Revenue Operations · Product Data Intelligence · v0.1.0-preview
Manufacturer Intelligence connects approved manufacturer sources, datasheets, and optional AI-assisted extraction to enrich industrial catalogs without merging variants or publishing unsupported specifications.
Production Patch Preview · Available through selected implementation engagements
Patch status
The private v0.1 patch is installable and reversible. It is not a claim of broad connector coverage or unattended catalog publication.
Industrial product data is spread across official product pages, datasheets, package drawings, lifecycle notices, and customer-owned systems. Similar names do not make orderable variants interchangeable.
The safest unit of work is a governed manufacturer connector applied to a bounded cohort, with exact manufacturer and MPN identity preserved for every candidate field.
Connector architecture
One approved manufacturer policy can support a bounded product cohort while every field still resolves back to an exact product and its evidence.
Product capabilities
Manufacturer-level reuse improves research efficiency. Exact-MPN validation and field-level evidence keep the result defensible.
Manufacturer, tenant, and the complete orderable part number remain the product identity. Package, carrier, grade, and suffix variants are never silently merged.
A governed connector can resolve approved product pages, documents, images, and package evidence across a selected manufacturer cohort.
Technical values retain their source, document location, qualifier, extraction method, and review state rather than becoming unattributed copy.
Each candidate field can be inspected with its identity, provenance, confidence, conflict state, and revalidation date.
Customer-approved models may assist discovery, classification, extraction, and reviewer summaries. The deterministic system works without AI.
Only approved fields enter a simulated, reversible publication batch. Connector output and model output cannot publish directly.
The module can run with AI disabled. When a customer-approved model is enabled, its suggestions remain visibly separate from deterministic identity, official evidence, reviewer decisions, and publication state.
Models may help rank candidates, classify documents, interpret tables, normalize units, explain conflicts, or summarize a review queue. They may not invent specifications, infer compliance, merge variants, declare evidence verified, or publish fields.
Non-negotiable controls
Source rights, identity, tenant ownership, review, and runtime safety are part of the product contract.
A product field retains its tenant, manufacturer, exact MPN, raw and normalized value, qualifier, approved source reference, document location, connector version, extraction method, confidence, conflict state, reviewer state, publication state, and revalidation date.
This makes missing evidence visible and lets teams roll back a field without replacing the catalog or deleting its history. Customer source locations, access methods, credentials, and connector internals remain private.
Candidate facts enter a review queue with their evidence and conflicts visible. Approved changes are assembled into an explicit publication batch that can be simulated, audited, and reversed.
Connectors and models cannot write directly into the public catalog. Ownership stays with the customer's existing catalog, PIM, ERP, or commerce system.
The preview is informed by ELVN Studio's work with B2B product catalogs where technical identity, evidence quality, search behavior, and commercial workflows meet.
Customer data, source locations, catalog counts, connector internals, and operational results are intentionally excluded from this public preview.
Delivery may use versioned APIs, controlled files, durable queues, or customer-specific adapters after permissions, ownership, retries, and reconciliation are defined.
Typical engagements
Ranges are one-time assessment or implementation fees. Hosting, connector maintenance, refresh frequency, AI usage, review users, and support are scoped separately.
Manufacturer and source audit, catalog coverage, connector feasibility, data-quality gaps, and an implementation roadmap.
Three to five manufacturers, bounded SKU cohorts, source policies, extraction, review workflow, and publication simulation.
Ten to thirty connectors, catalog integration, controlled publication, reporting, and optional customer-approved AI.
Private cloud or self-hosted deployment, custom manufacturers, PIM/ERP integration, tenant policy, support, and SLA.
Unlimited enrichment is not offered: source rights, exact identity, review capacity, refresh obligations, and publication risk must remain measurable.
FAQ
No. It is a source-policy-aware product intelligence system. Each connector is bounded to approved sources and fails closed when access or usage rights are unclear.
No. The complete exact MPN remains part of product identity. Normalization can assist search, but it cannot replace identity or collapse orderable variants.
No. Deterministic connectors, evidence storage, review, and publication control work with AI disabled.
No. AI suggestions remain a separate inference class. Verified source evidence and an approved review workflow control publication.
Yes, after an adapter and ownership boundary are defined. The module is designed to complement the current catalog, ERP, PIM, or commerce platform rather than silently replace it.
A Manufacturer Data Assessment identifies the highest-value manufacturers, source permissions, exact-identity risks, schema gaps, and the smallest viable connector pilot.
Revenue operations assessment
Document the current RFQ path, identify where demand disappears, and define a controlled implementation with measurable acceptance criteria.