How is this different from Algolia or built-in platform search?

Algolia is strong on speed and typo tolerance; we are stronger on semantic understanding and per-category intent mapping. Built-in search is usually term-match; ours uses embeddings to understand related concepts your customers actually search for.

What conversion lift should I expect?

Stores with poor existing search typically see search-to-purchase conversion improve by 30 to 60 percent. Stores already on a strong search product see 10 to 20 percent. Realistic target shared after the discovery audit.

Will it work with my filters and facets?

Yes. Existing facets are preserved. New facets we recommend (smart filters based on intent) get added if your merchandising team approves.

How does personalisation work?

Anonymous customers get session-based personalisation (what they have viewed in this visit). Signed-in customers get full personalisation based on consented browse and purchase history. We do not personalise on data we do not have consent for.

Can the merchandising team override results?

Yes. Curated rankings, pinned results, banned terms, redirect rules all manageable from a merchandising console. Your team operates the search day-to-day without us.

AI Automation for Ecommerce

AI Smarte Suche

AI smarte Suche für E-Commerce. Semantisches Verständnis, Tippfehler-Toleranz, facettierte Filterung, personalisierte Rangfolge.

Timeline: 4 to 8 weeks
Search: Semantic + typo-tolerant
Ranking: Personalised
Measurement: Pre + post conversion

Overview

What it is.

AI-gestützte smarte Suche für E-Commerce-Kataloge. Semantisches Verständnis (Synonyme, verwandte Konzepte), Tippfehler-Toleranz, facettierte Filterung und personalisierte Rangfolge basierend auf Browsing- und Kaufhistorie. Ersetzt Marktplatz-Such-Apps, die bei Long-Tail-Abfragen schlechte Ergebnisse liefern. Die meisten Implementierungen werden innerhalb von 4 bis 8 Wochen bereitgestellt, abhängig von der Kataloggröße und der Integrationskomplexität. Messbarer Anstieg der Such-zu-Kauf-Konversion innerhalb von 30 Tagen.

What you get

Deliverables.

Search index built and tuned to your catalogue
Storefront search widget on your platform
Faceted filtering interface
Personalised ranking based on browse and purchase history
Synonym and intent mapping tuned per category
Analytics dashboard for query terms and conversion
Pre and post conversion baseline
Operations runbook for your merchandising team

Honest limits

What it isn't.

Catalogue data quality directly affects search quality
Personalised ranking needs consented behavioural data
Very small catalogues may not benefit over standard platform search

Not sure if it's a fit? Email us. We'll tell you straight.

How it works

From inquiry to handover.

01
1. Discovery
Audit current search analytics. Confirm catalogue size, structure and personalisation goals.
02
2. Index
Search index built. Synonyms and intent mappings tuned per category.
03
3. Storefront
Search widget deployed. Personalisation rules wired up.
04
4. Measurement
Pre-engagement baseline captured. Post-launch 30-day delta reported.

Frequently asked

Questions we get from people about to book.

How is this different from Algolia or built-in platform search?: Algolia is strong on speed and typo tolerance; we are stronger on semantic understanding and per-category intent mapping. Built-in search is usually term-match; ours uses embeddings to understand related concepts your customers actually search for.
What conversion lift should I expect?: Stores with poor existing search typically see search-to-purchase conversion improve by 30 to 60 percent. Stores already on a strong search product see 10 to 20 percent. Realistic target shared after the discovery audit.
Will it work with my filters and facets?: Yes. Existing facets are preserved. New facets we recommend (smart filters based on intent) get added if your merchandising team approves.
How does personalisation work?: Anonymous customers get session-based personalisation (what they have viewed in this visit). Signed-in customers get full personalisation based on consented browse and purchase history. We do not personalise on data we do not have consent for.
Can the merchandising team override results?: Yes. Curated rankings, pinned results, banned terms, redirect rules all manageable from a merchandising console. Your team operates the search day-to-day without us.

Ready to start?

Tell us about your project.

A member of our team reads every message. Replies within one business day. No sales call required.

See all services

AI Automation for Ecommerce

AI Smarte Suche

AI smarte Suche für E-Commerce. Semantisches Verständnis, Tippfehler-Toleranz, facettierte Filterung, personalisierte Rangfolge.

Timeline: 4 to 8 weeks
Search: Semantic + typo-tolerant
Ranking: Personalised
Measurement: Pre + post conversion

Overview

What it is.

What you get

Deliverables.

Search index built and tuned to your catalogue
Storefront search widget on your platform
Faceted filtering interface
Personalised ranking based on browse and purchase history
Synonym and intent mapping tuned per category
Analytics dashboard for query terms and conversion
Pre and post conversion baseline
Operations runbook for your merchandising team

Honest limits

What it isn't.

Catalogue data quality directly affects search quality
Personalised ranking needs consented behavioural data
Very small catalogues may not benefit over standard platform search

Not sure if it's a fit? Email us. We'll tell you straight.

How it works

From inquiry to handover.

01
1. Discovery
Audit current search analytics. Confirm catalogue size, structure and personalisation goals.
02
2. Index
Search index built. Synonyms and intent mappings tuned per category.
03
3. Storefront
Search widget deployed. Personalisation rules wired up.
04
4. Measurement
Pre-engagement baseline captured. Post-launch 30-day delta reported.

Frequently asked

Questions we get from people about to book.

How is this different from Algolia or built-in platform search?: Algolia is strong on speed and typo tolerance; we are stronger on semantic understanding and per-category intent mapping. Built-in search is usually term-match; ours uses embeddings to understand related concepts your customers actually search for.
What conversion lift should I expect?: Stores with poor existing search typically see search-to-purchase conversion improve by 30 to 60 percent. Stores already on a strong search product see 10 to 20 percent. Realistic target shared after the discovery audit.
Will it work with my filters and facets?: Yes. Existing facets are preserved. New facets we recommend (smart filters based on intent) get added if your merchandising team approves.
How does personalisation work?: Anonymous customers get session-based personalisation (what they have viewed in this visit). Signed-in customers get full personalisation based on consented browse and purchase history. We do not personalise on data we do not have consent for.
Can the merchandising team override results?: Yes. Curated rankings, pinned results, banned terms, redirect rules all manageable from a merchandising console. Your team operates the search day-to-day without us.

Ready to start?

Tell us about your project.

A member of our team reads every message. Replies within one business day. No sales call required.

See all services

AI Smarte Suche

What it is.

Deliverables.

What it isn't.

From inquiry to handover.

1. Discovery

2. Index

3. Storefront

4. Measurement

Questions we get from people about to book.

Tell us about your project.

AI Smarte Suche

What it is.

Deliverables.

What it isn't.

From inquiry to handover.

1. Discovery

2. Index

3. Storefront

4. Measurement

Questions we get from people about to book.

Tell us about your project.