Completeness and verification
Relative to the question and the authorized sources available. Make the evidence used, the steps to check and the missing information identifiable.
Applied research. Systems for enterprise AI.
We develop systems to query enterprise data, documents and relationships. To understand not just what to answer, but which evidence is needed and what still needs checking.
One question crosses several sources
Our starting point
In a business, useful information lives across different systems. An answer can sound convincing while missing the very fact that changes a decision. We work on the path from a question to its evidence, before the words of the answer.
Read our manifestoWhat guides our work
Relative to the question and the authorized sources available. Make the evidence used, the steps to check and the missing information identifiable.
Study how to select useful sources, context and execution steps. Measure the work required alongside the quality of the answer.
Evaluate quality, latency and cost together. The aim is to understand the cost of reaching a verified answer, beyond the cost of generating tokens.
01 / Our first product
From distributed knowledge to useful context.
Engine connects enterprise knowledge and selects relevant context for AI applications. It is our first product taking this work out of the lab.
Explore Ando EngineArchitecture, examples and access requests on the product page.Research / Open directions
We explore systems that learn to navigate the structure of knowledge and plan queries. Models specialized in the path to evidence, without placing enterprise knowledge into their weights.
A research direction, not an available feature.
Approach, evaluations and limitationsStart with Engine. Explore the architecture and evaluate the product on your own workload.
Explore Ando Engine