Case Studies

Real enterprise AI programs. Measurable outcomes.

See how Argos Data helps AI teams move from pilot to production through custom tooling, vetted expertise, and quality-governed workflows.

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8 of 8 case studies

Capability

Industry

Modality

50%

Boost in Productivity

98% Fewer Quality Issues
90% Reduction in Project Backlog
2 Weeks to Deploy Custom Tooling

Annotation Technology Multimodal

Optimizing Multimodal LLMs With a Custom Annotation Tool

Argos Data built a custom multimodal annotation tool in two weeks, helping a global technology provider cut quality issues by 98% and reduce project backlog by 90% across 4,000+ image conversation threads.

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80%

Backlog Cleared in 3 Months

50% Efficiency Boost on Critical Tasks
30% Cost Savings Through Resource Optimization

Annotation Technology Mixed

How an Enterprise AI Leader Cleared 80% of Its Backlog in Three Months

An enterprise AI leader cleared 80% of a stalled annotation backlog in three months — through tiered expertise matching, embedded real-time QA, and elastic workforce scaling.

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4,000+

Videos Annotated

100% Video Metadata Integrity Maintained
Scalable Across Linguists and Languages

Annotation Technology Video

Automated Video Annotation for Conversational AI Training

Argos Data built a centralized video annotation environment with automated quality enforcement — preserving 100% metadata integrity across 4,000+ videos for a leading AI team's conversational training program.

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70,000

Annotations Managed

10–12 Quality Checks Per Task
Single-Environment Evaluation at Scale

Model Evaluation Technology Text

Automated Response Evaluation at Large-Scale AI Training Volume

Argos Data built a unified LLM evaluation environment that managed 70,000 long-form prompt-response annotations with 10–12 embedded quality checks per task — without fragmenting reviewer workflows.

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73%

Decrease in Per-File Processing Time

275% More Files Processed Daily
6,000+ Files Annotated Across 4 Language Combinations

Model Evaluation E-Commerce Text

Streamlining Multilingual LLM Quality Evaluation Across Four Languages

Argos Data built a custom MQM evaluation tool for a global online retailer's AI division — cutting per-file processing time by 73% and increasing daily file throughput by 275% across four language pairs.

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Billions

of Live Product Listings on the Platform

Hundreds of Millions of Active Buyers Worldwide
Argos Data's Position as the Marketplace's Preferred Annotation Supplier

Annotation E-Commerce Text

Mass Annotation of Billions of Product Listings — Without the Inconsistencies

A global e-commerce marketplace's previous annotation vendor fell short on the complexity of billions of live product listings. Argos Data adapted its tooling, introduced QA modules to catch labeling inconsistencies — and became the marketplace's preferred annotation supplier.

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5 Task Categories

Evaluated

4 LLMs Compared in a Unified Environment
3 Difficulty Levels Calibrated Through Cross-Model Performance

Prompt Engineering E-Commerce Text

Structured Prompt Engineering and Multi-Model LLM Evaluation for E-Commerce

Argos Data built a unified prompt engineering and multi-model evaluation environment — enabling structured stress-testing of e-commerce interaction understanding across four LLMs without workflow fragmentation.

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5 Languages

Evaluated (Hindi, Japanese, Korean, Brazilian Portuguese, Simplified Chinese)

2–3 Minutes Average Task Time (vs. 5–7 Minutes Estimated Without Tooling)
Zero Ingestion Backlog

RLHF and Human Feedback Technology Audio

Multilingual Spoken Agent Evaluation at Scale — With Zero Backlog

Argos Data deployed an automated three-pass-plus-adjudication pipeline for multilingual spoken agent evaluation across five languages — cutting per-task time by more than half while maintaining zero ingestion backlog.

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Have an AI data challenge that looks like one of these? Let's talk through it.

Talk to an expert Explore our solutions

Real enterprise AI programs. Measurable outcomes.

Optimizing Multimodal LLMs With a Custom Annotation Tool

How an Enterprise AI Leader Cleared 80% of Its Backlog in Three Months

Automated Video Annotation for Conversational AI Training

Automated Response Evaluation at Large-Scale AI Training Volume

Streamlining Multilingual LLM Quality Evaluation Across Four Languages

Mass Annotation of Billions of Product Listings — Without the Inconsistencies

Structured Prompt Engineering and Multi-Model LLM Evaluation for E-Commerce

Multilingual Spoken Agent Evaluation at Scale — With Zero Backlog

Have an AI data challenge that looks like one of these? Let's talk through it.