# CORA Group (CSI)

<figure><img src="https://3660801743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fr7aJ5Cs5vSNgj1v0N5pV%2Fuploads%2FHz82EsyXam50PbZXX368%2FCora.png?alt=media&#x26;token=76a0684e-e631-44f6-ac75-480ddde8062e" alt="" width="188"><figcaption><p><strong>Cora Group</strong> is a <strong>Constellation Software Inc. (CSI)</strong> portfolio company <strong>—</strong> Operating Group: <em><strong>Jonas Software</strong></em></p></figcaption></figure>

**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)

| Metric              | Before raia                                              | After raia (Target)                                                                                    |
| ------------------- | -------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ |
| **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:

1. **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.
2. **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.
3. **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.

| Component                                    | Role                                                                                                                                                                               | Key Knowledge Sources                                                              |
| -------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- |
| **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)

1. **Phase 1 — AI Blueprinting:** Develop the core knowledge model by defining the NDA clause taxonomy, playbook-to-clause mapping, and governance rules.
2. **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).
3. **Phase 3 — Integration:** Configure the secure document intake, processing, and output workflow within the raia platform.
4. **Phase 4 — Test Agent:** Backtest the agent against a corpus of previously reviewed NDAs to validate accuracy, consistency, and edge-case handling.
5. **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

1. **For legal work, human-in-the-loop is a feature:** The safest path to value is using AI to assist—not replace—human experts.
2. **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.
3. **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.
4. **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.
