Your Buyers Can't Buy What They Can't Find. Catalog Intelligence Fixes That.

Buyers search by specification—not by browsing. Catalog Intelligence enriches product data, maps fitment, and powers accurate search, integrated with your ERP.

For distributors and manufacturers with 5,000–500,000+ SKUs.

The Revenue Your Catalog Is Losing — One Failed Search at a Time

Poor product data doesn’t fail loudly — it shows up in call volume, abandoned sessions, and a quarterly review where revenue is down, and nobody knows why.

Buyers Who Search and Leave

43% of B2B buyers abandon an order and call a rep when they can’t find accurate product info — a data problem wearing a search mask.

The 12% Revenue Drain

Gartner puts poor product data at 12% of annual online revenue lost. For a $50M distributor, that’s $6M.

Wrong Parts, Real Consequences

In automotive, HVAC, MRO, and manufacturing, a wrong-part order shuts down a line or triggers a warranty claim — and the customer remembers.

AI Search Can't Fix Bad Data

Only 24% of B2B companies have AI search in production. The blocker isn’t the tech — it’s data quality. One 750,000-SKU distributor needed 30,000 manual rules just to get relevance.

New SKUs Arrive Broken

Products land as spreadsheets and raw supplier dumps. Without a structured pipeline, every new SKU restarts the problem.

The root cause is the same everywhere: catalog data built for ERP transactions, not buyer discovery. Catalog Intelligence rebuilds it for both.

B2B Catalog Intelligence Isn't Retail Feed Cleanup

Retail enrichment optimizes titles and imagery for browsing. B2B runs on different requirements: technical precision, compatibility mapping, part number cross-referencing.

Retail / B2C

What Most B2B Catalogs Have

True B2B Catalog Intelligence

Title/description optimization

Raw supplier data

Buyer-centric attribute schema built around specifications, dimensions, material, certifications, tolerances, and applications

Lifestyle imagery

Inconsistent attribute names across feeds

Normalized taxonomy across suppliers

SEO keyword targeting

Specs buried in free text

Structured technical specifications

Browse-oriented taxonomy

No compatibility mapping beyond category

Fitment mapping using industry standards and application data

Conversion-optimized copy

Missing dimensions, certifications, parameters

Complete attribute enrichment and validation

Small attribute set per SKU

Different supplier formats

Part-number cross-referencing, supersession tracking, and ERP-connected governance

A buyer searching “M12 hex cap screw, A2-70 stainless, 40mm, DIN 933” isn’t browsing — they’re specifying. Your catalog needs to be just as precise.

Two Connected Layers — Each One Required for the Next

Layer 1 — SKU Enrichment

The foundation everything else runs on

Catalog Audit

Attribute completeness, feed quality, inconsistencies, and duplicates mapped into a gap scorecard prioritized by revenue impact.

Attribute Enrichment

Product data is enriched, standardized, and structured using supplier catalogs, PDFs, ERP systems, and technical documentation to create a catalog optimized for buyer search and discovery.

Fitment & Compatibility Mapping

We structure compatibility, industry standards, applications, supersessions, and cross-references so buyers can quickly find the right part.

Ongoing Catalog Intelligence

Every new SKU is enriched before publishing—so your catalog improves continuously instead of degrading over time.

ERP-Connected Governance

Catalog Intelligence stays synced with your ERP while preserving enriched attributes, compatibility data, and commerce-ready content.

Layer 2 —AI-Powered Discovery

The result of layers 1 and 2 makes possible

Hybrid Search

Exact match for part numbers and specs, semantic search for natural language, behavioral learning from real clicks and orders.

B2B Search Behaviors

Part number tolerance, multi-attribute filtering, account-aware results, cross-reference resolution, visual search from a photo.

AI-Powered Chat Assistant:

Buyers describe what they need instead of knowing exact SKUs.

“Replacement pressure valve for a Carrier rooftop unit” “M12 stainless hex cap screw for outdoor use”

The assistant interprets intent, identifies specs, searches the catalog, resolves compatibility, and returns the right products instantly.

It doesn’t generate product data—it uses your structured catalog to find it faster.

Zero-Result Reduction

Failed queries get categorized and fed into a prioritized enrichment backlog.

Platform Implementation

Adobe Commerce/Magento: Adobe Live Search, Algolia, Coveo. BigCommerce: Algolia, Searchanise, Boost. Shopify Plus: Searchie, Boost, SearchPie.

How We Deliver It

Three sequential phases. AI search deployed before enrichment finishes underperforms; fitment built before the schema is set doesn’t align with search filters. Order matters.

1. Catalog Audit & Architecture Design (Weeks 1–3)

Full audit of attribute completeness, supplier data quality, fitment gaps, duplicates, and search baseline. Target architecture designed before any SKU is touched.

2. SKU Enrichment Sprint (Weeks 3–10)

AI enrichment runs on priority segments, reviewed and published in batches with QA. Fitment-dependent, spec-heavy categories get extra validation.

3. Fitment & Compatibility Mapping (Weeks 6–14)

Built in parallel with late-stage enrichment. Automotive data is validated against the VCdb; other verticals get application guides and interchange data structured in.

4. AI Search Configuration & Deployment (Weeks 10–16)

Tool selection, synonym configuration, behavioral learning, account-aware personalization, zero-result monitoring.

5. Ongoing Intelligence Loop (Post-Launch)

Monthly zero-result review, quarterly completeness audit, fitment updates as feeds change, ongoing search performance monitoring.

What Catalog Intelligence Delivers

2–3x Conversion Improvement

Catalogs with 10+ searchable attributes convert 2–2.4x better than lean ones.

Up to 80% Fewer Zero-Result Searches

AI hybrid search recovers queries that used to send buyers to competitors or the phone.

Fewer Wrong-Part Returns

Compatibility mapping and validation reduce the highest-cost failure mode in fitment-dependent verticals.

Lower Sales Call Volume

Self-service search cuts “product information” calls, the category that eats the most rep time for the least revenue.

Agentic Commerce Readiness and Agentic Commerce Optimization (Read about this)

AI buying agents are starting to place B2B orders autonomously, reading specs and checking fitment straight from your catalog. Only structured, standards-compliant data is readable by them. A messy catalog is invisible to human buyers and to the agents starting to represent them.

Built for Industrial B2B

Automotive

Millions of part numbers, ACES/PIES standards, VIN lookup, OEM cross-references, decades of supersession history. Wrong fitment data here costs returns, warranty disputes, and lost accounts.

HVAC & Plumbing

Compatibility across hundreds of equipment model lines, cross-referencing, and spec filtering for pressure ratings, refrigerant compatibility, and pipe sizing.

Manufacturing

Strict tolerance and certification requirements, where one wrong dimensional attribute can cause a production failure.

Wholesale Distribution

High-SKU, multi-category catalogs with high new-SKU velocity — enriched and made self-service viable.

Why DotcomWeavers?
Because Your Competitors Don’t Want You Here.

Your Strategic Partner in Enterprise Commerce

We bring together deep technical & business expertise, industry knowledge, and a collaborative approach to each client

Since

2007

Digital experience experts

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Markets globally

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Projects Built to Last

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Industries transformed

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Platforms / Technology Partnerships

We provide end-to-end ecommerce consulting services, Magento/commerce platform implementation, growth marketing, and managed services to deliver ongoing results.

FAQs

Clarifying Common Queries About Our Process & Solutions

We already have a PIM. Isn't our data fine?

A PIM organizes whatever you put into it. If the underlying data is incomplete, it’s managing a well-organized quality problem, not solving it. We enrich the data, then connect to your PIM as the governance layer.

Is enrichment feasible at 200,000+ SKUs?

Yes — that’s where AI enrichment has its biggest advantage, processing 6x faster than manual work. We prioritize by revenue impact and search volume so ROI shows up early.

How is this different from a one-time data cleanup?

Cleanup fixes today’s problem. Catalog Intelligence adds an enrichment pipeline for new SKUs, ERP-connected governance, zero-result monitoring, and fitment management — a living system, not a sprint.

Do you handle ACES/PIES for automotive?

Yes — VIN/YMM search, OEM cross-reference, supersession tracking, and VCdb-validated fitment, connected to your ERP’s data model.