Your catalog runs to thousands of SKUs with thin or inconsistent data
Thousands of SKUs sit with thin, inconsistent product data. Buyers cannot find them, agents cannot select them, and your team cannot fix them by hand. Elogic Commerce builds AI enrichment pipelines that draft attributes, descriptions, and translations from supplier sources. Humans approve. Then it publishes.
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 catalog enrichment?
AI catalog enrichment turns thin, inconsistent product data into complete, structured records: attributes, descriptions, and specs extracted from supplier sources and normalized to your taxonomy. Elogic Commerce runs enrichment as a grounded pipeline with human approval, because catalog data feeds search, AI answers, and shopping agents downstream.
Product data is the foundation everything else stands on. Search quality, AI answers, shopping agents, and marketplace listings all read the same attributes. Manual enrichment costs minutes per SKU and never catches up. Elogic Commerce grounds the AI in your supplier sources and your PIM, so drafts are verifiable instead of invented, and your team reviews instead of writes. Enriched records feed directly into AI search and the storefront answers that AI agents read.
In brief
- Elogic Commerce enrichment is supplier-grounded: every attribute traces to a source, nothing is invented
- Human approval gates every record before it reaches your live catalog
- Enriched data compounds: it powers AI search, chatbots, shopping agents, and GEO visibility
What the pipeline does
A production data pipeline with AI inside, not a copywriting toy. Built on your PIM, governed by your rules, measured by approval rates.
Attribute extraction from supplier sources
Reads datasheets, spec PDFs, and supplier feeds. Extracts structured attributes with the source reference attached, so reviewers can verify in one click.
Spec normalization
Units, terminology, and value formats are normalized to your taxonomy. ‘250bar’, ’25 MPa’, and ‘3625 psi’ become one clean attribute.
Descriptions in your brand voice
Product copy drafted from verified attributes, in your tone, at your length. Style rules are configured once and enforced on every SKU.
Translations at scale
Every market gets native-quality product content with consistent terminology, driven by your glossary. New market launches stop waiting on content.
Structured for search, SEO, and agents
Output lands as clean attributes and schema-ready data. The same enrichment lifts site search, Google visibility, AI answers, and shopping agents.
Human review workflow
A review workspace with confidence scores, batch approval, and QA sampling. Your team stays the editor in chief, at a fraction of the effort.
Review, not rewrite
The workspace your catalog team will use. Every draft shows its supplier source and confidence score. High-confidence batches approve in bulk. Anything uncertain waits for a person.
- • Every extracted value links back to its supplier source
- • Confidence scores route drafts to bulk approval or manual review
- • Nothing publishes to the PIM without human sign-off
Manual enrichment vs AI writer vs grounded pipeline
Three ways to fill a catalog. Only one is both fast and safe, and the difference is where the facts come from.
| Manual enrichment | Generic AI writing tool | Grounded pipeline (this) | |
|---|---|---|---|
| Speed | Minutes per SKU, forever | Fast | Fast, in governed batches |
| Source of facts | Whatever the person finds | The model’s guess | Supplier documents, with the source attached |
| Consistency | Varies by person and day | Varies by prompt | Enforced taxonomy, units, and brand voice |
| Invented specs | Rare but possible | A real and known risk | Blocked: no source, no claim |
| Publish control | Person by person | Usually none | Human approval and QA sampling before publish |
| Scales to | Hundreds of SKUs | Text, not data | Full catalogs and continuous supplier updates |
How many SKUs are holding you back?
Bring a sample of your thinnest product data to the discovery call. We show you what a pilot batch would look like, enriched.
Grounded in your PIM, not in thin air
Generic AI writing tools invent. This pipeline extracts. Elogic Commerce has built PIM and ERP data flows since 2009, and the enrichment engine plugs into that same infrastructure with full traceability.
• Drafts are grounded in supplier documents and existing data. No source, no claim.
• Your taxonomy, attribute model, and channel requirements drive the output structure.
• Delivered as your pipeline and your IP. No per-SKU software licensing from Elogic Commerce.
• ISO 27001 and SOC 2 Type II controls cover supplier and product data handling.
PIM and data sources
- Akeneo
- Pimcore
- inriver
- Supplier PDFs & feeds
- DAM
- ERP item masters
Commerce platforms
- Adobe Commerce (Magento)
- Shopify Plus
- BigCommerce
- Salesforce Commerce Cloud
- commercetools
- Shopware
AI models
- OpenAI
- Anthropic Claude
- Google Gemini
- Open-source (Llama, Mistral
Where enrichment pays off first
Four programs we scope most often. Each starts with a measured pilot batch on your real data.
01
Supplier range onboarding
A new supplier range of thousands of SKUs goes live in days instead of months. Extraction, normalization, review, publish.
02
Marketplace and channel expansion
Each channel gets content in its required structure and length, generated from one verified attribute set. One source of truth, many feeds.
03
New market localization
Full catalog translation with your terminology, reviewed by market. Launch content stops being the critical path.
04
Findability foundation
Complete attributes make site search, filters, AI answers, and shopping agents work. Enrichment is the first step of every AI roadmap.
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
Data audit
We measure attribute completeness, source coverage, and content gaps across your catalog. You see the baseline in numbers.
• STEP 2
Taxonomy and rules
We configure your attribute model, units, brand voice, and glossary. The rules that every draft must obey.
• STEP 3
Pilot batch with QA calibration
500 to 1,000 SKUs enriched and reviewed. We measure approval rates and tune extraction until quality holds.
• STEP 4
Scale runs
The full backlog processes in governed batches. Your team approves. Dashboards track completeness climbing.
• STEP 5
Continuous pipeline
New SKUs and supplier updates flow through automatically. Enrichment becomes infrastructure, not a project.
Built on proven B2B delivery
Enrichment quality depends on data engineering discipline. This is the record Elogic Commerce brings to it.
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 | Typical timeline | Investment | Best for |
|---|---|---|---|
| Data audit | 1-2 weeks | Scoped after discovery | A measured baseline of catalog completeness and a prioritized enrichment plan |
| Pilot batch | 2-4 weeks | Scoped after discovery | 500 to 1,000 SKUs enriched with QA calibration and approval-rate reporting |
| Full program & pipeline | 1-3 months, then continuous | Scoped after discovery | The complete backlog plus an always-on pipeline for new SKUs |
Start with a pilot batch
A 500 to 1,000 SKU pilot calibrates quality on your real catalog before you commit to the full program.
Is this the right fit?
Strongest fit when
Supplier data exists in PDFs, feeds, or spreadsheets that nobody has time to process
You run a PIM (Akeneo, Pimcore, inriver) or plan to introduce one
You sell in several languages or channels with different content requirements
Search, AI visibility, or agent readiness projects are blocked by data quality
Better fit elsewhere if
Your catalog is small enough to maintain by hand.
You have no supplier sources or existing data to ground the AI in. We can help you collect them first.
You want fully automatic publishing with no human review. We do not build that, on purpose.
Industries we build AI Catalog Enrichment for Ecommerce
Let’s talk
Start with the data audit. In 1-2 weeks you know exactly how complete your catalog is and what a pilot batch will prove. 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 catalog enrichment: frequently asked questions
By grounding and review. Every extracted value must trace to a supplier source, and the reviewer sees that source next to the draft. Low-confidence drafts route to manual review. Nothing publishes without human approval, and QA sampling continuously measures approval rates.
It depends on source quality, attribute depth, and languages. The pilot batch establishes your real per-SKU economics before you commit to the full program. As a rule, the pipeline costs a fraction of manual enrichment and gets cheaper as it scales.
All the languages you sell in. Translations use your glossary and terminology rules, and each market can have its own reviewer in the approval workflow.
Writing tools generate text from a prompt and hope. This is a data pipeline: extraction from verified sources, normalization to your taxonomy, drafting under your rules, human approval, and publication into your PIM with full traceability. It is engineered for catalogs, not blog posts.
Thin, duplicated content hurts SEO regardless of who wrote it. Unique, accurate, well-structured content helps, regardless of who drafted it. Search engines evaluate quality, not authorship method. Our pipeline produces verified attributes and unique descriptions, which is exactly what search and AI systems reward.
A PIM is the natural home for enriched data, and we work with Akeneo, Pimcore, and inriver. If you do not run one yet, we can enrich into your platform’s native catalog or introduce a PIM as part of the program. The audit tells you which path fits.
Yes. The pipeline can draft image alt texts, extract data from image-based datasheets, and structure rich content blocks. Scope depends on your channels, and the audit defines it.
Related AI solutions from Elogic Commerce
AI Search
Hybrid semantic search that finds the right part in large, spec-heavy catalogs.
Agentic Commerce
Strategy, protocols, and measurement for commerce where AI agents are the buyer.
AI Agents
The gateway build that makes your store readable and transactable for AI agents, with guardrails.
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 catalog enrichment practice turns this data engineering depth into content velocity: supplier-grounded AI drafting with human approval, feeding PIM, search, and agent channels from one verified source. 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.