actowiz

  Client Snapshot A B2B SaaS startup building a price-monitoring product for mid-market retailers. They owned the application and billing; they needed the data engine underneath it. The Challenge The founders had built a working single-tenant prototype that scraped [VERIFY: 3] sites. It broke roughly [VERIFY: weekly]. Scaling it to a paying multi-tenant product surfaced problems the prototype had never faced: Each tenant wanted different sites, different SKUs , different check frequencies Anti-bot systems began blocking as volume rose One tenant's heavy schedule could starve another's Every site layout change meant a silent data gap nobody noticed until a customer complained Objectives A data layer supporting [VERIFY: 7–10] retail sources with per-tenant configuration Scheduled collection with per-tenant isolation and fair scheduling Anti-bot resilience with graceful degradation, not silent failure Historical price storage and change-based alerting Health monitoring that catch...

  Client Snapshot A US specialty retailer moving off BigCommerce onto a headless stack. The Challenge The platform's native export produced a flat product list, but the client's catalog depended on variants — size, colour and configuration combinations, each with its own SKU , price and stock level. The export flattened these, and a flattened variant structure is not a catalog, it's a mess. 1,000 products expanded to ~4,300 variants. Category hierarchy and SEO metadata (which the client did not want to lose rankings over) were also absent from the export. Objectives Extract all products with their full variant matrices intact Preserve category hierarchy and SEO metadata (URLs, meta titles, meta descriptions) Deliver in the new platform's import schema The Actowiz Approach We extracted at the variant level rather than the product level, reconstructing the parent-child relationship explicitly so that the new platform received a proper product-with-variants structure rathe...

  Client Snapshot A UK D2C personal-care brand also sold through 6 third-party retailers. The Challenge The brand's products regularly went out of stock at retailer level — and nobody told them. A retailer showing "out of stock" on a hero SKU is lost revenue and, worse, lost search ranking on that retailer's internal search, which is slow to recover. They were finding out days later, usually from a customer complaint. There was also a second, quieter problem: unauthorized sellers listing their products, which they had no systematic way to detect. Objectives Monitor availability of 180 SKUs across 6 retailers Alert within 1 hour of a stock status change Flag any seller not on the authorized list The Actowiz Approach Availability sounds simple and isn't: retailers signal stock state in wildly inconsistent ways — an explicit badge on one site, a disabled add-to-cart button on another, a silently missing size option on a third. We built per-retailer availability det...

  Introduction The grocery retail industry is becoming increasingly competitive, with weekly promotions and Buy One Get One (BOGO) offers playing a major role in influencing consumer purchasing decisions. To remain competitive, brands require real-time visibility into promotional campaigns, discount structures, product availability, and competitor pricing across multiple grocery retailers. Manual monitoring often results in delayed insights, inconsistent data, and missed opportunities to optimize promotional strategies. Actowiz Solutions partnered with a leading grocery brand to automate Scarpe Weekly BOGO Deals from Grocery Stores using a scalable Web scraping API . The solution enabled continuous collection of promotional information, including BOGO offers, limited-time discounts, pricing updates, coupon campaigns, and featured products from major grocery chains. By transforming raw promotional data into structured, analytics-ready datasets, the client gained faster access to act...