For CFOs and CIOs looking to modernize their finance operations, automation alone, as seen in generations of robotic process automation (RPA), isn’t enough. It is clear that transparency and explainability are equally important.
Accounting firms and internal finance departments are now turning to AI systems that make inferences rather than just calculations. One of the most ambitious examples is Basis, a US-based startup founded just two years ago. The company builds AI agents designed to automate structured accounting tasks and stay on top of human oversight.
Such systems represent a shift toward automation in enterprises. Rather than replacing people, AI agents augment human expertise, combining the accuracy of AI models with the oversight financial professionals need for compliance and customer trust.
Efficiency with responsibility
Basis develops AI agents that handle routine financial tasks such as reconciliations, journal entries, and financial summaries. The platform is built on OpenAI’s GPT-4.1 and GPT-5 models and provides the ability for operators to autonomously inspect each decision-making step.
Accounting firms using Basis report up to 30% time savings and improved advisory capabilities as a result. This is the type of value creation that traditional automation cannot deliver to businesses as quickly or at a similar cost.
Unlike many automation tools that operate as black boxes, Basis emphasizes reviewable inference. All recommendations include an explanation of the data used and the logic behind it. Visibility means accountants can verify each result and remain accountable for the results. This is always an important feature in financial operations, especially in highly regulated industries.
Building a learning system
Agentic AI can treat accounting as a network of workflows rather than an isolated task. For Basis’ platform, a monitoring AI agent powered by GPT-5 manages the entire process. You can choose an AI model depending on the complexity of your job and the type of data being processed, and delegate subagents for specific tasks to be performed by different models.
For example, for quick queries and clarifications, Basis uses GPT-4.1 for speed, but for complex classifications and month-end closes, GPT-5 provides better inference and context processing.
The company benchmarks each model against real-world accounting workflows to determine when it’s safe to give agents more responsibility. Financial professionals can always see what the system did, why it made certain choices, and how confident they are in its recommendations.
This flexible architecture allows businesses to scale AI and ensure accuracy as levels of automation increase. This process reflects the hybrid human-AI collaboration that is now becoming the norm in areas such as legal services and risk management.
Lessons for other fields
What makes Basis and financial multi-agent AI relevant beyond accounting is a model orchestration approach that routes tasks to the most appropriate AI model based on performance and latency.
This format may inform similar developments in procurement, human resources, or compliance operations. In fact, wherever a large amount of structured decision-making requires transparency and, to use a terrible pun, accountability.
The collaboration between Basis and OpenAI shows how effective AI inference engines can be in secure data environments.
The goal is not pure speed, but automation that increases trust in the operator and the model itself. These are systems that evolve without humans losing control of their outcomes.
conclusion
AI in the accounting field is not limited to automating data entry, but is moving towards building systems that think like accountants rather than machines.
For enterprise leaders, Basis’ model shows a path to automation that improves over time. Every time your model improves, your team gets faster and smarter, without handing over control to automated processes.
(Image source: “Accounting charts” from World Bank Photo Collection is licensed under CC BY-NC-ND 2.0.)
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