Pravin Muthu | 30 May 2025
A common and powerful use case for Generative AI involves leveraging a repository of documents to enable both sophisticated search and query across the corpus and the ability to train models for generating new, customized documents. This approach offers a synergistic development pathway, as proper design allows both search and generation functions to utilize common data elements, maximizing efficiency. A case study is Defense Logistics Agency’s (DLA’s) DAAS, a significant DoD data broker. DAAS faces the substantial challenge of maintaining complex agreement cycles with numerous DoD partners, and the strategic application of AI to search existing agreements and generate new ones based on this rich dataset presents a transformative opportunity to streamline these critical processes and enhance operational effectiveness.
Building on a Strong Foundation: Existing GenAI Search and Metadata
DLA has already made significant investments in Generative AI for sophisticated search functionalities. Their Agreement Search Tool, for example, currently utilizes a collection of agentic agentic AI model to effectively query unstructured Interface Control Agreements (ICAs). This system leverages the newly approved FedRAMP High AWS Bedrock as a cost-effective endpoint, orchestrating an agentic model capable of running parallel searches across hundreds of documents within seconds, addressing the shortcomings of traditional search by understanding context and navigating complex document structures.
This advanced framework includes a custom-developed document indexing algorithm, which demonstrates significantly higher accuracy and recall compared to traditional RAG implementations. Embedding domain expertise in a dedicated knowledge base allows the system to understand accreditation statuses and recognize Risk Management Framework (RMF) tables. Furthermore, this knowledge base dynamically updates its information based on new inputs, such as addendums and annexes, which can overwrite learned knowledge from the base contract. Ongoing development is focused on creating an even more flexible approach to learn and incorporate information from freeform sources like emails and coordinator notes, making this existing infrastructure a launchpad for intelligent document generation.
The Leap to Generation: Turning Insights into Drafts
The transition for the DLA from its advanced Agreement Search Tool to a system capable of sophisticated document generation involves moving from information retrieval to active content creation. Structured metadata guides document generation by organizing system descriptions, technical specifications, and deadlines within the search tool’s architecture. It can pre-fill templates, define constraints for the GenAI, and validate outputs, repurposing this curated information for a new task. Similarly, the vast corpus of unstructured data—past ICAs, annexes, addendums, and even freeform communications like emails and coordinator notes—which the system learns from for search is invaluable raw material. This allows the GenAI to master stylistic nuances, common clauses, specific DoD language, and DLA terminologies, ensuring that the generated ICAs are not only accurate but also reflect established practices and negotiated specifics, showcasing an efficient repurposing of the entire data ecosystem for enhanced operational efficiency.
A Look Inside: The Agreements AI Generation System
The Agreements Generation System is designed to automate the extraction of key information from diverse documents and draft ICA renewals, often integrating with existing Agreement Lifecycle Management Systems (ALMS) to support collaboration.
- Document and Metadata Storage: ICA documents in PDF format are stored in an S3 bucket, serving as the primary repository. A DynamoDB instance stores the crucial metadata, facilitating efficient retrieval.
- Automated Triggers: An EventBridge event trigger can initiate the agreement generation process based on pre-defined schedules, such as an agreement nearing its 90-day expiration window.
- Intelligent Workflow: A Lambda function orchestrates the core workflow, including information extraction, draft generation using an agreement template (a NoSQL structure that drives prompt engineering), and validation processes. This workflow can employ both LLM approaches and non-LLM approaches for more static sections.
- Preprocessing Power: A crucial initial step involves a preprocessing Lambda function that reads ingested PDFs, extracts relevant metadata (like vendor name, system name, agreement ID), and structures this into a JSON format. This preprocessed data is essential for populating parts of the new ICA.
- Content Sourcing and Validation: The content for the new agreement is pulled from the extracted sections of the source agreement and any addenda. Validation rules, often embedded in the template, are then used to check if the generated content meets specified criteria, ensuring accuracy and compliance.
The Future is Generated
The transition from GenAI-powered search to automated document generation exemplifies how organizations can leverage existing technological investments to achieve greater efficiencies. By strategically leveraging established search capabilities and rich metadata stores, businesses can streamline complex processes like agreement renewals, leading to faster turnaround times, enhanced accuracy, significant cost savings, and a more strategic allocation of valuable human expertise. As these systems mature, the potential for continuous optimization and expansion to handle even more complex legal and contractual documents is immense.