Personalization Engines

The Future of GenAI Fine-Tuning

Fine-tuning large language models (LLMs) for specific tasks has become a critical challenge.

Crossing Minds' RAG-Sys offers a groundbreaking approach that transforms how businesses leverage and customize GenAI technologies.

Your ultimate LLM fine tuning tool.

Unparalleled Adaptability

Instantly switch between diverse domains without retraining.

RAGSys allows effortless adaptation to legal, ecommerce, or technical fields by simply changing the knowledge base.

Maximize versatility while saving time and resources.

Real-Time Knowledge Integration

Stay cutting-edge with immediate knowledge updates.

RAGSys integrates new information in real-time, ensuring your LLM always leverages the latest data.

Deliver relevant, up-to-date responses in fast-paced environments.

Precision and Transparency Redefined

Achieve new levels of accuracy and accountability. RAGSys grounds responses in factual information, dramatically reducing hallucinations.

With clear source attribution, it offers unmatched transparency crucial for high-stakes industries.

Unmatched Efficiency and Scalability

Transform the economics of LLM deployment. RAGSys focuses on efficient retrieval rather than constant retraining, significantly reducing costs and environmental impact.

Scale seamlessly to handle growing data volumes without proportional resource increases.

Dynamic Knowledge Integration Without Retraining

Traditional fine-tuning requires extensive retraining to incorporate new knowledge, a process that's both time-consuming and computationally expensive. RAGSys changes the game by allowing real-time knowledge integration:

  • Instant Updates: New information can be added to the knowledge base and immediately utilized by the LLM, ensuring up-to-date responses without model retraining.

  • Flexible Knowledge Management: Easily add, remove, or modify domain-specific information without touching the underlying LLM architecture.

  • Reduced Computational Overhead: Eliminate the need for frequent large-scale model retraining, significantly reducing computational costs and environmental impact.

Precision and Consistency in Outputs

LLMs are prone to hallucinations and inconsistencies, especially when dealing with specialized or rapidly changing information. RAGSys addresses this head-on:

  • Fact-Grounded Responses: By retrieving and incorporating relevant information, RAGSys ensures LLM outputs are anchored in factual data.Consistency
  • Across Queries: The retrieval mechanism helps maintain consistent responses to similar queries, enhancing reliability in critical applications.
  • Transparent Source Attribution: RAGSys can provide references to the sources of information used in generating responses, adding a layer of explainability often missing in traditional LLM outputs.

Adaptive Domain Expertise

While general-purpose LLMs struggle with specialized domains, RAGSys enables rapid adaptation to specific industries or use cases:


Advanced capabilities:

  • Effortless Domain Switching: Swap out knowledge bases to instantly repurpose the same LLM for different domains, from legal to medical to technical support.

  • Granular Expertise Layers: Layer multiple knowledge bases to create nuanced, multi-disciplinary expertise tailored to specific organizational needs.

  • Continuous Learning: The system can learn from interactions and feedback, constantly refining its domain expertise without the need for model-wide updates.

Scalability and Efficiency Reimagined

RAGSys transforms the scalability and efficiency landscape of LLM deployments:
Resource Optimization:

  • Focus computational resources on retrieval and integration rather than massive model retraining, allowing for more efficient scaling.

  • Distributed Knowledge Architecture: Leverage distributed knowledge bases, enabling organizations to manage and update information across various departments or geographical locations seamlessly.

  • Adaptive Performance: The system can dynamically allocate resources based on query complexity and retrieval needs, ensuring optimal performance under varying loads.
70%
Top GMV increase
+64%
Increase in push notification conversions
+67%
Increase in GMV through emails
+31%
Increase in products “saved for later”
-13%
Decrease in app uninstalls

How Grailed leveraged Crossing Minds’ scalable ML pipelines to solve for their marketplace “cold start” problem.

2.5x
Top sales increase
+43%
Increase in email CTR
+136%
Increase in conversion per email
+65%
Increase in conversion per email
+148%
Increase in paid booking

How Eventbrite harnessed Crossing Minds’ item based embeddings to power behavior-based recommendations that increased bookings by 184%

1320%
increase in
Conversion Rate
+96%
avg. increase in sales
+120%
avg. increase in
email CTR
+69%
increase in
new user conversion
+48%
increase in
advertising ROI

How Flink boosted conversion rates by 93% using Crossing Minds’ multimodal data enrichment to power highly-tailored grocery recommendations.

5x
Top sales increase
+175%
Increase in Conversion
+52%
Increase in return on ad sales
+78%
Average increase in cold start conversion rate

How Drinks leveraged Crossing Minds’ ML personalization pipeline to power their wine recommendations and lift average revenue per session by 78%.

Get an overview of Crossing Minds and its features.
Find out how to take personalized experiences to the next level.
A/B test and customize the smartest recommendations for your unique scenario.
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