The legal and compliance sectors are currently undergoing a significant shift as firms move away from tedious, manual document review toward intelligent automation. PwC’s new AI-driven annotation (AIDA) solution, built on Amazon Web Services, illustrates this transformation by tackling the challenge of 'unstructured' data—the messy, dense text found in contracts that has historically been difficult for machines to interpret reliably.

At the core of AIDA’s functionality is a strategy to combine human-defined rules with the reasoning capabilities of Large Language Models (LLMs). Rather than simply performing a keyword search, which often misses the nuance of legal clauses, AIDA interprets the context of these agreements. This allows legal teams to ask natural language questions—such as 'What is the termination notice period?'—and receive answers that are not only accurate but also explicitly linked to the relevant section of the source document. By using this conversational interface, employees can interact with their data more intuitively, much like chatting with a knowledgeable assistant.

A critical technical component of this system is Retrieval Augmented Generation (RAG). In high-stakes fields like law, simply generating an answer is not enough; the system must prove its work. RAG solves this by forcing the model to 'ground' its responses in the specific text of the documents provided. Instead of relying on the broad knowledge the model acquired during training, it first searches the internal library of contracts to find the relevant clauses and then synthesizes an answer based only on that retrieved information, complete with citations.

The practical impact on operations is substantial. For organizations like film and TV studios managing vast portfolios of license agreements, the time required to research rights information—such as streaming, theatrical, or derivative rights—has dropped by 90%. This allows teams to shift their focus from the rote work of searching and copying text to high-level analysis and decision-making, ultimately accelerating the review cycle and reducing the risk of human oversight in complex document workflows.

The legal and compliance sectors are currently undergoing a significant shift as firms move away from tedious, manual document review toward intelligent automation. PwC’s new AI-driven annotation (AIDA) solution, built on Amazon Web Services, illustrates this transformation by tackling the challenge of 'unstructured' data—the messy, dense text found in contracts that has historically been difficult for machines to interpret reliably.

At the core of AIDA’s functionality is a strategy to combine human-defined rules with the reasoning capabilities of Large Language Models (LLMs). Rather than simply performing a keyword search, which often misses the nuance of legal clauses, AIDA interprets the context of these agreements. This allows legal teams to ask natural language questions—such as 'What is the termination notice period?'—and receive answers that are not only accurate but also explicitly linked to the relevant section of the source document. By using this conversational interface, employees can interact with their data more intuitively, much like chatting with a knowledgeable assistant.

A critical technical component of this system is Retrieval Augmented Generation (RAG). In high-stakes fields like law, simply generating an answer is not enough; the system must prove its work. RAG solves this by forcing the model to 'ground' its responses in the specific text of the documents provided. Instead of relying on the broad knowledge the model acquired during training, it first searches the internal library of contracts to find the relevant clauses and then synthesizes an answer based only on that retrieved information, complete with citations.

The practical impact on operations is substantial. For organizations like film and TV studios managing vast portfolios of license agreements, the time required to research rights information—such as streaming, theatrical, or derivative rights—has dropped by 90%. This allows teams to shift their focus from the rote work of searching and copying text to high-level analysis and decision-making, ultimately accelerating the review cycle and reducing the risk of human oversight in complex document workflows.