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Kumora

Layer 1 of 3 · Data Intelligence

Before you use AI, make sure you can trust your data.

Kumora analyzes your catalog, finds quality issues and turns inconsistent data into information ready for search, compatibility and AI-assisted selling.

Analyze my catalogSee the demo

An AI is only as reliable as the data it queries.Kumora first checks the data that will feed its recommendations.

Catalog health
Illustrative example

Your catalog today

18,420products

But also

  • 384possible duplicates
  • 1,240products without a category
  • 612products without a brand
  • 92products with an invalid price
  • 146inconsistent units of measure
  • 350products missing critical attributes
  • 26incomplete compatibility relationships

Catalog Health Score

Weighted average of six dimensions

82/ 100

Kumora turns abstract data quality problems into actionable metrics.

  • Completeness78
  • Consistency91
  • Uniqueness86
  • Validity89
  • Freshness74
  • Relationships68

AI Catalog Readiness

Is your catalog ready for an AI to sell correctly?

69%

ReadyTo fix

  • Complete attributes: To fix
  • Consistent categories: To fix
  • Unique SKUs: Ready
  • Normalized units: To fix
  • Valid prices: Ready
  • Up-to-date stock: To fix
  • Product relationships: To fix
  • Compatibility: To fix
  • Traceability: Ready

Before putting an agent on top of poor data, Kumora shows what needs fixing.

Data Profiling

products analyzed
18,420
attributes
47
sources
6
findings
3,814

Trusted data. Trusted recommendations.

  • Fewer errors

    Keeps inconsistent data out of the sales process.

  • Better search

    Normalized products produce better results.

  • More reliable AI

    The AI works on structured, validated information.

  • Faster implementation

    Finds problems before they block projects.

  • Less manual work

    Automates checks that usually live in spreadsheets.

  • Governance

    Know who changed what, and why.

Detection and normalization

Detect before you sell. Normalize without losing control.

Kumora finds problems before they reach a customer and proposes fixes your team reviews.

What Kumora detects

  • Duplicates

    Identical or potentially equivalent products.

    • HP LaserJet M404DN
    • Laserjet M404 DN HP
    • HP M404DN
    Possible duplicate

  • Incomplete data

    Missing critical attributes.

    • Voltage: —
    • Power: —
    • Model: ABC-500
    2 required attributes missing

  • Inconsistencies

    The same value written in different ways.

    • Negro
    • Black
    • BK
    • NGR
    Suggested normalization: Black

  • Invalid values

    Data outside its type or range.

    • Price = 0
    • Stock = -4
    • Weight = "N/A"
    Invalid data detected

  • Broken relationships

    References to records that don't exist.

    • Accessory → missing product
    Invalid reference

  • Incomplete compatibility

    Not enough data to validate a fit.

    • Model: Mazda CX-5
    • Year: —
    • Engine: —
    Compatibility not sufficiently defined

Normalize without losing control

Kumora proposes. Your team decides.

  1. Original data
  2. Kumora analyzes
  3. Detects anomalies
  4. Suggests normalization
  5. 5Human review
  6. 6Approved data

Examples

Original · Vehicle model

  • Mazda CX5
  • Mazda CX-5
  • MAZDA CX 5

Kumora suggests

3 variants of the same model

Mazda CX-5

Pending review

Impact

Before

3 different models reach the compatibility engine

After

1 normalized identifier to validate compatibility

AI where it helps to interpret. Rules where precision matters.

AI can suggest a fix. Rules decide whether the data meets the standard.

AI · Suggests

  • Detect equivalent names
  • Suggest categories
  • Harmonize descriptions
  • Infer candidate attributes
  • Detect possible duplicates
  • Suggest taxonomies

Rules · Control

  • SKU format
  • Required fields
  • Allowed units
  • Data types
  • Valid ranges
  • Relationships
  • Uniqueness
  • Publishing policies

Data governance

Normalize once. Monitor always.

Kumora can analyze data through files, APIs or read-only connections. Fixes are reviewed before they are applied.

Connect your data

Read-only
  • Excel / CSV
  • ERP
  • PIM
  • Database
  • REST API
  • Supplier catalog
  1. Read-only ingestion
  2. Kumora Data Intelligence
  3. Suggestions
  4. Approval
  5. Publish / Export

Fixes aren't written straight into your production systems: they are reviewed, approved and then published or exported.

Data lineage

ABC-1042

Source
ERP
Last update
Today, 08:32
Transformation
“24v” → “24 V”
Approved by
Admin user

Every critical field keeps its source, transformation and approval.

Continuous monitoring

Data quality doesn't end after implementation.

  • 47 new products detected

    13 have incomplete attributes.

  • 22 new possible duplicates

    Review recommended.

  • “Voltage” attribute coverage

    Decline detected

    92%76%

  • Stock

    Source not updated for 18 hours.

Security principles

  • Read-only access
  • Protected credentials
  • Data isolated per customer
  • Audit trail
  • Access control

Platform design principles. The scope of each control is defined during implementation.

Entry diagnosis

Kumora Catalog Assessment

Connect a sample of your catalog and get a data quality and AI Readiness diagnosis before implementing Kumora.

You receive

  • Catalog Health Score
  • Duplicates
  • Missing fields
  • Inconsistencies
  • Critical attributes
  • Relationships
  • AI Readiness
  • Recommendations
Evaluate my catalog

Try Kumora with a real request.

With your products, your rules and the requests that slow your team down today.

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