CORA Group (CSI)
LEGAL / M&A
CORA Group partners with raia to build a custom AI agent for NDA review and redlining -accelerating early-stage deal execution by automating first-pass legal review and ensuring consistent application of CORA’s legal playbook.
Snapshot (Planned Targets)
NDA Review Time
Hours or days per document
Faster first-pass NDA turnaround via AI-generated redlined draft (targets set after baseline review)
Consistency
Varies by reviewer and workload
Standardized to CORA’s legal playbook and historical precedent
Manual Effort
High; manual clause-by-clause review and redlining
Lower; team shifts from drafting to reviewing AI-generated redlines
Risk Control
Dependent on individual knowledge of preferred positions
Uniform application of approved fallback language and risk tolerances
Scalability
Limited by legal team bandwidth during peak deal flow
Supports higher throughput without proportional legal headcount growth
Note: This case study describes the planned solution and target outcomes. Measured results can be added once CORA’s agent is deployed and benchmarked.
1 | Company & Challenge
NDAs are a frequent, time-sensitive first step in CORA Group’s deal lifecycle. The existing manual review process can create bottlenecks—slowing early-stage execution and consuming valuable legal team capacity.
As CORA evaluated how to accelerate this workflow, three core challenges emerged:
Slow, manual review cycles: The legal and M&A teams spend significant time on repetitive, clause-by-clause review, cross-referencing internal playbooks, and drafting redlines.
Inconsistent application of standards: Under time pressure and variable deal volume, ensuring every NDA reflects CORA’s latest preferred legal positions and risk tolerances is difficult.
Low-value use of expert time: Highly skilled legal professionals are pulled into routine drafting work instead of focusing on exceptions, negotiation strategy, and complex legal matters.
CORA needed a solution that could automate the first-pass review, enforce consistency, and free up its legal team to focus on more strategic work—without introducing unnecessary technical or compliance risk.
2 | Why CORA Group chose raia
CORA selected raia to build a sophisticated AI legal assistant that balances speed with the critical need for human oversight:
Human-in-the-loop safety: The solution is designed as a document analysis and redlining agent, not a fully autonomous editor. The AI generates a revised draft for human review, ensuring a legal professional makes the final decision.
Playbook-driven consistency: raia trains the agent on CORA’s NDA playbook, preferred clauses, fallback language, and historical precedent so the output aligns with internal standards.
No risky system integrations: The agent operates on document inputs (Word, PDF) and produces a revised document as output—without direct integrations into Microsoft Word or live collaboration platforms.
Foundation for future M&A automation: This initial project creates a reusable legal knowledge framework that can be expanded to other agreement types, supporting CORA’s broader M&A enablement interests.
3 | Solution Design
The solution is an AI-powered legal co-pilot, trained on CORA’s proprietary data, that transforms the NDA review process from manual drafting to automated analysis and supervised revision.
NDA Review & Redlining Agent
Ingests an NDA (Word/PDF), analyzes it against CORA’s playbook, and generates a revised draft with recommended, clearly annotated redlines and preferred replacement language.
CORA M&A NDA playbook; previously executed NDAs (both clean and revised versions)
Clause Taxonomy & Knowledge Architecture
A structured framework mapping clauses to CORA’s preferred positions, fallback language, and risk tolerances—forming the agent’s decision logic.
CORA legal standards; historical redlining patterns
Secure Document Processing Workflow
A controlled, internal workflow for document intake and output within the raia platform, with version traceability and access controls.
Internal raia platform
Illustrative redline focus areas (examples)
To increase consistency and reduce back-and-forth, the agent focuses on common NDA pressure points such as:
scope of “Confidential Information” and permitted disclosures
term and survival provisions
return/destruction obligations
residuals / use restrictions
injunctive relief and remedies language
assignment and change-of-control language
governing law / venue (where applicable)
(Final scope and preferred language are driven by CORA’s playbook and precedent.)
Guardrails (by Design)
Human-in-the-loop approval: The AI only generates drafts; a CORA team member must review and approve changes before they are shared externally.
Conservative legal behavior: The agent adheres strictly to the playbook and historical precedent—avoiding novel legal interpretations.
No direct editing: The agent does not have live access to edit documents in place. It produces a new, redlined version for review, ensuring a clear audit trail.
Internal & controlled: Processing remains within a secure environment with no external system integrations, minimizing compliance and security risks.
4 | Deployment Timeline (Phased)
Phase 1 — AI Blueprinting: Develop the core knowledge model by defining the NDA clause taxonomy, playbook-to-clause mapping, and governance rules.
Phase 2 — Train AI Agent: Train the agent on historical NDAs to learn clause-level reasoning and the correct application of preferred language (including when “no change” is appropriate).
Phase 3 — Integration: Configure the secure document intake, processing, and output workflow within the raia platform.
Phase 4 — Test Agent: Backtest the agent against a corpus of previously reviewed NDAs to validate accuracy, consistency, and edge-case handling.
Phase 5 — Launch: Deploy the production-ready agent for the M&A and legal teams, including user training and feedback collection.
5 | Expected Impact (Targets)
CORA’s target outcomes for the engagement include:
Accelerated deal velocity: Reduce time spent on initial NDA review so the M&A team can engage counterparties faster.
Improved consistency & risk control: Ensure every NDA review uniformly applies CORA’s approved legal positions and risk tolerances.
Reduced legal team load: Free up the legal team from repetitive drafting to focus on complex and strategic M&A work.
Scalable M&A operations: Handle increasing deal flow without requiring a proportional increase in legal headcount.
6 | Lessons for Prospective Clients
For legal work, human-in-the-loop is a feature: The safest path to value is using AI to assist—not replace—human experts.
Your playbook is your moat: The AI is only as good as the knowledge it’s trained on. A clear playbook and good precedent examples are the critical inputs.
Avoid unnecessary technical risk: Live-editing integrations can introduce security and compliance risk. A document-in, document-out workflow is safer and faster to deploy.
Backtest before rollout: Comparing AI drafts to historical finals creates a clear quality bar and reveals edge cases early.
7 | What’s Next
Following deployment of the NDA Review & Redlining Agent, raia and CORA Group can expand the reusable legal knowledge foundation to additional M&A document types (e.g., LOIs, MSAs) and adjacent M&A enablement workflows.
8 | Key Takeaway
CORA Group is building a secure, human-in-the-loop AI agent to automate first-pass NDA review and redlining. By training an AI on its internal legal playbook and historical data, CORA is creating a scalable system to accelerate deal cycles, enforce consistency, and reduce manual legal effort—establishing a reusable foundation for broader M&A automation.
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