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Text & NLP Annotation

High-quality linguistic data processing for chatbots, sentiment analysis, and search.

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Input Text:

Apple Inc.ORG: 99.8% plans to open a new office in San FranciscoLOC: 98.2% by late 2024DATE: 99.5%.
Output PreviewJSON
[
  {
    "text": "Apple Inc. plans to open...",
    "entities": [
      {
        "start": 0,
        "end": 10,
        "label": "ORG",
        "text": "Apple Inc."
      },
      {
        "start": 28,
        "end": 41,
        "label": "LOC",
        "text": "San Francisco"
      }
    ],
    "sentiment": "neutral"
  }
]

Capabilities & Features

  • Named Entity Recognition (NER)Enterprise grade precision
  • Sentiment AnalysisEnterprise grade precision
  • Intent ClassificationEnterprise grade precision
  • OCR CorrectionEnterprise grade precision

Quality Assurance Process

Every project undergoes a rigorous 3-step verification process: Annotator Self-Check, Senior Reviewer Audit, and Automated Consistency Checks. We guarantee 98%+ accuracy on Golden Sets.

Start a Pilot
How it flows

Text & NLP Annotation, as one route

From your raw data to a model-ready dataset, with quality gates you can audit.

  1. Business problem

    Your raw data

    Text & NLP

  2. VentureSoft thinking

    Guidelines and gold set

    Agreed with your ML team

  3. Technology

    Named Entity Recognition (NER)

  4. Technology

    Sentiment Analysis

  5. Technology

    Three-tier QA

    100% review, golden-set audit

  6. Measured outcome

    Model-ready dataset

    Delivered in your format

Where it is usedCustomer support automationDocument processingCompliance reviewSearch and recommendation
Engagement

From pilot to production

  1. 1
    Phase 1

    Sample and scope

    Send a sample; we return a labeled subset with QA metrics.

  2. 2
    Phase 2

    Guidelines

    Schema, edge cases, and acceptance criteria versioned together.

  3. 3
    Phase 3

    Pilot batch

    Up to 5,000 annotations through the three-tier workflow.

  4. 4
    Phase 4

    Scale

    Dedicated pod with API delivery into your pipeline.

  5. 5
    Destination

    Continuous improvement

    Gold sets and guidelines evolve with your model.

Start with a pilot dataset

Send us a sample and we will return annotated data with QA metrics within days.