Your catalog is large or spec-heavy: thousands to hundreds of thousands of SKUs
Buyers search with part numbers, technical specs, and plain language. Standard site search fails on all three. Elogic Commerce builds AI-powered ecommerce search that understands what buyers mean and returns the right products from large, complex catalogs.
17+ years in ecommerce, since 2009
200+ commerce specialists
NPS 70 post-launch client surveys
5.0 on Clutch 64 verified reviews
Trusted partnerships
Trusted by ecommerce teams
★★★★★ 5.0 · 64 reviews on Clutch
Mid-market & enterprise · EU & US
ISO 9001 and 27001-aligned processes for quality and security management.
What is AI search for ecommerce?
AI search for ecommerce understands what a buyer means, not just the words typed: synonyms, specs, part numbers, and intent. Elogic Commerce implements AI-powered search grounded in enriched catalog data, so industrial and B2B queries like a spec or an application return the right SKU with account-correct pricing.
This matters most in B2B. Catalogs run to 100,000+ SKUs. Buyers search for ‘2 inch stainless fitting rated 300 psi’ or paste a competitor’s part number. Keyword search returns zero results, and the buyer calls sales or leaves. AI site search closes that gap, but only when it is built on clean spec data from your PIM and ERP. Elogic Commerce engineers both the search and the data pipeline that feeds it. Weak results usually trace to weak data: our AI catalog enrichment service fixes relevance at the source.
In brief
- Elogic Commerce implements AI search that resolves specs, synonyms, and part numbers to the right SKU
- Relevance is fixed at the data layer with enriched catalog records, not patched with rules
- Results respect account pricing and availability, measured by search conversion before and after
What you get
A complete AI search solution for ecommerce: the engine, the data pipeline, and the controls your merchandising team needs.
Hybrid semantic search
Vector search for meaning, keyword search for precision, combined and re-ranked. Buyers find products even when their words never appear on the page.
Part number and cross-reference search
Exact and fuzzy matching on SKUs, manufacturer part numbers, and competitor references. Industrial buyers find the right part on the first try.
Natural-language filters
Queries such as ‘show 24V motors under 2 kg in stock’ become structured filters automatically. No filter tree required.
Zero-results rescue
When nothing matches exactly, the engine suggests the closest products and alternatives instead of a dead end. Every search leads somewhere.
Merchandising controls
Your team pins, boosts, and buries results by rule, margin, or stock. AI ranks, people decide. No black box.
Search analytics
Dashboards for top queries, zero-result rates, and search-to-order conversion. You see what buyers want and what the catalog is missing.
From plain language to the right part
The engine parses the query into structured filters, matches meaning as well as keywords, and ranks by relevance and availability. Near misses become suggestions instead of dead ends.
- • Natural-language queries become precise spec filters automatically
- • Semantic matching finds products the exact words would miss
- • Zero-result searches return the closest viable alternatives
Keyword search vs AI-powered search
Where standard site search stops and hybrid AI search continues. This is the gap your zero-result rate is measuring.
| Standard keyword search | AI-powered hybrid search | |
|---|---|---|
| Understands | Exact words on the page | Meaning, synonyms, and intent |
| Typos and variants | Often fail | Handled by fuzzy and semantic matching |
| Part numbers | Exact match only | Exact, fuzzy, and competitor cross-references |
| Natural-language queries | Return noise or nothing | Parsed into spec filters automatically |
| Zero results | A dead end | Closest viable alternatives, ranked |
| Improvement loop | Improvement loop | Improvement loop |
What does failed search cost you?
Bring your search logs to the discovery call. We estimate the revenue hiding behind your zero-result rate.
Connected to the systems you already run
Search engines fail when specs are incomplete or inconsistent. Elogic Commerce fixes the pipeline first: attributes from ERP and PIM, normalized units, and enriched content, then builds the search on top.
• Platform-neutral: we implement and extend leading search platforms, or build custom when your catalog demands it.
• Real-time stock and price sync from your ERP, so results reflect what buyers can actually order.
• Works with headless and classic storefronts alike.
• Multi-language search across all your markets.
Commerce platforms
- Adobe Commerce (Magento)
- Shopify Plus
- BigCommerce
- Salesforce Commerce Cloud
- commercetools
- Shopware
Data sources
- Akeneo
- Pimcore
- inriver
- SAP S/4HANA
- NetSuite
- Microsoft Dynamics 365
- Visma
Search technology
- Vector search
- OpenSearch / Elasticsearch
- Algolia
- Klevu
- Custom RAG
Where AI product discovery pays off
Four patterns we build most often, in B2B and large-catalog B2C.
01
Industrial distributor search
Part-number search with cross-references from competitor SKUs, plus spec filters. Engineers and buyers self-serve instead of calling sales.
02
Spec-based discovery for engineers
Search by requirement, not product name: dimensions, ratings, materials, standards. The engine reads the spec sheet so the buyer does not have to
03
Large-catalog B2C search
Natural-language queries across tens of thousands of products, with synonyms, typo tolerance, and personalized ranking.
04
Internal search for sales reps
Reps quoting on the phone find the right product and price in seconds. Faster quotes win more orders.
How we deliver
Structured, governed, accountable. The same delivery framework behind every Elogic Commerce build: defined discovery, milestone delivery, RACI accountability, and transparent reporting.
• STEP 1
Search audit
We analyze your current queries, zero-result rate, and conversion. We quantify what failed search costs you today.
• STEP 2
Data pipeline
We connect and clean product data from PIM and ERP: attributes, units, availability. The single highest-leverage step.
• STEP 3
Engine build
Hybrid search implementation, ranking tuned to your catalog, and merchandising controls for your team.
• STEP 4
Launch and A/B test
We ship behind an A/B test and measure search conversion, zero-result rate, and revenue per search session against the old engine.
• STEP 5
Run and improve
Continuous relevance tuning, new synonyms from real queries, and a monthly review of the numbers.
Built on proven B2B delivery
Search is only as strong as the catalog, ERP sync, and storefront under it. This is the engineering record Elogic Commerce builds on.
5×
Faster order approvals after digitizing B2B workflows on Adobe Commerce
40% fewer manual orders
B2B self-service portal with SAP S/4HANA and PIM integration
3+ years
Engagement period
+31%
Increase in checkout conversion rate
-65%
-65%
+$1.1M
New B2B revenue stream unlocked in year 1
2.5 months to launch
B2B wholesale portal for 1,400+ wholesale users, live and transacting
Engagement at a glance
Every engagement is scoped after discovery, with milestone-based billing and transparent change management. No surprises.
| Engagement | 2-3 weeks | Scoped after discovery | A quantified report on search failure cost and a prioritized improvement plan |
|---|---|---|---|
| Search audit | 2-3 weeks | Scoped after discovery | A quantified report on search failure cost and a prioritized improvement plan |
| AI search build | 2-4 months | Scoped after discovery | Full hybrid search with data pipeline, merchandising controls, and A/B-tested launch |
| Managed relevance | SLA-backed retainer | Scoped after discovery | Ongoing tuning, analytics, and catalog data improvements after launch |
Fix findability where it pays
A 30-minute call scopes the audit and the fastest path to a measurable lift in search conversion.
Is this the right fit?
Strongest fit when
Buyers search by part number, application, or technical requirement
Your zero-result rate is high, or search converts worse than browsing
Product data lives in a PIM or ERP and can feed the engine
You sell across several languages or markets
Better fit elsewhere if
Your catalog is small and simple. Your platform’s built-in search is likely enough, and we will say so.
You need marketing SEO, not site search. See our GEO and AI visibility services instead.
You cannot access or export your product data at all yet.
Industries we build AI Search
Let’s talk
Start with a search audit. In 2-3 weeks we quantify what failed search costs you and show the fastest path to fix it. Tell us what you are working on. We come back with an honest assessment of fit, scope, and timeline.
Ready to chat?
Send your requirements. We reply within one working day with a fit assessment and next steps.
AI ecommerce search: frequently asked questions
Normal search matches keywords. AI search understands intent. It uses vector embeddings to match meaning, so ‘waterproof winter boots’ finds products described as ‘insulated snow footwear’. It also handles misspellings, synonyms, part numbers, and full questions, and it learns from behavior.
Complete product attributes, specs with normalized units, availability, and pricing, ideally from a PIM and ERP. If the data is weak, we fix the pipeline first. Search built on bad data disappoints no matter the engine.
Yes. Queries such as ‘which pump fits a 3-phase 400V supply’ are parsed into intent and filters, and answered with matching products. On product pages, the same engine can power a Q&A assistant grounded in your spec sheets.
A search audit takes 2-3 weeks. A full build with data pipeline, engine, and A/B-tested launch typically takes 2-4 months, depending on catalog size and data quality.
It depends on your catalog. Platforms such as Algolia or Klevu are strong for standard catalogs, and we implement and extend them. Very large, spec-driven B2B catalogs often need custom pipelines and ranking on top, or a custom engine. Elogic Commerce is neutral: the recommendation follows your catalog, integrations, and total cost of ownership.
We launch behind an A/B test and track four numbers: search-to-order conversion, zero-result rate, revenue per search session, and search exit rate. You see the lift, or the honest absence of one, in the dashboard.
Yes, and this is where Elogic Commerce is strongest. Results respect each account’s catalog visibility, contract prices, and stock rules, synced from your ERP.
Related AI solutions from Elogic Commerce
Catalog Enrichment
Supplier-grounded product data enrichment at scale, with human approval before publish.
AI Chatbots
ERP-grounded chatbots that answer with live orders, stock, and each account’s contract prices.
AI Personalization
Predictive reordering and account-based recommendations built on ERP order history.
About Elogic Commerce
Elogic Commerce is an ecommerce development company founded in 2009, headquartered in Tallinn, Estonia, with offices in Prague, Munich, Dresden, Amsterdam, Stockholm, London, New York. With 200+ commerce engineers, architects, and project managers, Elogic Commerce specializes in complex B2B and B2B2C ecommerce, enterprise replatforming, ERP, CRM, and PIM integration, and long-term managed support across Adobe Commerce (Magento), Shopify Plus, BigCommerce, Salesforce Commerce Cloud, and commercetools.
The AI search practice extends this base: semantic product discovery engineered on clean PIM and ERP data, for catalogs where findability drives revenue. Elogic Commerce holds ISO 27001 · SOC 2 Type II · ISO 9001 certifications, an NPS of 70, and a 5.0 rating on Clutch. The company is a certified Adobe Commerce Silver Solution Partner, Shopify Plus Partner, Certified Bigcommerce Partner, Salesforce Select Partner, and Hyvä Bronze Partner, and received the Magento Community Engineering Award at Adobe Imagine 2019.