Conversant AI

A field report on how distributors are modernizing supplier data at scale

The State of Catalog Operations

In industrial distribution, catalog data sits at the heart of nearly every business process, from purchasing to pricing to ecommerce. Yet it remains one of the least automated functions in the enterprise.

Most catalog teams are stuck in a relentless loop of manual cleanup. Large distributors spend as much as 40% of product management time reconciling supplier data, a hidden tax that can cost millions in lost productivity. Supplier feeds arrive in inconsistent formats (Excel, PDFs, APIs) each with unique quirks. Even trusted vendors change field names or redefine attributes, forcing teams to rebuild mappings and manually validate thousands of SKUs. What should be strategic work becomes a treadmill of rework.

For large distributors, this is a structural bottleneck. A single supplier update can ripple through tens of thousands of SKUs and require days of analyst review. Errors cascade downstream into misclassified products, broken search results, and customer confusion. The result is high operational cost, frustrated teams, and slower time to market.

It is a familiar story across the industry and one that Conversant set out to change.

Product Data Challenges

At Conversant, we've seen this problem play out across distributors managing millions of SKUs and thousands of supplier relationships. Even with disciplined internal processes, manual mapping simply doesn't scale. Supplier data arrives in proprietary formats that must be parsed, validated, and restructured before integration, often taking weeks per cycle, only for new versions to appear days later.

Compounding the challenge is categorical drift, the gradual misalignment of supplier taxonomies and product titles that leads to duplication, misclassification, and inconsistent search results across digital storefronts.

Across the industry, the pain points are consistent:

Constant rework caused by evolving supplier schemas
Limited visibility into data quality and coverage
Dependence on tribal knowledge for attribute mapping and validation
Fragmented data pipelines across disconnected tools

Addressing these challenges requires more than a workflow fix. It requires intelligence that can understand business structure, reason over ambiguous data, and continuously improve.

Conversant's Approach

Conversant's Catalog Agent is an intelligent system designed to address these exact challenges by automating supplier onboarding and catalog harmonization. Instead of imposing rigid workflows, the system learns from an organization's taxonomy, mapping logic, and review patterns, then scales that expertise across suppliers and categories.

The process begins with adaptive ingestion. Conversant's pipelines interpret data from virtually any source including structured feeds, PDFs, and custom exports, and identify schema patterns automatically. Using various search technologies, the system detects relationships between supplier attributes and internal fields, even when terminology differs.

Once ingested, reasoning models and context aware clustering help the Catalog Analyst align attributes, resolve duplicates, and flag anomalies for review. When supplier data is incomplete or contradictory, agentic workflows trigger automated web and document research to verify missing specifications or correct inaccurate values.

A continuous feedback loop connects every action to a growing knowledge model. Each review by a human product manager strengthens the model's future accuracy, creating a compounding asset that evolves with the business.

The result is a continuously learning system that transforms fragmented supplier inputs into clean, structured data ready for downstream systems such as ERP, PIM, and ecommerce platforms.

The goal is not to replace human expertise but to extend it, capturing the logic of experienced catalog managers and applying it at scale.

Results

The early outcomes are clear. Onboarding cycles that once required weeks are now completed in hours. Data consistency across suppliers has improved, and teams are spending more time validating exceptions rather than performing repetitive cleanup.

In one case, discrepancies between supplier feeds surfaced automatically, allowing the client to correct systemic issues before they reached production. In another, auto generated product titles and normalized categories improved customer comprehension and internal analytics.

Each engagement reinforces the same pattern. When data quality rises, every downstream system, from search relevance to quoting accuracy, improves.

The Bigger Picture

These efforts reflect a broader shift toward AI enabled product intelligence. Conversant's technology goes beyond data hygiene. It is about teaching systems to understand products, their relationships, applications, and constraints, so that decisions can be made faster and with greater confidence.

By integrating structured data, unstructured documents, and organizational knowledge into a unified model, Conversant provides a foundation for automation that grows more capable over time. The Catalog Analyst is the first of many intelligent coworkers designed to handle detailed, domain specific work that has traditionally required expert human oversight.

For organizations seeking to modernize catalog management and supplier onboarding, Conversant offers a proven path forward. By pairing domain expertise with adaptive AI systems, we help teams turn fragmented data into a strategic asset, one that grows more valuable with every interaction.

To learn more or compare experiences, contact us at info@conversant.ai.

Sidebar: Agentic Catalog Operations