GCG platform partners / Search & product discovery
HawkSearch implementation.
Help buyers find the right part.
GCG helps manufacturers and distributors implement and improve HawkSearch for technical catalogs. We connect product data, search behavior and storefront interactions so buyers can move from a part number or application to a useful result.
A clearer path to the right product.Platform architecture
Platform + perspective
Built on HawkSearch.
Built around your business.
HawkSearch provides site search and product discovery with relevance tuning, merchandising controls, and recommendations for organizations with complex catalogs.
We connect those capabilities to your customer relationships, product information, and enterprise workflows, with a clear view of what the platform handles and where integration or customization is needed.
Explore HawkSearchIndustrial expertise
Complex environments.
Considered experiences.
For manufacturers
Connect your products
to your channels.
Help engineers, dealers, and service teams discover products and related technical information. We connect catalog attributes, terminology, and product relationships to how buyers actually search.
For distributors
Make every buying
relationship work.
Make supplier-driven catalogs easier to navigate across part numbers, brands, attributes, and varied customer terminology. We focus on useful filters, understandable results, and fewer dead ends.
How we help
From platform potential
to everyday performance.
Search implementation
Integrate HawkSearch into storefront and content experiences with responsive result and filtering interfaces.
Catalog & relevance
Structure searchable fields, normalize attributes, and tune synonyms, ranking, and merchandising rules.
Measurement & refinement
Review search behavior, unsuccessful queries, and product discovery friction to prioritize ongoing improvements.
Inside the implementation
The product can exist and still be hard to find.
A maintenance buyer enters a familiar part number with a space instead of a hyphen. Another buyer starts with a material, size and application. Those searches reveal different needs. We examine the source data, indexed fields, query handling and results experience together, then define a small set of real buying scenarios to evaluate the changes.
Part-number discovery
Review identifiers, aliases and punctuation with the people who know how customers ask for products. Define which exact matches should be clear, which alternatives are appropriate and when the interface should ask for more detail.
Technical navigation
Choose facets and labels from meaningful product attributes. Keep units, category context and available values understandable so a buyer can narrow the catalog without applying incompatible or misleading filters.
Index and storefront alignment
Map the source fields, indexing workflow and display behavior. Coordinate catalog updates, product eligibility and storefront links so a result remains useful after the buyer selects it.
Search quality starts before the query.
A planning model for technical search. Feature configuration, integration and evaluation are scoped to the HawkSearch environment and connected systems.
Scope and delivery
Treat relevance as a business workflow.
Implementation and integration
We define the data contract between the product source, HawkSearch and the website or commerce platform. The work includes the search experience, result presentation and the responsibilities for maintaining the index.
Relevance improvement
We build an evaluation set around representative buying tasks, including known-item searches, technical filters and poor-result cases. We compare behavior against agreed expectations rather than treating a single appealing search as proof that the full catalog works.
Merchandising and governance
Marketing and ecommerce teams need to understand the rules that shape discovery. We document responsibilities for search tuning and product data, and account for how merchandising decisions interact with technical accuracy and product eligibility.
The experience we’re working toward
Search quality begins with product data and buyer intent. A search engine works best when both are translated into a clear discovery model.
Useful conversations
A clear place
to start.
Can you improve an existing HawkSearch implementation?
Yes. We can begin with the current data feed, indexed fields, configuration and a set of searches that are not meeting expectations. That gives the work a concrete starting point and a basis for evaluating improvement.
Is search tuning enough if product data is inconsistent?
It may be only part of the solution. We separate source-data problems from indexing, query and interface problems so each change has an appropriate owner. Consistent identifiers and technical attributes support more dependable discovery.
How do we know a change improved search?
Agree on representative queries and expected outcomes before making changes. Review the returned products, ordering, filters and next action, then combine those checks with available search behavior and business feedback.
Adobe Commerce
Start with the challenge
What could work
better together?
Let’s connect your next ambition to a practical path forward.