AI That Learns While You Use It

Sudip Roy, co-founder and CTO of Adaption Labs, argues that enterprise AI adoption often fails due to a “last mile reliability” problem—the final 5% of edge cases where models are unreliable. This gap blocks full productization in enterprise settings where dependability is paramount. Current strategies to bridge this gap have significant drawbacks:

• Prompt Engineering: While offering some control, it is time-consuming and creates brittle solutions that are specific to a particular model version.
• Fine-Tuning: This process is expensive and slow, often taking weeks or months. Its success is heavily dependent on access to large amounts of high-quality data, which is often a major barrier.
• Waiting for Frontier Models: Relying on the next major model release yields diminishing returns, as much of the high-value data needed for customization is locked within enterprises, not available to public model trainers.

The Case for Adaptation

Adaptation is presented as a new paradigm to address these challenges. It is distinct from traditional post-training techniques, which typically rely on gradient updates to modify a model’s weights. Instead, Adaption Labs focuses on gradient-free approaches, which are inference-time strategies designed to be fast, interactive, and more cost-effective.

The core intuition is to place an algorithmic layer between the user and the foundation models. This layer abstracts away complexity and makes intelligent decisions to fulfill the user’s objective. Its functions include:

• Routing tasks to the most appropriate model.
• Using a combination of models to generate a response.
• Dynamically merging models to accomplish a specific task.

This approach enables continuous learning and improvement without the high unit cost associated with retraining or fine-tuning. While access to open-weight models can unlock more powerful techniques, many adaptation strategies can be successfully applied even to proprietary, black-box models.

The Three Pillars of Adaption Labs

Adaption Labs’ strategy is built on three core pillars designed to create a holistic ecosystem for adaptive AI:

1. Adaptive Data: Addresses the foundational challenge of data scarcity for fine-tuning and customization. The goal is to develop products that help enterprises synthesize and improve the quality of their data, making customization more accessible.
2. Adaptable Intelligence: This is the core algorithmic engine responsible for continuous learning and dynamic optimization. It manages the proportional allocation of compute, ensuring that simple tasks use cheaper models while complex reasoning tasks are routed to more powerful ones, leading to significant cost savings.
3. Adaptable Interfaces: Moving beyond the monolithic chat interface, this pillar focuses on creating task-appropriate UIs. These interfaces are designed to present information in the most natural way for a given task and, crucially, to create a seamless feedback loop that allows users to guide the continuous learning process.

Relationship with the AI Ecosystem

Adaptation is not a replacement for existing tools but a complementary technology that enhances the core modeling layer.

• RAG and AI Agents: Retrieval-Augmented Generation (RAG) solves for context, and agents provide an action-taking framework. However, both ultimately rely on a model to perform the core reasoning. Adaptation makes that “brain” of the system more reliable, efficient, and intelligent.
• Monitoring and Observability: Adaptation does not eliminate the need for monitoring. Enterprises should maintain their observability practices to baseline performance and validate the improvements delivered by the adaptive layer.
• Transparency and Control: For regulated industries, the abstraction of an adaptive layer can raise questions about transparency. The solution lies in providing users with more direct control over model behavior and deploying systems in a compliant manner. The desire for “white-box” models is often a proxy for the need for control, which adaptation aims to provide through more intuitive and effective mechanisms.

• Tags:
• LLMs
• Fine-Tuning
• Enterprise AI
• Model Reliability
• Continuous Learning
• Adaptation
• Gradient-Free Methods