VERLYour AI-built app, fixed, finished, and shipped.
Built in Lovable, Bolt, Base44, or Replit and stuck at 80%? I fix the deployment blocker, the security hole, and the error loop the AI keeps patching wrong. Below that: listing optimization, AI automation, data research, and Python tools from the same operator. The big claims link to proof, every build ships with documentation, and you own what I hand over.
Rescue my app — from $95 See the proof first
Start here // vibe-code rescue
Vibe-code rescue — fix, finish, deploy
For Lovable, Bolt, Base44, and Replit apps stuck at 80%: deployment blockers, exposed API keys and open database rules, half-wired features, design cleanup, and doom-loops broken at the root cause. Fixed-scope tickets, handled asynchronously. $95 diagnose + one fix / $295 fix, secure & deploy / $750 finish the app. If it needs a rebuild instead of a patch, you hear that before you pay.
Listing optimization
Amazon, Shopify, or eBay listings rebuilt around verifiable specs and how buyers actually search. Title, bullets, description, backend terms, and a note explaining what changed and why.
Operations dashboards & systems
Tracking systems and operations dashboards built in Python and delivered with documentation. The pattern behind a 24-project operations board, a live debt inventory, class and market dashboards. Proof first in the work section; every job is a fixed-scope quote, no retainers.
AI automation builds
Custom agents and workflows that survive contact with real data. Scoped honestly before you pay: what automates reliably, what still needs a human.
AI workflow audits
Your recurring tasks mapped and honestly scored: automate, assist, or leave alone. A prioritized roadmap that names the mechanism for every recommendation and is allowed to say no.
Data research
Public web data collected, cleaned, deduplicated, and summarized into a report you can decide with. Not a raw dump.
Custom Python tools
Scripts that replace a manual weekly task. Commented code, plain-English run instructions, error handling that tells you what happened.
Proof layer // receipts, not stamps
Newegg listing curation: 45% average sales lift
Method, so you can check the number: at Newegg I scored 3,000+ weekly product submissions against historical performance and promoted the top 1,200. Promoted products averaged a 45% sales lift over the unpromoted baseline. Same mechanism I now run as listing optimization: verifiable specs, buyer search behavior, no invented claims.
See the case on Fiverr ↗Production Python trading system
Designed and built independently: real-time API ingestion, ETL with DuckDB and Pandas, fault-tolerant handling of rate limits and bad data, ML models and a live dashboard. Runs every trading day. The logging, validation, and error handling in it go into every client build.
See the case on Fiverr ↗509-day market study (a receipt of rigor, not a service)
509 trading days of morning-pattern statistics, quoted net of spread with the measurement convention named. Nothing trading-related is for sale here; it exists so you can see how claims get checked before they get shipped. The same discipline runs the ledger above.
The lab behind the numbers Study summary in the lab notes ↓#8595;Small-seller operations, in a real niche
For a fragrance decanting seller: an order-turnover sheet that runs the weekly operation, and a shipping SOP checked line by line against USPS Publication 52 hazmat rules. Small store, real constraints, paperwork that survives contact with a carrier counter.
Ask for the walkthrough ↓Reel Intelligence lab // free findings
What 5,127 reels actually say
A local pipeline (transcription + OCR + structured extraction, $0/month) distilled 4,563 of 5,127 collected reels into hooks, pain points, and CTAs. Likes follow a power law: median 305, top 1% over 43,000. This lab publishes the pattern checks for free.
Findings below ↓Three checks that held up
Hooks under 40 characters average roughly 2,509 likes vs 1,493 for longer ones. Explicit money claims underperform at 0.46x the median. Asking viewers to comment a keyword lifts engagement about 1.43x — published here as a finding, not sold as an automation service.
Want this run on your niche? ↓A paid "what to post Monday" briefing gets built only if readers ask for it. Until then the lab is proof, not a product.
How an engagement runs
Honest scoping first
Describe the task as you do it now. I tell you plainly what can be automated or improved, what can't, and what it costs. If an existing tool beats a custom build, I say so.
Built against real data
Everything is tested against your actual files and samples, not a demo case. The habits from running a production trading system, logging, error handling, validation, go into every job.
You own the result
Delivery includes documentation written for a non-programmer. No black boxes and no dependence on me to keep it running.
Start with the task, not a sales call
Work runs on fixed-scope tickets, handled asynchronously around a working schedule — no retainers, no standing availability promises. Send five lines and you get a plain answer on fit, scope, and price: (1) platform or system, (2) link or files, (3) what is broken or missing, (4) what "done" looks like, (5) your deadline if you have one. No pitch deck, no discovery-call runaround.
Start a ticket by emailOrder on Fiverr ↗Prefer a platform escrow and reviews? The same services run on Fiverr under Verlogix Operations.