AI-Powered Catalog Intelligence

Your product data exists. Getting it to every channel — correctly — is the problem.

DataForge sits upstream of your distribution channels and continuously produces channel-ready product data from your internal sources. One normalized layer. Every format. Every partner. Automatically.

Request a free data audit See how it works
ACES/PIES Compliance Automation · Vendor Scorecard · Smart Sheet Enrichment · Closed-Loop Evaluation · Multi-Channel Distribution · Confidence Scoring · Human-in-the-Loop Governance · Category Intelligence · A/B Content Testing · Real-Time KPI Reporting ·
ACES/PIES Compliance Automation · Vendor Scorecard · Smart Sheet Enrichment · Closed-Loop Evaluation · Multi-Channel Distribution · Confidence Scoring · Human-in-the-Loop Governance · Category Intelligence · A/B Content Testing · Real-Time KPI Reporting ·
The Problem

Bad data at the source.
Compounding damage downstream.

Every channel has different requirements. Every manual translation is a liability. The further your product data travels from its source, the less reliable it becomes.

01
Data leaves your organization in six different formats
WDs want spreadsheets. Amazon needs its schema. JEGS has its own template. A new distributor onboards — the same process restarts. Every export is manual, error-prone, and out-of-date the moment it ships.
02
One-time cleanup projects don't compound — they decay
A cleanup produces a snapshot. Three months later a product line updates. A marketplace changes its schema. The catalog has drifted again. The value doesn't accumulate. The overhead of the next cleanup is exactly the same as the first.
03
AI initiatives stall before they start
Conversational commerce, intelligent search, AI-assisted configurators — all require governed, structured, attribute-rich product data. Without the upstream layer, AI applications can't deliver. You have a data problem before you have an AI problem.
How It Works

A continuous data refinement layer.
Not a one-time project.

DataForge ingests your internal data, normalizes it against every channel schema you need, and distributes channel-ready output automatically — while learning and improving with every cycle.

📥
Ingest
Any Source
ERP exports, PIM data, PDFs, spec sheets, manufacturer portals, vendor feeds
⚙️
Normalize
Single Layer
Cleaned, enriched, validated. Attributes scored per-field and per-record
🔍
Evaluate
Confidence Scoring
High → auto-publish. Medium → human review. Low → flag and route
📤
Distribute
Every Channel
ACES/PIES, marketplace schemas, distributor templates, API feeds — automatically
🔄
Learn
Closed Loop
Every output measured. Every cycle raises the automation threshold. HITL need reduces over time
Built for Two Audiences

Same engine. Two sides
of the supply chain.

Whether you're receiving product data from vendors or sending it to channels, the problem has the same root cause: no normalized layer between the source and the destination.

Your vendor data arrives incomplete, inconsistent, and immediately out of date.

You depend on hundreds of vendors to keep your catalog accurate. They don't share your standards, your schemas, or your urgency. DataForge normalizes the data coming in — regardless of source format — and keeps it current automatically.

  • Vendor data ingested in any format — structured feeds, spreadsheets, PDFs, manufacturer portals
  • Normalized against your category taxonomies and attribute schemas automatically
  • Smart Sheet records augment your existing product numbers — no disruption to your PIM
  • Search performance, conversion rates, and return reduction tracked per vendor, per category
  • Vendor scorecards surface which suppliers are creating catalog liability — and where
  • A/B testing infrastructure determines which attribute completeness drives conversion
  • Category Manager dashboards updated in real time as new vendor data flows in
Vendor Quality Scorecard (sample)
Vendor Completeness Score
Vendor A
92%
Vendor B
67%
Vendor C
31%
Vendor D
88%

Scores update continuously as new data flows in.

Your product data is accurate internally. Getting it to channels — correctly, at scale — is the bottleneck.

Your ERP has the specs. Your engineering team knows the fitment data. But every channel partner needs it in a different format, and maintaining consistency across six channel types manually is a structural problem, not an operational one.

  • Single normalized layer from your ERP, PIM, and spec sheets — one source of truth
  • ACES/PIES compliance automated — fitment accuracy at scale without manual re-entry
  • Channel-ready output for every partner: WDs, Amazon, JEGS/Summit, DTC, international
  • When internal specs update, all channels update automatically — no manual sync
  • Reseller governance: which partners are representing your products incorrectly
  • New channel partner onboarding time drops from weeks to days
  • AI-ready product infrastructure for conversational commerce and intelligent search
Channel Distribution Architecture
Internal Sources
ERP System
Product PIM
Spec Sheets & PDFs
↓
DataForge Normalization LayerSingle source · Confidence scored · HITL governed
↓
Channel-Ready Output
ACES/PIES
Amazon
WDs / Jobbers
JEGS / Summit
DTC / eComm
International
Results

What changes when the
data layer is right.

55%
Faster production throughput
AI-augmented data pipelines measurably accelerate experienced teams — GitHub Copilot trial benchmark on structured production tasks.
4–8wk
Pilot to first measurable results
Fixed-fee, defined-deliverable pilots against your real data. Proof before broad deployment — no open-ended commitments.
→0
Manual re-entry per new channel
Once the normalization layer is live, adding a new channel partner means adding a schema target — not restarting a manual export workflow.
The Closed-Loop Methodology

The difference between deploying AI
and operating AI.

Every cycle is measured. Every measurement informs the next iteration. The system improves continuously — the human review dependency reduces as confidence thresholds rise.

01
Goal Definition
Align on what "better" means: completeness rates, ACES/PIES compliance, search lift, onboarding time, return reduction.
02
Metric Design
Instrument the pipeline to measure what was defined. If it isn't measured, it doesn't improve.
03
Build & Deploy
DataForge layer goes live against your data. Human review is high at start — thresholds are conservative by design.
04
Measure & Analyze
Outputs evaluated against defined metrics. Each cycle produces a quantified delta. Confidence thresholds rise with evidence.
05
Improve & Iterate
Schemas updated, enrichment logic refined, automation frontier advanced. Each cycle is faster than the previous.
Free · No commitment · Results in one session

See exactly where your
product data is breaking down.

We run a focused data audit against a sample of your catalog — vendor feeds, channel exports, or internal product records. You get a clear picture of where the gaps are and what it would take to close them.

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