Why LastArray Exists
Shubhankar Kahali
AI systems are no longer experimental artifacts. They operate inside real work. They evaluate people, speak on behalf of organizations, gather information, and increasingly take action within complex environments shaped by incentives, uncertainty, and change.
Many of these systems appear capable at launch. Over time, reliability weakens. This rarely happens because the underlying models lack intelligence. It happens because the surrounding systems were never designed to endure shifting conditions. Context drifts. Data changes. Human behavior adapts. What once seemed correct becomes misaligned, often without a single, visible failure.
Most AI research ends at deployment.
Real behavior begins after.
LastArray was formed to work in that gap.
Our work spans human assessment, voice agents, and agentic market research. These domains may appear distinct, but they share a common constraint: they operate in high-variance environments where information is incomplete and decisions have real consequences. What connects our work is not the domain, but the way we build systems to survive those conditions.
We start from the assumption that intelligence alone is not sufficient. AI behavior emerges from the full system—data collection, representations, inference logic, infrastructure, feedback loops, and human interaction. Treating any one of these in isolation produces fragile outcomes and misplaced confidence. At LastArray, the system itself is the unit of work.
We design for conditions after deployment. Usage patterns will drift. Definitions will evolve. What counts as a “correct” answer will change over time. Performance matters, but only alongside reliability, interpretability, and the ability to remain stable under pressure. Our systems are built to surface uncertainty rather than hide it, and to work with human judgment rather than attempt to replace it.
LastArray is intentionally small. We operate as a tightly aligned research and engineering lab, optimized for long-horizon observation rather than rapid product cycles. This structure allows us to remain accountable for how systems behave in real settings, not just how they perform in early evaluations.
Every technical decision reflects this approach. Representations are chosen for stability. Evaluation prioritizes durable signal over short-term accuracy. Agent behavior is designed to adapt without quietly eroding trust.
Our goal is not visibility or speed.
It is to build AI systems that remain reliable as the world around them changes.
This is the work LastArray is here to do.
Co-Founder & CEO
