Karg

Autonomous Data Cleaning Infrastructure

Karg uses layered AI and rule systems to standardize messy spreadsheets and databases, without requiring data engineers or complex pipelines.

What Karg Does

The problem is simple. The mess is not.

Every team working with tabular data encounters the same failures: inconsistent entries, duplicate records, broken formats, and ambiguous values. Karg combines deterministic rules with AI reasoning to produce reliable, transparent cleaning decisions.

Karg Runtime Preview

Live Processing

Records Processed

4.2M

Auto Resolution

97.6%

Review Queue

2.4%

Schema InferenceStable
Entity ResolutionRunning
Confidence RoutingReady
Audit ExportEnabled

09:42:11 Detected duplicate entities in provider table.

09:42:19 Applied rule layer canonical_company_name.

09:42:21 Routed 218 rows to human review at < 0.80 confidence.

Architecture

Multi-layer cleaning from raw data to clean dataset

Karg flows messy inputs through profiling, deterministic rules, layered AI reasoning, and human review to deliver trustworthy output.

  1. Raw Data

    Messy spreadsheets, CSV exports, and database dumps enter the cleaning run.

    Stage Details

    • Typical inputs include survey exports, CRM dumps, vendor CSV files, and manual spreadsheets.
    • Rows can contain inconsistent categories, typos, and mixed formatting patterns.

  2. Profiling Engine

    Karg profiles columns, detects anomalies, and maps likely data types before cleanup.

    Stage Details

    • Detects column intent (state, phone, date, company name) and outlier values.
    • Builds a quality profile to route each field through the right cleaning path.

  3. Rule Engine

    Deterministic rules fix format errors, validate values, and apply fast standardizations.

    Stage Details

    • Applies known mappings and formatting constraints for consistent outputs.
    • Examples: CA -> California, TX -> Texas, ny -> New York.

  4. Specialized ML Models

    A targeted family of machine learning models governing intent, routing, and safety.

    Stage Details

    • Powers intent classification, transformation ranking, and risk assessment for auto-applying changes.
    • Works in sequence to optimize retrieval, measure confidence, and prioritize the review queue.

  5. Quick AI Reasoning Layer

    Fast model reasoning resolves common semantic mismatches that rules alone cannot catch.

    Stage Details

    • Resolves fuzzy variants and abbreviations at high speed for common edge cases.
    • Examples: Calif -> California, Cali -> California.

  6. Advanced AI Reasoning Layer

    Deeper reasoning handles harder edge cases and ambiguous records with confidence scoring.

    Stage Details

    • Handles ambiguous values with stronger context and confidence evaluation.
    • Example: Nwe York -> New York with confidence scoring and traceable rationale.

  7. Human Review Queue

    Low-confidence records are routed to your team with full context for fast decisions.

    Stage Details

    • Each flagged row includes reason codes, confidence, and suggested normalized value.
    • Teams can approve, override, or add rules that improve future runs.

  8. Clean Dataset

    You export standardized, trustworthy data ready for analysis, dashboards, and models.

    Stage Details

    • Output is normalized, deduplicated, and enriched with confidence + audit metadata.
    • Ready for BI dashboards, analytics workflows, and downstream modeling.

Dataset Transformation

Watch messy rows transform into clean records

Run a realistic multi-column cleaning workflow, inspect detected issues, and trace field-by-field decisions from rules to AI.

Dataset preview

3,482

Rows

12

Columns

217

Issues detected

Sample window below shows 5 representative rows from a larger run.

Detected problems

  • State variations7 variations
  • Company duplicates3 clusters
  • Email typos2 records
  • Phone format inconsistencies4 rows

Column profiling

  • State - Geographic region

    12 unique values, expected domain: US states

  • Phone - Phone number

    5 format variations detected

  • Company - Organization entity

    3 dedupe clusters identified

  • Email - Email address

    2 domain/local-part anomalies

Messy DatasetClean Dataset
NameEmailCompanyStatePhoneNameEmailCompanyStatePhoneDecision
John Smithjsmith@gmail.conACME IncCA(415)555-1212..................
J. Smithjohn.smith@gmail.comAcme IncorporatedCalif4155551212..................
Maria Lopezmaria@globex.coGlobexNwe York212-555-9912..................
Mike Chenmchen@initech.ioInitechTX(713) 555 2281..................
Alicia Gomezagomez@northwind,comNorthwind LimitedCA - USA+1 310 555 7777..................

Explainability

Active row: John Smith

Rule layer handles deterministic normalization and routes unresolved semantic fields to AI review.

Latest transformation event: Name

  • Email

    AI review

    jsmith@gmail.con -> AI review

    Domain typo is detected, but deterministic validation cannot safely infer the canonical mailbox. Confidence: 57%.

    Regex validation failed -> Escalated to AI review queue

  • State

    Rule decision

    CA -> California

    Two-letter abbreviation matched deterministic state dictionary. Confidence: 99%.

    Rule abbreviation match -> Applied canonical state name

  • Phone

    Rule decision

    (415)555-1212 -> +1-415-555-1212

    US phone parser normalized punctuation and added country code. Confidence: 98%.

    Digit extraction -> Formatted to E.164 display

Transformation history

  1. Column profiling confirmed person/company/state/phone schema
  2. Deterministic mappings normalized state and phone
  3. Ambiguous email correction escalated to AI review

Pattern discovery

  • ACME identity cluster detected across rows 1, 2, and 5.
  • Email typo pattern (.con) appears against validated gmail.com frequency.
  • US phone normalization template applied with country code inference.

Cleaning results

0%

Rows standardized

0

Records deduplicated

0

Fields corrected

0

Needs AI review

Common Data Issues

Compact proof of recurring messy-data failures

Quick examples below reinforce the full transformation simulation above without duplicating its detailed explainability.

Inconsistent State Names

Geo normalization

CA | Calif | CA - USA

->

California

Rule + AI layers unify mixed location tokens.

Duplicate Customers

Entity resolution

John Smith | J. Smith

->

John Smith

Identity variants collapse into one canonical record.

Broken Phone Numbers

Format standardization

5551234567 | (555)123-4567

->

+1-555-123-4567

Deterministic formatting yields analysis-ready phone fields.

Mixed Date Formats

Date harmonization

03/02/24 | March 2 2024

->

2024-03-02

Multiple date styles normalize to ISO output.

Ready to modernize your data pipeline?

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