Pricing engine
Disivo · Rails · PostgreSQL · Elasticsearch · Redis
Built and maintain the core of Disivo's dynamic pricing platform: the engine that turns competitor data into prices for online stores.
~⅓of Czech e-commerce
~/about $ cat plan_a.txt
Senior full-stack engineer. I build pricing engines, scrapers and LLM pipelines in Ruby on Rails. 6+ years in production, working remote from Czechia. Senior full-stack engineer. Pricing engines, scrapers and LLM pipelines in Ruby on Rails. Remote from Czechia.
01 — what happened
Pro hockey until 28. Two years selling cars at a Škoda dealership. Then I taught myself to code, and since 2019 I've been building the core of Disivo, a dynamic pricing SaaS. Pro hockey until 28. Two years selling cars. Self-taught into code, building Disivo's pricing engine since 2019.
## [3.0.0] — 2019 → now
### Added
- software_engineer
- ship_to_production
## [2.0.0] — 2017–2019
### Changed
- hockey → selling Škodas
## [1.0.0] — until 28
### Deprecated
- play_pro_hockey
[3.0.0] 2019 → now
Added software_engineer
[2.0.0] 2017–2019
Changed hockey → selling Škodas
[1.0.0] until 28
Deprecated play_pro_hockey
02 — somewhere else
Problem, what I built, what changed. Production systems at Disivo, plus the iOS apps I build on my own.
Disivo · Rails · PostgreSQL · Elasticsearch · Redis
Built and maintain the core of Disivo's dynamic pricing platform: the engine that turns competitor data into prices for online stores.
~⅓of Czech e-commerce
Disivo · HTTP + parsing · proxies
Headless rendering was slow and expensive. I built a scraper API on plain HTTP requests and parsing, running in production next to the old system, so a new source goes live fast.
0headless browsers
Disivo · LLM extraction · RAG
A production LLM extraction pipeline, not a demo. Next-generation scraping of a million products per day.
1Mproducts per day
03 — what I do
I like building web apps that stay clean as they grow, and putting LLMs to work on real business problems. Six years in production taught me to own things end to end, from the scraper that collects the data to the price that goes live.
full-stack
Ruby on Rails, Hotwire, PostgreSQL, Elasticsearch, Redis. Boring tech, used well.
genai & llms
Extraction pipelines, RAG and LLM features that run in production behind a queue, not in a notebook.
data at scale
Large-scale scraping and pricing data for roughly a third of Czech e-commerce.
versatility
Hockey, then sales, then software. After that, picking up a new stack is the easy part.