Kairos

Companies in regulated, high-stakes industries need AI that follows their specific policies, but off-the-shelf frontier models are probabilistic and don't know a company's internal rules or workflows. Public benchmarks show these models fail on long, real-world enterprise workflows, such as investigating a flagged transaction from alert to resolution, because most enterprise data is proprietary and never seen on the public web. Unlike math or code, most enterprise work (like writing a sound underwriting memo or reconciling an invoice) lacks a clearly verifiable right answer, making it harder for generic models to be trusted. This creates a reliability gap that makes stakeholders skeptical, since small output errors compound across an organization and erode trust, which Kairos describes as the last mile of enterprise AI adoption.

Kairos embeds with enterprise teams to draw out existing operator expertise and turns it into specialized AI agents trained on the company's own first-party data. These agents are deployed inside the customer's own environment so data never leaves their walls, and the resulting system is described as being owned by the company rather than Kairos. The company states its agents hit 99%+ reliability by learning an organization's specific rules from the people who run the process. Kairos has also launched Telos, described as a learning layer for long-horizon AI workflows, which the company says has been benchmarked on τ-bench.