AI That Works!

Enterprise-grade AI Quality

Built with quality checks at every stage to ensure consistent accuracy and reliability for every response.

Restructured's novel approach gets rid of RAG systems, and is literally "restructuring" the entire process.

Kolena Research & White Papers

AI-Chat Fails in 79% of Tasks in Real World Use Cases

Traditional RAG applications fail in complex, real world tasks most of the time.

Handle vast datasets

RAG models struggle with efficiently retrieving and synthesizing insights from large datasets without significant delays or loss of contextual relevance, leading to incomplete or oversimplified responses.

Long Documents

When processing long documents, RAG systems often miss critical information located beyond the model’s maximum context length, resulting in partial or fragmented outputs that lack the full picture.

Complex Data

RAG models often perform poorly with intricate data, especially when interpreting complex relationships or multi-dimensional data structures. They may generalize incorrectly, failing to capture data-specific nuances.

Mathematical Operations

Most language models are not inherently built for precise mathematical computations. RAG systems, lacking deterministic processing capabilities, are prone to inaccuracies in calculations, especially in multi-step or logical math tasks.

Multi-Step Tasks

RAG models face limitations in performing tasks that require sequential steps, as they often overlook dependencies between tasks or fail to maintain the required context across steps, resulting in incomplete or erroneous outputs.

Kolena's Novel Approach to High Quality Outcomes

Enterprise-grade AI empowering informed decisions at scale. Quality checks at every stage and specialized agents that completes tasks the way you do.

Kolena's 5 AI Quality Principles

Specialized Agents

AI is not the right tool for every task. Kolena's proprietary Task Planner breaks the task down into components and intelligently assigns the correct agent to your task.

Transparency

Show your work. Kolena’s proprietary Task Planner breaks down complex problems into actionable tasks, sharing a detailed execution plan to build trust. This approach allows users to guide the AI toward the most accurate outcomes.

Critical Reasoning

Transparent AI reasoning for trust. Kolena agents share their decision-making process, enabling users to understand, trust, and refine AI outputs efficiently.

Quality Checks

Rigorous validation at every stage. Kolena’s AI quality agents work as an ensemble, meticulously scanning prompts and responses. Using a sophisticated voting system, they flag responses with low confidence to ensure only the highest quality outputs reach the user.

Continuous Improvements

Feedback-driven enhancement. Kolena’s feedback engine gathers insights from quality checks, ensuring that the system continuously learns and adapts to maintain peak performance.