Haratres Lists an AI Search Engine for SAP Commerce Cloud

Haratres Teknoloji, a Turkish SAP Commerce Cloud consultancy, has published its Wawlabs AI search engine on the SAP Store as an extension for SAP Commerce Cloud. The product targets a familiar retail problem: shoppers who cannot find the item they came for. Haratres organizes the tool around three levers that shape on-site search, namely relevance, product ranking, and personalization. The listing positions Wawlabs to work with SAP Commerce Cloud, SAP Commerce, and SAP Integration Suite without requiring them, which places it among the partner extensions that add specialized capability to SAP’s commerce platform.

How the Engine Reads Queries, Ranks Results, and Personalizes

Wawlabs applies natural language processing to the query before any results are ranked. The engine corrects typos, resolves synonyms, and handles transliteration, so a shopper who spells a brand phonetically or mistypes a product name still reaches the right listing. It supports description-based search, matching intent expressed in plain language rather than exact catalog terms, and it suggests similar products even when the searched item sits outside the active catalog. The SAP Store page describes the same engine as language independent, a design choice that matters for retailers serving several markets from one storefront.

Ranking is where the tool asks a merchandiser to hand off routine control. Rather than ordering results by hand, the engine sets listing and ranking against stated commercial targets such as revenue, unit volume, or stock position, and it holds search relevance steady while doing so. The system also learns during operation, acquiring synonyms and equivalents with limited manual input, and it can align placement with paid channels so advertised products surface higher. Personalization runs at the visitor level. The engine calculates profile parameters for each shopper, including within a single session, and adjusts listings to that profile while preserving relevance and the merchant’s strategy.

On-site search and merchandising rules are usually maintained by e-commerce or IT staff who curate synonyms, write boost rules, and tune facet logic. An engine that learns equivalents and reorders results toward a business target moves that tuning from people to a model. The team’s job shifts from writing rules to watching outcomes, which changes what it monitors rather than removing the work.

Where Wawlabs Fits in the SAP Commerce Stack

The SAP Store lists Wawlabs as an extension in the Commerce category, aimed at retail, consumer products, wholesale distribution, and sports and entertainment. It connects to SAP cloud solutions through standard SAP-approved interfaces and APIs, and it ships as a software-as-a-service subscription. The engine runs as a cloud service alongside the storefront rather than as installed code inside it. The page records a stated operational service level above 99.5 percent, internationalization for global customers, and configurable authentication policies, the kind of technical attributes procurement teams check before adding a partner app.

Pricing scales with search volume. Haratres offers a 14-day free version covering language-independent search and automated ranking, a Starter Edition at EUR 1,000 per month plus a EUR 2,000 setup fee for 500,000 to one million requests a month on a six-month minimum, and an Enterprise Solution priced on request above one million requests. Tiering cost to query traffic gives a buyer a rough way to model spend against storefront scale.

Haratres brings SAP Commerce Cloud consulting experience to the product, with delivery, development, and functional teams that have supported commerce projects across industries. That consulting base is the context for Wawlabs. A search add-on published by an implementation partner arrives with the delivery capacity to integrate it, a different proposition from a standalone tool for teams weighing who will connect the extension to their catalog and channel data.

What This Means for SAPinsiders

• Model-driven ranking changes what merchandisers govern. Teams that hand result ordering to an engine tuned on revenue or stock targets shift from writing boost rules to overseeing outcomes. Governance moves toward confirming that automated ranking still serves both customers and margin.
• Volume-based pricing rewards accurate traffic forecasting. Because tiers scale with monthly request counts, buyers should size their search volume before committing. A gap between forecast and actual query load can push a storefront into a higher tier or an enterprise negotiation sooner than planned.
• Partner-published add-ons narrow the integration gap. An extension delivered by a SAP Commerce consultancy pairs the tool with people who can connect it. Buyers can weigh product fit and integration capacity together, which affects how they scope internal versus partner effort.