BRD — Legal Document Generation Agent

Business Requirements Document v2.0

Prepared for: G42 Enterprise AI Agent Submission Track: Legal AI Document Type: Business Requirements Document (BRD)

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1. Business Context & Problem Statement

1.1 Industry Problem

Legal professionals spend excessive amounts of time drafting, reviewing, and adapting repetitive contracts (e.g., NDAs, Master Service Agreements, Employment Contracts). These tasks are:

• Repetitive & Time-Consuming: Drafting a standard NDA from an existing template can take 1-2 hours of billable (or in-house) time.
• Prone to Risk: When using generic LLMs (like ChatGPT or standard Claude/DeepSeek interfaces), the models frequently hallucinate clauses, cite non-existent case law, or apply the wrong jurisdictional rules.
• Strictly Governed: Every clause must be verified and approved by legal counsel. There is zero tolerance for hallucinated legal obligations.

1.2 Market Opportunity

The Legal Document Generation Agent aims to solve this by creating an autonomous, audited, and strictly-governed AI assistant that:

• Generates required legal documents by retrieving and assembling only pre-approved clauses from a curated legal knowledge base (RAG).
• Never hallucinates text into a contract.
• Provides citations for every assembled clause, tracing back to the approved template or playbook.
• Drastically reduces drafting time while maintaining 100% compliance with internal legal standards.

1.3 Strategic Alignment

G42 Requirement	Our Agent Capability
Production Performance Metrics	Reduction in drafting time, hallucination rate dashboards
Base Model Architecture	DeepSeek LLM + ChromaDB RAG pipeline + strictly grounded prompts
Autonomy & Escalation Framework	Action allowlists (e.g., cannot draft outside jurisdiction), escalation to Senior Counsel
Security & Data Governance	RBAC, Client Confidentiality (PII/MNPI redaction), immutable audit trail
Observability & Improvement	Prometheus/Grafana metrics, strictly measured grounding rates

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2. Business Objectives & Success Criteria

2.1 Primary Business Outcomes

Outcome	Target	Measurement Method
Reduction in contract drafting time	≥ 60%	Time-on-task comparison vs. manual baseline
Hallucinated clause rate	0%	Measured by citation validation (every clause must map to DB)
Citation accuracy rate	100%	Validated during human-in-the-loop review
Cost per drafted document	< $1.00	LLM API + infra cost

2.2 G42 Submission Success Criteria

The agent is submission-ready when ALL of the following are met:

• End-to-end NDA/MSA generation runs without hallucination on 100% of benchmark tasks.
• The Citation Validator actively blocks/flags any LLM output that cannot be tied back to a retrieved chunk.
• Zero instances of PII/MNPI leakage in LLM call logs (redaction verified).
• RBAC roles enforced (Paralegal, Associate, Partner, Auditor).
• Audit log reconstructs full drafting history, including data sources used.
• Red-team: zero successful prompt injections attempting to insert malicious or unapproved clauses (e.g., hidden indemnity).

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3. Stakeholders & Roles

Role	Responsibility	Interaction with Agent
Paralegal / Jr. Associate	Primary workflow user	Prompts agent to draft documents, reviews initial output
Partner / Senior Counsel	Escalation recipient	Final approver for non-standard clauses or high-risk jurisdictions
IT/Infra Team	Deployment & maintenance	Manages infrastructure, RBAC, KMS for secrets
Compliance/Audit	Governance oversight	Reviews audit trails, signs off on data privacy
Agent Developer	Builds & maintains agent	Implements RAG pipeline, tests citation validators

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4. Business Scope

4.1 In-Scope Business Processes

1. Contract Drafting (Primary) — Generation of standard contracts (NDAs, Employment Agreements, MSAs) based on user prompts and retrieved approved templates.
2. Clause Adaptation — Modifying an approved clause for a specific jurisdiction (e.g., adapting a governing law clause to UAE Law) using only verified playbook rules.
3. Draft Review & Citation Checking — Validating that a generated draft contains zero ungrounded statements.

4.2 Out-of-Scope (Business Boundaries)

• The agent does NOT provide binding legal advice to external clients.
• The agent does NOT negotiate directly with counterparties.
• The agent does NOT access external, unverified legal databases (it operates ONLY on the internal ChromaDB knowledge base).
• The agent does NOT sign documents.

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5. Risk Register

#	Risk	Impact	Likelihood	Mitigation
R1	LLM hallucinates a legally binding clause not in the KB	Critical	Low	Strict RAG implementation; automated Citation Validator runs on output; 100% human-in-the-loop review.
R2	PII or Client Data sent to third-party Cloud APIs (OpenRouter/Supabase)	Critical	Low	Hard boundary: An offline, strictly local script (e.g. presidio) tokenizes all sensitive data before any network call occurs.
R3	Prompt injection to alter core contract terms (e.g., changing governing law against policy)	High	Medium	Input sanitization; red-team testing; system prompts enforce playbook constraints.
R4	G42 sovereign LLM requirement unspecified	Medium	High	Pluggable LLM adapter design; currently built on OpenRouter API but hot-swappable to local models (Ollama).

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6. Timeline Overview & Cost Projections

Phase	Focus	Est. Duration
Phase 1	Core RAG Pipeline (Ingestion, Retrieval, Drafting NDA)	2 weeks
Phase 2	Guardrails & Governance (Citation Validator, RBAC, PII)	2 weeks
Phase 3	Observability & Testing (Red-team hallucination tests)	1 week
Phase 4	Submission Package (Presentation, Metrics)	1 week

Estimated API Costs: DeepSeek API usage is highly cost-effective, projected at ~$30-80/month for development and initial testing. Local embedding models (sentence-transformers) cost $0.

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Version 2.0 | Business Requirements Document | G42 Legal AI Agent Submission