <paste>
Step 2: submit the sitemap and see which queries bring impressions; Search Console exports feed the coding ("I just uh mainly use the search console and make export and give to code").
Step 2: generate the 20 landing pages from one shared layout.
Cut the free allowance to very little, sell single files and a monthly plan via Stripe, and get the first payment
Cut the free allowance to very little (or watermarked previews only), and after the preview offer two options: buy just this file, or a monthly plan, collected with Stripe Checkout. You set the prices. What the founder did: traffic arrived but nobody paid until he cut the free allowance sharply and then got his first payment (interview 10:13); the pricing page now sells both single files and subscriptions (vectosolve.com pricing).Expand ▾Collapse ▴
Cut the free allowance to very little (or watermarked previews only), and after the preview offer two options: buy just this file, or a monthly plan, collected with Stripe Checkout. You set the prices. What the founder did: traffic arrived but nobody paid until he cut the free allowance sharply and then got his first payment (interview 10:13 ↗); the pricing page now sells both single files and subscriptions (vectosolve.com pricing ↗).
- The free allowance is reduced; after the preview two paid options appear and both succeed in Stripe test mode.
- Stripe shows the first real payment (not test mode).
My conversion page has traffic but no payers. The free allowance is <allowance> and will become <new allowance>, with two options after the preview: one file at $<price> one-time, or $<monthly price>/month for <count> conversions. Write: 1) English popup copy: headline, one line per option, button text; 2) the message when the free allowance runs out; 3) backend pseudocode for Stripe Checkout in both one-time and subscription modes. Use only my numbers.
Step 3: take one-time payments and subscriptions; the founder reads his revenue off Stripe in the interview, and TrustMRR's data also comes from his Stripe.
Collect requested export formats through live chat and ship a dedicated export with its own landing page
Add a live chat widget and ask on the download success page where the user will use the file. When the same need keeps coming up, have Claude Code build the export format that use requires, then add a matching landing page and submit it to Search Console. What the founder did: he collects feedback through Crisp, users kept asking for exports for specific machines, and he added exports, landing pages and articles (interview 10:48, interview 30:05).Expand ▾Collapse ▴
Add a live chat widget and ask on the download success page where the user will use the file. When the same need keeps coming up, have Claude Code build the export format that use requires, then add a matching landing page and submit it to Search Console. What the founder did: he collects feedback through Crisp, users kept asking for exports for specific machines, and he added exports, landing pages and articles (interview 10:48 ↗, interview 30:05 ↗).
Original page 1 ↗Original page 2 ↗Original page 3 ↗Author post 4 ↗
- The chat widget is live and the success page asks the question; a requests log exists; at least one new export format and its landing page are live.
- A user adopts the new export and pays through the new landing page.
Below are users' answers, from chat and the download success page, to "where will you use this file". Please: 1) group them by use and count; 2) for the most common use, describe the export format and settings it needs; 3) write an English title, description, H1 and 3 FAQs for a landing page for that use; 4) use only these answers. Answers: <paste>
Step 4: a site chat box to collect the machine formats users want; the site uses Crisp as its chat box ("I have uh put a crisp. It's a chatbot.").
Step 4: build the machine-specific export and its landing page.
Step 4: submit the new landing page and watch its impressions.
Sources
- TrustMRR
- TrustMRR public Markdown page (sync time, revenue and sale listing)
- From the founder in an October 2026 interview: AI Bites, "$3K mrr AI SaaS founder did it in 6 months!", 05:56, the need came from converting his own logos to SVG
- From the founder in an October 2026 interview: 07:15, about 1,000 landing pages and 10x users two weeks later
- From the founder in an October 2026 interview: 10:13, the first payment came after cutting free conversions from 25 to 3
- From the founder in an October 2026 interview: 10:48, adding machine export formats from user feedback
- From the founder in an October 2026 interview: 32:31, about $3K revenue and about $1,500 MRR on Stripe
- X: "I put my SaaS VectoSolve ($1.7K MRR) up for sale on TrustMRR" (2026-10-10, self-reported)
- X: "I build this for some new customers" (Bambu Lab page, 2026-10-11)
- Pricing page vectosolve.com/pricing
- VectoSolve website
- youtube.com
- vectorizer.ai
Next case
SEOBOT: blog SEO AI agent
Business model: B2B SaaS subscription (listed at about $49/month)
Data source: TrustMRR, checked 2026-10-22
