RAGSys:
Next-Gen Retrieval for LLM Workflows

RAGSys, Crossing Minds' innovative AI Retrieval Engine, supercharges LLM performance without the pain of traditional fine-tuning.

Discover a cost-effective, scalable solution to enhance AI language models for any task.

RAGSys Value Propositions

Learn about RAGSys’s fast, injection-based in-context learning with the precision of fine-tuning—without the high cost.

Real-Time Knowledge Injection

Keep LLMs current with real-time, scored knowledge injection.
Avoid outdated AI with live, KPI-prioritized data retrieval.
Ensure accurate, compliant AI decisions with instant knowledge updates.

KPIs Driven Fine Tuning

Adapt LLMs to new tasks instantly with dynamic examples.
Skip fine-tuning: Real-time learning with live task demonstrations.
Empower anyone to refine LLM behavior instantly.

Context-Aware Personalization

Deliver hyper-personalized AI with real-time user context.
Engage users with LLM responses tailored to their history.
Create AI that adapts to each user, without fine-tuning.

Hallucination Reduction
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Reduce LLM errors with curated, domain-specific grounding.
Ensure high-confidence, source-backed AI responses.
Deliver accurate AI for critical applications, minimizing risk.

Format-Robust Generation

Force LLMs to generate reliable, structured data (JSON, SQL, etc.).
Ensure consistent AI formatting, eliminating post-processing.
Deliver structured outputs every time, preventing API and parsing errors.

Low-Code/No-Code Prompt Optimization

Refine performance through knowledge curation.
Give domain experts control: optimize AI without ML engineering.
Accelerate AI development: empower teams to tune performance easily.

Model-Agnostic Retrieval
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Use any LLM: avoid vendor lock-in with universal retrieval.
Swap models easily: keep your knowledge intact across LLMs.
Flexibility and portability: leverage any LLM without retraining.

Real Time Fine Tuning / Self-Improvement

Get fine-tuned performance without the GPU costs.
Improve AI in real-time without retraining models.
Achieve domain specialization without expensive fine-tuning.

Agentic A/B Testing
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A/B test AI responses in real-time with dynamic examples.
Refine AI with targeted testing, no model retraining needed.
Experiment and optimize AI with granular control and A/B testing.

Key Innovations

Real-Time KPIs Driven Fine-Tuning

RAGSys transcends traditional RAG limitations: 

KPI-Optimized Retrieval: Moves beyond static semantic search—our system prioritizes data that directly impacts your key business metrics, ensuring LLMs optimize for real-world success.
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Live Feedback Integration: Every user interaction refines the retrieval model and in-context learning in real time, eliminating the need for slow, expensive fine-tuning cycles.
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Adaptive Learning Loop: Instead of static prompts or manual tweaks, LLMs continuously self-improve by dynamically adjusting responses based on live KPI performance.

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Adaptive Knowledge Repository

At the heart of RAG-Sys lies a dynamic, self-improving knowledge base:

Custom Example Database: Create and maintain a tailored database of examples specific to your use cases and domain expertise. This allows your ML team to build a proprietary knowledge base that continuously enhances your LLM's performance in your unique business context.
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Model-Agnostic Design: Our adaptive few-shot database is engineered to be compatible across various LLM architectures. This flexibility allows you to switch between different LLM providers or versions without losing your accumulated knowledge and optimizations. 
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Continuous Learning: The system features an automated feedback loop that refines and expands its knowledge base in real-time, ensuring your AI capabilities evolve alongside your business needs.

RAG-Sys features a self-improving knowledge base with a custom example database, model-agnostic design, and continuous learning, enhancing LLM performance and adapting to unique business needs.

Advanced RAG

RAGSys transcends traditional RAG limitations: 

Entropy-Maximizing Selection: Our proprietary algorithms ensure LLMs receive a diverse, information-rich input, improving response quality and reducing redundancy. 
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Quality-Weighted Retrieval: Multi-factor scoring system prioritizes high-quality, relevant information, significantly reducing hallucinations and improving factual accuracy. 
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Domain-Specific Customization: Flexible rule engine allows seamless integration of business logic and regulatory requirements into the retrieval process.

RAG-Sys overcomes traditional RAG limitations with entropy-maximizing selection, quality-weighted retrieval, and domain-specific customization, enhancing input quality, reducing redundancy, and integrating business logic.

Efficient Few-Shot Learning

Redefining few-shot learning for enterprise LLM deployment: 

Optimal Example Selection: Leveraging advanced information theory, RAGSys identifies the most informative examples for in-context learning, dramatically improving task performance. 
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Accelerated Fine-Tuning: By optimizing the retrieval model instead of the entire LLM, RAGSys achieves fine-tuning speeds up to 300x faster than traditional methods. 
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Transfer Learning Across Models: Retrieval engines trained on one LLM can be efficiently transferred to another, allowing you to leverage your optimizations across different models and providers.

RAG-Sys redefines few-shot learning for enterprise LLMs with optimal example selection, accelerated fine-tuning, and transfer learning across models, enhancing performance and efficiency.

Results & Applications

Model Distillation in FinTech

Context: FinTech Data

One of our client is a large FinTech Enterprise
Billions of credit card transactions
Unstructured use of abbreviations

Example

New York Home Hardware Distributors

NY HOME HARDWARE D

NEW YORK HHDW DISTR

Task: Entity Deduplication with LLM

Extract and clean the Merchant Name using a Strong LLM (Claude 3.5 Sonnet)
Distill the Strong Teacher LLM into a Cheap Student LLM while preserving accuracy (Claude 3 Haiku)

Generate 4k training data from Claude 3.5 Sonnet (Teacher)
Fine-tune the student using this 4k dataset

Method

Accuracy

Total Time (min)

Static Few Shots

0.65

Fine Tuning

0.88

660

RAGSys

0.91

eCommerce Product Catalog Enrichment

Context

Leading B2C Marketplace
Millions of end-users are creating hundreds of millions of products
Extract products tags to boost search and recommendation

Example

Use an LLM to extract Product Tags based on the product details
Leverage a Manually Curated Set of Tags for Train and Validation

LLM Tags completion

Stone Nail File, Nail Art, Manicure
Amazing Stone Nail File
"best nail file i have ever used".
its never wears outs, and the tapered chiseled end is
wonderful Measures 4" long x 1/4" wide
you get 1 Pink or Green

Hidden Tags:

stone nail file, nail art, nail polish, nail tools, manicure, manipedi, pedicure, gift, nail health, nail file

Generated Tags:

stone nail file, nail art, manicure, nail tools, nail care, nail file, pink nail file, green nail file, nail grooming, nail accessories, nail health, pedicure

Method

Precision

Recall

Zero Shot

0.1196

0.1326

Static Few Shots

0.1295

0.1415

RAGSys

0.2286

0.2559

LLM Tags Completion: Results. Average over 1k items in the test set. LLM: gpt-4o

ICLERB

Organization

Model

ERR@10

nDCG@10

Crossing Minds

cm-ragsys-rlaif-mini-v1

0.860

0.701

Salesforce

SFR-Embedding-2_R

0.775

0.610

Cohere

rerank-english-v3.0

0.773

0.618

Snowflake

snowflake-artic-embed-m-v1.5

0.751

0.596

OpenAI

text-embedding-3-small

0.751

0.606

Nvidia

NV-Embed-v2

0.741

0.612

Designed for Enterprise Scale and Flexibility

Scalable Architecture & Rules

RAGSys is designed for enterprise-scale deployment, efficiently handling large datasets and complex retrieval tasks.

Our infrastructure scales seamlessly from proof-of-concept to full production, ensuring consistent performance as your AI needs grow.

Customizable Dataset Creation

Our intuitive dashboard enables rapid development of domain-specific knowledge bases.

Easily create and iterate on custom datasets, tailoring RAGSys to your unique business requirements without extensive data engineering.

Revolutionizing Task-Specific Performance

RAGSys achieves superior task-specific performance without resource-intensive fine-tuning.

Rapidly adapt LLMs to new tasks or domains, saving computational resources and accelerating deployment cycles.

RAGSys: Next-Gen Retrieval for LLM Workflows