Turning contracts into searchable data at OpenAI

这是我们关于 OpenAI 如何在自身技术上构建解决方案系列的一部分。

当合同成为瓶颈

每笔企业交易都伴随一份签署的合同。每份合同都有起始日期、计费条款和续约条款。

起初，这个流程还能应付：逐行阅读，抄到电子表格中，然后继续下一份。但当数量翻倍又翻倍时，这种人工方式就崩溃了。

“不到六个月，团队每月审查的合同从数百份增加到一千多份。我们只新招了一个人，很明显这个流程无法扩展，”AI 工程师 Wei An Lee 说道。

构建更智能的工作流

我们的财务和工程团队没有简单地增加人手，而是构建了一个合同数据代理。设计原则很简单：把合同审查中的重复工作去掉，同时让专家保持对结果的掌控。

该代理分三步工作：

• 导入数据：PDF、扫描件，甚至带手写修改标注的手机照片。原本几十个不一致的文件现在都流入同一条管道。
• 基于提示的推理：使用检索增强提示，系统将合同解析成结构化数据。它不会把上千页内容全部塞进上下文；只提取相关内容，对其进行推理，并展示其推理过程。
• 审核：财务专家审核结构化输出，输出包含注释和对任何非标准条款的参考。代理会标出不寻常的地方；然后由人工介入复核。

“我们不仅是在解析合同，我们在推理——说明为什么某个条款被视为非标准，引用参考材料，并让审阅者确认 ASC 606 的分类。”——AI 工程师 Siddharth Jain

自信的合同审查

输出是一个可以立即用于各类财务工作流的数据集。曾经需要数小时的工作现在可以在一夜之间完成，带注释并准备好验证。专家仍在环，但他们的角色从手动录入转为判断和决策。

“令人惊讶的是，繁重的工作由 AI 完成——然后我们的团队早上起来就能看到已经准备好供他们复核的数据。”——AI 工程师 Wei An Lee

这种设计保证了信心：专业人员可以在规模化情况下获得结构化、有理据的数据，但最终由他们的专业知识来驱动结果。

成果：

• 周转更快：审查时间减半，夜间即可完成。
• 更高产能：在不按比例增加人员的情况下处理数千份合同。
• 更智能的上下文：非标准条款会带有推理和参考并被标注出来。
• 可查询的结果：在数据仓库中以表格形式输出，便于进一步分析。

每一轮人工反馈都会使代理变得更好，让每次审查更快、更准确。

“随着 OpenAI 的扩展，我们唯一能扩展的方式就是通过这种方法，”Wei An 说。“没有它，你就得随合同量线性扩张团队规模。这个方法让我们在处理高速增长时依然保持精简团队。”

超越合同

这一架构现在支持采购、合规，甚至月末结账。原则相同：自动化机械性工作，让人类掌握判断。

工程师把它形容为“把手工活做完”，而不是替代决策。财务团队仍然撰写数字背后的故事；代理确保他们不用整天做繁琐的工作。

财务的新工作模式

最初为合同问题设计的解决方案已成为财务工作的新方式。数据解析在夜间运行。专业人员专注于分析和策略。领导者在增长时也能自信扩张，而不必与团队规模同步增长。

合同数据代理是 AI 在受监管、高风险工作中负责任地转型的蓝图。它展示了当专家与智能系统合作时可能实现的成果：更高的杠杆、更大的信心，以及更多时间投入到最重要的事情上。

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This is part of our series about how OpenAI is building its own solutions on our technology.

When contracts became the bottleneck

Every enterprise deal comes with a signed contract. Each one has start dates, billing terms, renewal clauses.

At first, the process was manageable: read line by line, retype into a spreadsheet, move on. But when volume doubled and doubled again, this manual approach broke.

“In less than six months, the team went from reviewing hundreds of contracts each month to more than a thousand. And yet we’d only hired one new person. It was obvious that the process wasn’t going to scale,” says Wei An Lee, AI Engineer.

Building a smarter workflow

Instead of throwing more people at the problem, our finance and engineering teams built a contract data agent. The design principle was simple: take the repetition out of contract review, keep experts firmly in control.

The Agent works in three steps:

• Ingest data: PDFs, scanned copies, even phone photos marked up with handwritten edits. What used to be dozens of inconsistent files now flow into one pipeline.
• Inference with prompting: Using retrieval-augmented prompting, the system parses contracts into structured data. It doesn’t dump a thousand pages into context; it pulls only what’s relevant, reasons against it, and shows its work.
• Review: Finance experts review the structured output, complete with annotations and references for any non-standard terms. The agent highlights what’s unusual; humans are then looped in to review.

“We’re not just parsing, we’re reasoning—showing why a term is considered non-standard, citing the reference material, and letting the reviewer confirm the ASC 606 classification.”

Confident contract reviews

The output is a dataset that’s immediately useful across finance workflows. What once took hours arrives overnight, annotated and ready for validation. Experts remain in the loop, but their role shifts from manual entry to judgment.

“The amazing thing is that the heavy lifting happens with AI—and then our teams wake up in the morning to data that’s ready for them to review.”

This design ensures confidence: professionals get structured, reasoned data at scale, but their expertise drives the outcome.

The results:

• Faster turnaround. Reviews cut in half, ready overnight.
• Higher capacity. Thousands of contracts processed without expanding headcount in lockstep.
• Smarter context. Non-standard terms flagged with reasoning and references.
• Queryable results. Tabular output in the data warehouse allows for easier data analysis.

Each cycle of human feedback sharpens the Agent, making every review faster and more accurate.

“The only way we can scale as OpenAI scales is through this,” Wei An said. “Without it, you’d have to grow your team linearly in lockstep with contract volume. This lets us keep the team lean while handling hypergrowth.”

Beyond contracts

This architecture now supports procurement, compliance, even month-end close. The same principle applies: automate the rote work, keep humans in charge of judgment. 

Engineers describe it as “manual work already done,” not decisions replaced. Finance teams still write the story of the numbers; the Agent ensures they don’t spend their day doing painstaking work.

A new operating model for finance

What started as a fix for contracts has become a new way of working in finance. Data parsing runs overnight. Professionals focus on analysis and strategy. Leaders scale confidently with growth, without growing teams in lockstep.

The contract data Agent is a blueprint for how AI can responsibly transform regulated, high-stakes work. It shows what becomes possible when experts partner with intelligent systems: more leverage, more confidence, and more time spent on what matters most.

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