A pricing system that reprices 200 products in one upload

The problem

SinaTools sells hundreds of product lines whose prices move with the exchange rate and raw-material costs. Every price change meant editing pages one by one, and the published price list was often out of date.

Their old site had also been attacked, and it needed to stay reachable both inside Iran and for suppliers abroad.

On the buying side, suppliers in China raised prices citing material costs, and SinaTools had no independent way to check whether the increase was fair.

What we built

A trilingual catalogue

223 product, size-table and guide pages in Persian and English, plus a Chinese landing page for suppliers. It is served from a host inside Iran for local customers and a global mirror for everyone else, with visitors routed automatically.

A pricing CMS

The team uploads one master price spreadsheet. The system validates it, updates every product page and regenerates the price-list PDF in one go. A spot-checker lets them search any stock code and correct a single price inline. The admin works in Persian and English.

Automatic publishing

Every night, price changes made in Iran sync to the global site without anyone touching it.

A cost index for negotiations

A public, bilingual chart tracks the input costs behind each product family: tungsten, cobalt, molybdenum, steel, abrasive grain and resin, with freight and currency shown separately. It runs from a May 2021 baseline and is updated monthly from published market data.

A quote estimator

Give it a past supplier quote and it estimates what today’s fair price should be, given how the inputs have moved. It turns “your prices went up 12%” into a conversation backed by numbers.

The result

Price changes in minutes, not days. One upload reprices the whole catalogue and the price list.

Always current, everywhere. The Iranian and global sites stay in step automatically each night.

Negotiations backed by data. On its first real test, a September 2026 supplier quote, the estimator predicted a 1.9% rise; the actual like-for-like rise was 3.0%. The model was then recalibrated on that quote.