Our thesis

The companies who figure out how to accurately teach AI about their business and data will win BIG.

Introduction

The most popular methods of giving AI knowledge are currently:

Training bespoke AI models
Fine tuning
Retrieval-augmented generation (RAG)

Form a business perspective, we believe that RAG is currently the most cost-effective and efficient way of giving AI the context and understanding it needs to solve real business problems.

Our leaderboard explores the current options on the market, scores them against a range of criteria (see our methodology) and gives them a rank out of 10.

Of course, the best solution can vary depending on the requirements, but we hope that the leaderboard gives you a starting point for further exploration.

Would you like us to evaluate your solution?

RAG Leaderboard

Solution	Documents	Overall	Accuracy	Latency	Consistency	Cost

We're currently performing tests - please subscribe using the form below to be updated when we go live.

Why RAG?

The competitive advantage of RAG

Our thesis: The companies that figure out how to teach AI about their specific business will win big.

Competitive Advantage: They will likely see exponential gains in efficiency, decision-making, and innovation, potentially leapfrogging competitors who are still struggling with basic AI implementation.
Scalability of Expertise: Businesses that can effectively transfer their unique knowledge and context into AI systems will be able to scale their expertise rapidly, potentially entering new markets or expanding services at unprecedented rates.
Personalisation at Scale: Solving this problem could allow businesses to offer hyper-personalised products and services to a mass market, combining the benefits of mass production with individual customisation.
Accelerated Learning and Adaptation: Organisations that crack this code will likely be able to learn and adapt to market changes much faster, as their AI systems will be able to quickly internalise and act on new contextual information.
Knowledge Preservation and Utilisation: These companies will be better at preserving and utilising institutional knowledge, even as employees come and go, providing a sustained competitive edge.
Innovation Catalyst: With AI systems that truly understand the context of the business, these companies may see a surge in innovation as the AI can make novel connections and suggestions based on deep, contextual understanding.
Operational Efficiency: These businesses will likely see dramatic improvements in operational efficiency as their AI systems can make more contextually appropriate decisions without constant human intervention.
Talent Attraction: Success in this area could make these companies extremely attractive to top talent, further fuelling their growth and innovation.
Industry Leadership: Businesses that solve this problem first in their respective industries could quickly become the new standards against which others are measured.
New Business Models: This capability could enable entirely new business models that we haven’t even conceived of yet, based on the ability to rapidly contextualise and apply knowledge at scale.

Imagine hiring the world’s smartest person, but they know nothing about your company. That’s current AI. Now imagine if you could instantly give them years of experience in your specific business. That’s the challenge companies need to solve with AI.

The most common approach to solve this problem is called RAG (“Retrieval-Augmented Generation”), and the goal of this leaderboard is to identify the tools and methods that give the best results.

A simple approach

Our methodology

Synthetic data

We have carefully prepared a dataset which allows us to put RAG solutions to the test in a range of scenarios.

Standardised tests

To ensure consistent and fair analysis of the different solutions, we repeat the same tests across all lthe RAG tools.

Constantly updated

As new solutions or versions are released, we'll update the RAG Leaderboard with the newest benchmark results.

The companies who figure out how to accurately teach AI about their business and data will win BIG.

Introduction

RAG Leaderboard

We're currently performing tests - please subscribe using the form below to be updated when we go live.

The competitive advantage of RAG

Our methodology

Synthetic data

Standardised tests

Constantly updated

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