AI case study

Indexing massive digital libraries

by
Tune AI
Context

Tune AI is a fine-tuning and deployment platform assisting large enterprises. For an information services enterprise, Tune AI selected Meta's Llama 3 to index a massive 7B+ page digital library, reducing costs from $18 to $0.60 per document and processing time from 1 hour to 7 minutes, achieving a 99% accuracy rate, significantly enhancing efficiency and cost-effectiveness.

Results

Reduced costs from $18 to $0.60 per document, reduced processing time from 1 hour to 7 minutes, achieved a 99% accuracy rate.

Results not reported in the source
Region
Global
Published
April 1, 2024
Agent type
Data Agents
AI provider
Meta
Models/tools
Not disclosed
ICE score
288
The ICE framework in this database provides a quick way to assess the feasibility and potential impact of AI use cases, with higher scores signaling more actionable opportunities.

Impact: Potential benefits to the business.

Confidence: Likelihood of achieving expected results.

Ease: Simplicity of implementation in terms of resources and time.

ICE Score: Calculated by multiplying the component scores.

Note:
Each score is AI-generated based on available data and should be viewed merely as a general guideline for deeper exploration of the use cases.
Impact
9
Confidence
8
Ease
4

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Thoughts & ideas