Gravity Stack is a subsidiary of global law firm Reed Smith LLP launched in 2018 that helps in-house legal departments with technology implementation, data and operations. With deep expertise across forensics, eDiscovery, risk management and response, contract management, deal support, and legal AI transformation, GravityStack delivers modern solutions that help legal and compliance teams operate faster and smarter.
Their team combines its deep understanding of how in-house legal departments work (bolstered by the legal acumen of the lawyers at Reed Smith) with cutting-edge AI to help enterprises modernize operations without sacrificing accuracy, privacy, or control.
The Challenge: Manual Review at Enterprise Scale
One of GravityStack’s clients, a leading global bank, was struggling with the growing complexity of reviewing high volumes of credit agreements.
Each document required careful legal scrutinyand manual extraction of key terms, followed by a four-step quality control process. They were stuck with review cycles that took 3 to 5 days, and any attempt to support a new usecase meant starting from scratch and rebuilding the whole workflow.
The Goal: Automate Without Sacrificing Precision
The team at GravityStack saw a chance to make things better using AI.
They wanted to ease the manual workload while still keeping the level of legal care needed for risk-sensitive work. The goal was to move faster without lowering the bar for quality. Here’s what they set out to do:
- Extract critical data points from unstructured credit agreements
- Validate for legal accuracy
- Auto-populate a user-friendly checklist for stakeholders
All without bloating the QC process or introducing unnecessary tech overhead.
The Solution
To solve the problem, GravityStack built a smarter review system using Vellum. The result was a three-phase semi-automated workflow:
1. LLM-Powered Data Extraction
Using Vellum Workflows they built a system that pulls key terms and clauses from each agreement, transforming them into structured data.
2. Legal Validation Layer
Human reviewers validated AI output with a focus on context, ensuring results met legal and operational standards.
3. Automated Checklist Generation
The validated data fed directly into a customized checklist tailored to the bank’s internal review process. The result was a checklist that was actionable, compliant, and consistent.
Underpinning this system was an agentic AI layer via Vellum, making the entire workflow scalable and adaptable to future document types and compliance requirements.
The Results
After rolling out the new process, the results were clear.
Turnaround time dropped from up to five days to less than 24 hours. The number of quality control steps was cut in half, making reviews much simpler. What used to be a slow and manual process is now a fast, semi-automated workflow. And because it’s built on agentic AI with Vellum, the team can now scale to new use cases without needing to rebuild everything from scratch.
Why It Matters
This case study showcases how deep domain expertise and generative AI can work hand-in- hand to not only accelerate legal department workflows, but also maintain the precision required in high-stakes environments like banking.
“Vellum gives us the flexibility to design intelligent agents that think like our best legal professionals, but at scale. It’s not just about automation; it’s about transforming how knowledge work gets done.” — Bryon Bratcher, Managing Director, GravityStack
Summary
In partnership with Vellum, GravityStack transformed a manual, high-risk legal workflow into a scalable, hybrid AI and human solution.
Now, they’re able to deliver faster turnaround times, improved quality control, and future-ready infrastructure. Using agentic AI, they’ve created a repeatable blueprint for modernizing complex document review across financial services. If your team is looking to unlock similar efficiencies and elevate legal department operations with AI, GravityStack and Vellum can help!
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