Every year, in the delivery of tax compliance and prospective analysis work products, accounting professionals expend millions of hours working with antiquated tools in error-prone and labor-intensive business processes. Industry leaders are increasingly focused on the use of automation and computation to significantly reduce these low-value labor hours.

Crimson Tree Software creates high-performance tax automation systems that dramatically reduce costs of delivery and improve the quality of the work delivered. Our focus lies on those difficult problems that have both tax-technical “depth” and volumetric “breadth”.

Over the past 20 years, our clients have included Big 4 accounting firms, UPREITs, hedge funds, private equity investment funds and corporate joint ventures.

Our team consists of accounting and finance professionals, computer scientists, software engineers, and information security specialists.

In 2018, systems invented by Crimson Tree Software service over 1,000 public accounting clients and over $1 billion in recurring fees.

Parallel Processing

Our computational systems are optimized for performance using parallel algorithms and lock-free data structures. The speed of our solutions ensures that mistakes are easy to correct, and that no time is wasted waiting for low-value tasks to complete.

In-Memory Analysis

We empower users to make informed decisions in real time using fast, in-memory analytical tools. Combined with parallel processing, in-memory data analysis allows difficult “what-if” questions to be answered in minutes instead of days or weeks.

Artificial Intelligence and Machine Learning

Our automation systems employ modern heuristic AI and applied statistics in the areas of computer vision and natural language processing. In practice, these systems dramatically reduce the needs for error-prone, manual data extraction and document analysis.

Research and Development

Our development process is rooted in practical experimentation. Decisions are made based on results of tests using flawed, real-world data. We leave no room for false, simplifying assumptions. When approaching novel problems, we digest academic literature, consult with other researchers in industry and academia, conduct experiments, and back our work products with quantifiable and repeatable results.

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