The Rise of Self-Evolving AI: How Aavenir is Revolutionizing Contract Management

In the world of contract management, innovation is key to staying ahead of the curve. At Aavenir, we’ve been pushing the boundaries of what’s possible with AI-powered solutions. Our journey began with a humble laptop, a stray wire, and a vision to harness the power of AI in contract management.

The Early Days of Aavenir

It was March 2019 when Aavenir was born, with a modest team of ten members and a bold vision to revolutionize contract management. Among them were two dedicated souls in the AI department: myself, Anand Trivedi, and my colleague Sohel. We faced the daunting task of building advanced AI tools on a shoestring budget.

Aavenir’s early days: where innovation meets determination

The Challenge of Resources

In those early days, finding affordable technology was critical. Sohel and I spent countless hours researching where to find cheap GPUs. It was 2019, just before the pandemic, and the availability of cost-effective GPUs was already scarce. Our need for powerful computing resources to train sophisticated models was palpable, yet the financial constraints were a constant reminder of our startup reality.

The Arrival of COVID-19

Just as we were starting to make headway with our AI developments, the world was hit by the unprecedented COVID-19 pandemic. For a young startup like Aavenir, the timing could not have been more challenging. The early days of 2020 brought not just a health crisis but a severe disruption to businesses everywhere. We were no exception.

A Product Ahead of Its Time

Before the pandemic struck, we had just completed the initial version of our automated invoice scanning system. This was not just another AI tool—it was a groundbreaking solution capable of extracting data from financial documents without any predefined templates. You could throw any document at it, and it would work its magic, a feature that was poised to revolutionize financial processing. However, as markets trembled under the weight of the pandemic, collaboration and ventures stalled, and the promise of quick adoption seemed to fade away.

The Leap into Transformers

In the early months of 2021, as the world grappled with uncertainty, Aavenir was on the brink of a technological breakthrough. Sohel and I, determined to keep our momentum, pivoted from the LSTM and attention-based systems we had been laboring over to something more potent—transformer models. This shift was not just an upgrade; it was a revolution in how our systems understood legal and financial documents.

Transformer models: the game-changer in AI

Embracing the Challenge

The buzz around ChatGPT’s capabilities was undeniable, but it also brought to light a significant concern—data security. Our clients were excited about the possibilities of a generative AI but were equally worried about sharing sensitive data on an open platform like ChatGPT. This gap presented us with a unique opportunity.

Fine-Tuning Aavenir’s AVY AI

The buzz around ChatGPT’s capabilities was undeniable, but it also brought to light a significant concern—data security. Our clients were excited about the possibilities of a generative AI but were equally worried about sharing sensitive data on an open platform like ChatGPT. This gap presented us with a unique opportunity.

Fine-tuning AVY AI: the pursuit of excellence

Overcoming Deployment Challenges

As we progressed with our specialized AI model, the next monumental task was deployment. Achieving the speed and responsiveness of ChatGPT was essential, but the costs associated with high-performance computing resources were daunting. Our team was determined to find a solution that wouldn’t break the bank but would still deliver the blazing fast performance our clients expected.

Advancing AI with Self-Evolving Capabilities

As we faced the challenges of enhancing and refining our AI system, we turned to the wealth of knowledge available in the broader AI research community. Delving into research papers from leading organizations like Meta and Microsoft, we gleaned insights that shaped our strategy for creating a self-evolving large language model (LLM). Our goal was to not only address the immediate feedback from users but to establish a system that continually improves and adapts over time.

Self-evolving AI: the future of contract management

The Observer Model

The core of our new strategy was the implementation of what we called the “Observer Model.” This was a fine-tuned component designed to monitor and evaluate the main production model’s performance in real-time. The Observer Model’s primary function was to assess the responses generated by our AI on several critical parameters, including fairness, ethics, and factual correctness.

The Observer Model: the guardian of AI excellence

Reinforcement Learning Techniques

To integrate the insights gained from this feedback loop effectively, we employed advanced reinforcement learning techniques, specifically Proximal Policy Optimization (PPO) and Distributional Policy Optimization (DPO). These methods allowed us to fine-tune our AI based on real-world interactions and feedback, adjusting the model’s behavior in a controlled and incremental manner.

Reinforcement learning: the key to AI evolution

Continuous Enhancement

Our AI system at Aavenir is constantly evolving, making notable progress each day. However, it is not without its imperfections. The system still encounters glitches and suffers from various process gaps, highlighting the complexities of such advanced technologies.

Continuous enhancement: the pursuit of perfection