Redefining Legal Operations with Multi-Model AI and Amazon Bedrock

Impact of Agentic AI & Intelligent Case Grading

‍

Overview

To address the challenge of processing high volumes of complex US visa applications, we developed a centralized, event-driven AI platform. This innovative solution automates the creation of sensitive legal declarations and intelligently grades case viability, streamlining the process for legal teams and strengthening the firm's operational efficiency through Generative AI. By implementing this platform, the firm empowered its attorneys and writers, reducing manual drafting time and instantly auditing compliance before investing billable hours.

Opportunity

Empowering Legal Teams with Smarter Case Management

In immigration law, the accuracy and emotional precision of client documentation are often the deciding factors in a successful case. High-quality documentation significantly influences the outcome, but creating extensive 10-30 page declarations remains a big challenge. It is a process that requires 8 to 10 hours, psychological expertise, and specialized legal knowledge. Furthermore, experienced attorneys were previously forced to read entire interview transcripts—often exceeding 50,000 tokens—to manually analyze statutory criteria and judge if a case had a viable chance of success. In an environment where operational efficiency is critical, solutions that automate high-friction workflows and improve case viability metrics are not just relevant, but essential for law firms to remain competitive and scale.

Solution

Agentic AI & Intelligent Case Grading

We developed a cutting-edge solution backed by the robust infrastructure of AWS and a multi-provider AI orchestration strategy. By utilizing Amazon Bedrock, which offers developers access to high-performance foundation models through a single API, we ensured the solution was scalable, reliable, and secure.

The platform features two core capabilities:

1. Automated Legal Document Generation: An Agentic AI workflow utilizing a Retrieval-Augmented Generation (RAG) architecture. It uses Amazon Nova Pro for preprocessing, Claude Opus 4.6 for complex drafting, and Jamba 1.5L as an uncensored fallback to accurately capture highly sensitive trauma descriptions without triggering false-positive safety filters.
2. Intelligent Case Viability Grading: A hybrid Machine Learning pipeline (XGBoost + LLM judges) utilizing Moonshot Kimi K2.5 via AWS Bedrock to process massive context windows. It automatically evaluates legal compliance (e.g., Abuse Sufficiency) and outputs an instant viability grade (A, B, C, or D).

‍

Architecture of the Solution

The platform operates by streamlining case processing through a highly scalable, event-driven architecture that prioritizes cost-efficiency by eliminating managed database overhead.

1. Ingestion & Storage: New legal transcripts are uploaded to Amazon S3 and logged.
2. Event Triggers: Amazon EventBridge and AWS Lambda detect uploads and send job messages to an Amazon SQS / Redis queue.
3. Compute & Semantic Retrieval: Containerized worker pods on Amazon EKS scale automatically. To execute the RAG pipelines, the workers do not rely on an expensive managed vector database. Instead, they run FAISS as a lightweight, in-memory vector database. This achieves sub-millisecond latency at zero infrastructure cost, with the vector indices safely cached directly in Amazon S3.
4. AI Orchestration: The system routes prompts through Amazon Bedrock (and direct Anthropic APIs) to generate drafts and viability reports.

Serverless Analytics: Finalized case data is structured into Apache Iceberg tables, where Amazon Athena handles all heavy lifting and querying directly from S3. This provides a serverless, pay-per-query model without the need for cluster management.

Results

80% Time Reduction and Unprecedented Cost Scaling

Following its deployment, the AI platform has delivered remarkable results. The initiative resulted in a 80% reduction in manual case drafting time and increased the human-reviewed draft quality to 4.0 out of 5.0. Furthermore, by strategically deploying models like Kimi K2.5 for heavy reading tasks, the pipeline operates at a fraction of the cost—up to 124x cheaper per call for large-scale case analysis.

Data & AI Infrastructure Stack

The platform operates on a heavily optimized, asynchronous architecture designed to separate lightweight API traffic from heavyweight AI processing, utilizing auto-scaling and serverless components to minimize idle costs.

• Compute & Orchestration (Amazon EKS & KEDA): The system separates the lightweight FastAPI layer from heavyweight background workers. Deployed on Amazon EKS using cost-effective ARM64/Graviton nodes, the architecture uses KEDA to automatically "scale to zero" based on Redis queue depth, ensuring compute is only billed when active jobs exist.
• Multi-Provider AI Orchestration: Employs Amazon Bedrock alongside Direct Anthropic APIs. This allows the platform to intelligently route preprocessing tasks to cost-efficient models (Amazon Nova, Kimi K2.5) while reserving premium reasoning tasks for Claude Opus 4.6.
• In-Memory Semantic Search (FAISS): The RAG pipelines execute FAISS in-process within the worker pods. This delivers sub-millisecond latency at zero infrastructure cost, with ephemeral vector indices safely cached directly in Amazon S3.
• Medallion Data Lakehouse (S3, Iceberg & dbt): Raw case data and LLM outputs are stored in Amazon S3 and transformed via dbt into an Apache Iceberg table format (Landing → Bronze → Silver → Gold). This ensures ACID transactions, schema evolution, and full historical preservation without data corruption.
• Serverless Analytics (Amazon Athena): By utilizing Amazon Athena, all heavy analytical lifting and complex transformations are executed serverless directly from S3 on a pay-per-query basis. This protects the main transactional CRM database from performance degradation.
• High-Speed Queueing (Redis): A standalone Redis pod serves a dual role: managing the asynchronous job queues for the AI workers and acting as a temporary status storage with a 24-hour TTL.

‍