To stay competitive, firms regularly invest in innovation by supporting internal capital projects (funded and executed in-house) that explore new products and operational improvements. Each year, in a highly competitive process, managers from different functional units of the firm submit proposals (that include estimated costs and benefits) for such projects. Managers, because of their domain knowledge and expertise, are naturally better informed about the costs and benefits of their respective projects and can use this information strategically to secure funding. An example of such behavior is the under-reporting of the cost estimate of a project and subsequently requesting additional funding during the execution phase. Such strategic behavior not only affects the firm’s ability to fund the best projects but is also costly. Motivated by this challenge of deciding the funding of such projects at a global agribusiness firm, we seek a mechanism that is both provably near-optimal for the firm and guarantees truthful reporting from managers. Our setting consists of a principal and multiple agents. In each time period, over an infinite horizon, each agent requests funding for a potential project from the principal. Before submitting his proposal, each agent privately estimates the project’s cost and its benefit. If funded, each project is executed in one period. The principal’s funding decisions are binary; that is, each project is either funded in full or not funded. The actual cost (benefit) of a funded project is realized on its completion and incurred (earned) by the principal. The agents earn utility from the experience and reputation gained in completing projects. In each period, the principal desires to keep the total spend below a budget but can borrow external money at a cost. Our main contribution is a practically appealing dynamic nonmonetary mechanism for internal capital allocation under which, for any ϵ>0, (a) truth-telling forms an ϵ-Bayesian Nash equilibrium and (b) the principal’s expected utility is within ϵ of the expected utility in the first-best setting.