Skip to content
Rivac AI Labs
Data Intelligence, Document AI & Applied Analytics

Data Labeling & Annotation for ML Training

No machine learning model is better than the training data it's built on, and most teams either burn far too many hours labeling data by hand or trust fully automated labeling that quietly bakes errors into the model from day one. This service builds a hybrid labeling pipeline that uses AI to handle the volume (pre-labeling, flagging low-confidence cases) while your subject-matter experts or our trained annotators review and correct the cases that actually need a human judgment call, producing training data that's both fast to generate and genuinely high quality. We set up the labeling guidelines, inter-annotator agreement checks, and quality sampling so you can trust the resulting dataset rather than hoping it's good enough. It solves the real chokepoint in most ML projects: not the model architecture, but the unglamorous work of getting enough correctly labeled data to train it on.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

  1. 1

    Define labeling guidelines and taxonomy with your team, including the edge cases that cause the most disagreement

  2. 2

    Pilot the hybrid AI-plus-human labeling workflow on a sample batch, measuring inter-annotator agreement and AI pre-label accuracy

  3. 3

    Review disagreement cases and guideline gaps with your team, refining instructions before scaling to full volume

  4. 4

    Run the labeling pipeline at production volume with ongoing quality sampling and a feedback loop to catch guideline drift

What You Get

Deliverables from this engagement

  • Labeled training dataset at agreed volume and quality bar
  • Documented labeling guidelines and taxonomy
  • Inter-annotator agreement and quality-sampling reports
  • Repeatable labeling workflow for ongoing or future training runs

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Data Intake

  • Raw Unlabeled Dataset
  • Sample Batch Selected
  • Basic AI Pre Labeler

Annotation

  • Manual Review Spreadsheet
  • Annotator Corrections

Quality Check

  • Spot Check Sample
  • Guideline Adjustment

Output

  • Labeled Dataset Export
  • Delivered to ML Team
  • Training Run Feedback
  • Guideline Doc Updated
  • Next Batch Prepared

Cloud-Native / Managed Services Architecture

Managed Storage Layer

  • Cloud Storage for Raw Assets
  • Managed Pre Labeling Model API
  • Managed Labeling Platform

Task Management Layer

  • Managed Task Queue
  • Annotator Workforce Portal
  • Quality Sampling Service

Export Layer

  • Managed Dataset Versioning
  • Cloud Storage Export Bucket

Operations Layer

  • Model Training Pipeline Trigger
  • Labeling Throughput Dashboard
  • Inter Annotator Agreement Report
  • Automated Backup of Labels

Event-Driven & Automation-First Architecture

Triggers

  • New Data Batch Uploaded
  • Pre Label Job Triggered
  • Annotator Submits Task
  • Agreement Check Triggered

Automation Layer

  • Auto Pre Label Applied
  • Task Auto Assigned to Queue
  • Inter Annotator Agreement Met

Review Gate

  • Auto Flag Disagreement
  • Lead Notified for Adjudication
  • Auto Accepted Label

Publishing

  • Dataset Auto Updated
  • Export Triggered on Quality Pass

Enterprise-Grade & Compliance-Ready Architecture

Governance Layer

  • Annotator Access Control Policy
  • PII Handling Policy
  • Labeling Guidelines Version Control

Secure Annotation Layer

  • Access Controlled Task Assignment
  • Redundant Dataset Versioning
  • Encrypted Raw Asset Storage

Auditable Labeling

  • Every Label Change Logged
  • Quality Sampling Audit
  • Inter Annotator Agreement Records

Compliance Output

  • Compliance Sign Off
  • Certified Training Dataset
  • Retention and Access Review

AI-Augmented, Human-in-the-Loop Architecture

Data Ingestion

  • Unlabeled Training Data
  • AI Pre Labeling Engine

AI Labeling Layer

  • AI Drafted Labels at Volume
  • Confidence Score per Label
  • Confidence Threshold Met

Human in the Loop

  • Annotator Review Queue
  • Inter Annotator Agreement Reached
  • Guideline Clarification Needed
  • Human Approved Label

Delivery

  • Labeled Dataset Assembled
  • Released to ML Training Pipeline
  • Feedback Retrains Pre Labeler

Integration-Heavy / Ecosystem-Connected Architecture

Client Systems

  • Client Data Warehouse
  • Existing Labeling Tool
  • Model Registry

Labeling Core

  • Unified Labeling Pipeline
  • Hybrid AI and Human Labeling Engine
  • Quality Assured Dataset

Two Way Sync Layer

  • Model Training Pipeline Sync
  • CI CD Retraining Trigger
  • ML Engineering Team
  • Automated Model Deployment

Shared Governance

  • Joint Review with ML Team
  • Published Dataset Version

More in Data Intelligence, Document AI & Applied Analytics

Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about Data Labeling & Annotation for ML Training

Most engagements like this start as a $500–$2,500 pilot, see full pricing.

Questions? Book a free call