Rebase enables organizations and AI product companies to move deployed AI models from pilot or initial rollout into fully adopted workflows that deliver measurable business impact. By providing tools and processes that bridge the deployment gap, it helps mid‑market firms integrate existing AI into daily operations and assists AI vendors in scaling post‑sale deployments, capturing structured feedback and driving tangible results.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations and AI product-focused companies often deploy models that remain in pilot or limited rollout phases, failing to become integrated parts of daily workflows. This results in unrealized business value, limited impact measurement, and insufficient feedback for continuous improvement.
Solution
Rebase provides a forward‑deployed engineering service that works alongside customers to transition deployed AI models into routinely adopted workflows. Using a repeatable “Understand, Apply, Measure” method, Rebase aligns AI capabilities with existing roles, processes, data governance, and performance metrics. The service embeds AI into day‑to‑day tasks, establishes ownership and control structures, and implements measurement disciplines to track business outcomes. For AI vendors, Rebase extends post‑sale deployment capacity, helping customers move from rollout to sustained adoption while collecting structured product feedback for iterative enhancement.
Target Audience
Primary customers are mid‑market enterprises seeking to operationalize existing AI models and AI product/platform companies that need to scale post‑sale deployments and drive measurable adoption.
Features
- Collaborative engagement model that integrates AI engineers with client teams to map AI to real work processes
- Structured methodology (Understand, Apply, Measure) with shared artifacts and measurement discipline
- Workflow integration support covering roles, data governance, ownership, and control mechanisms
- Continuous impact tracking and reporting to quantify business outcomes and ROI
- Feedback loop design that captures structured product usage data for AI product improvement
- Scalable post‑sale support for AI product and platform companies to increase deployment capacity