Whitney provides research‑grade, self‑improving AI agents designed for enterprise use, enabling companies to automate complex workflows while continuously learning from production data. The platform captures interaction traces to retrain models, ensuring agents stay aligned with business outcomes such as cost savings, exemplified by a $80M reduction in AI spend for a large healthcare organization.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often rely on generic large language models that lack domain-specific knowledge, require extensive manual prompting, and degrade in performance over time as business processes evolve. This leads to inefficient automation, higher operational costs, and limited control over data security and model behavior.
Solution
Whitney AI delivers research‑grade, self‑improving AI agents tailored to an organization’s specific workflows, policies, and historical data. By embedding domain expertise and guardrails directly into custom models, the platform enables reliable task automation while preserving safety and compliance. Production traces are continuously captured, evaluated, and used to retrain the agents, ensuring performance improves rather than erodes with use. The solution provides end‑to‑end infrastructure for deploying, monitoring, and updating agents, giving enterprises full ownership of their models and data. Demonstrated outcomes include multi‑million‑dollar AI spend reductions in large healthcare settings and near‑perfect automation accuracy for revenue‑cycle management tasks.
Target Audience
Primary customers are AI‑native enterprises in sectors such as healthcare, finance, and media that require secure, domain‑specific automation of complex workflows.
Features
- Custom model training that incorporates company SOPs, policies, processes, and historical operational data
- Integrated guardrails and safety controls to enforce compliance and prevent undesired actions
- Continuous learning loop that captures real‑world production traces, evaluates outcomes, and retrains agents for ongoing improvement
- Tool execution layer enabling agents to perform actions across browsers, APIs, and enterprise applications
- Contextual memory system that retains conversation and task state across interactions
- Deployment pipeline for production‑grade inference with sub‑100 ms latency and high availability