Custom GPTs for Enterprise: Building Internal AI Tools

OpenAI’s Custom GPTs, launched at DevDay 2023, enable non-developers to create specialized AI assistants. For enterprises, this opens opportunities to empower employees while maintaining governance. Here’s how to approach Custom GPTs strategically.

Custom GPTs vs. Assistants API

Understanding when to use each:

Feature	Custom GPTs	Assistants API
Creation	No-code builder	Code required
Hosting	OpenAI ChatGPT	Your infrastructure
Access Control	ChatGPT Enterprise	Your auth system
Data Residency	OpenAI	Azure/your cloud
Customization	Limited	Full control
Cost Model	Subscription	Per-token

For enterprises with ChatGPT Enterprise, Custom GPTs are ideal for internal tools. For customer-facing applications, use the Assistants API.

Enterprise Use Cases

1. HR Policy Assistant

# HR Policy Assistant Instructions

You are the company HR Policy Assistant. Your role is to help employees understand company policies and procedures.

## Your Knowledge
- You have access to the Employee Handbook (uploaded)
- You know company policies for PTO, benefits, and workplace conduct
- You can explain processes for common HR requests

## Guidelines
- Always cite specific policy sections when answering
- If a policy doesn't exist, say so clearly
- For sensitive matters (harassment, legal), direct users to HR directly
- Never give legal advice
- Use a friendly, professional tone

## What You Can Help With
- PTO policies and calculations
- Benefits enrollment questions
- Expense reimbursement process
- Performance review timeline
- Remote work policies

## What You Should Escalate
- Specific compensation questions -> HR manager
- Legal concerns -> Legal department
- Harassment reports -> HR confidential line
- Medical accommodations -> HR specialist

2. Code Review Assistant

# Code Review Assistant Instructions

You are a senior software engineer helping team members with code reviews.

## Your Expertise
- Languages: Python, TypeScript, C#, SQL
- Frameworks: FastAPI, React, .NET
- Standards: Our coding guidelines (uploaded)

## Review Approach
1. First, understand the intent of the code
2. Check for:
   - Logic errors and edge cases
   - Security vulnerabilities (OWASP Top 10)
   - Performance issues
   - Code style violations
   - Missing tests
3. Provide specific, actionable feedback
4. Suggest improvements with code examples

## Output Format
For each issue found:

[Severity: High/Medium/Low] Brief description Location: file.py, line X Issue: Detailed explanation Suggestion: How to fix with code example

## Guidelines
- Be constructive, not critical
- Explain the "why" behind suggestions
- Acknowledge good patterns when you see them
- Prioritize security and correctness over style

3. Sales Enablement Assistant

# Sales Enablement Assistant

You help the sales team with product information, competitive intelligence, and customer communication.

## Your Knowledge Base
- Product documentation and pricing
- Competitive comparison sheets
- Case studies and success stories
- FAQ documents
- Objection handling guides

## What You Can Do
1. **Answer Product Questions**
   - Features and capabilities
   - Pricing and packaging
   - Technical requirements
   - Integration options

2. **Competitive Intelligence**
   - Compare our product vs competitors
   - Highlight differentiators
   - Address competitive objections

3. **Draft Communications**
   - Follow-up emails
   - Proposal introductions
   - Meeting summaries

## Guidelines
- Always use accurate, current pricing
- Don't make up features we don't have
- Qualify uncertain information
- Maintain our brand voice: professional but approachable

Governance Framework

Access Control Strategy

# GPT Access Policy Structure
categories:
  public_internal:
    description: "Available to all employees"
    examples:
      - HR Policy Assistant
      - IT Help Desk
      - Company Directory
    approval: Automatic
    review_frequency: Quarterly

  department_specific:
    description: "Limited to specific departments"
    examples:
      - Sales Enablement (Sales only)
      - Financial Analysis (Finance only)
      - Legal Research (Legal only)
    approval: Department head
    review_frequency: Monthly

  sensitive:
    description: "Contains confidential data"
    examples:
      - M&A Research
      - Executive Briefing
      - Compensation Analysis
    approval: C-level + Legal
    review_frequency: Weekly

Data Classification

# Framework for evaluating GPT data sensitivity

class GPTDataClassifier:
    CLASSIFICATION_RULES = {
        "public": {
            "description": "Publicly available information",
            "examples": ["Product docs", "Public FAQ", "Blog content"],
            "restrictions": None
        },
        "internal": {
            "description": "Internal but not sensitive",
            "examples": ["Org charts", "Process docs", "Training materials"],
            "restrictions": ["No external sharing"]
        },
        "confidential": {
            "description": "Business-sensitive information",
            "examples": ["Pricing", "Strategy docs", "Competitive intel"],
            "restrictions": ["Need-to-know access", "No copying"]
        },
        "restricted": {
            "description": "Highly sensitive data",
            "examples": ["PII", "Financial data", "Legal matters"],
            "restrictions": ["Strict access control", "Audit logging", "DLP required"]
        }
    }

    def classify_gpt_data(self, uploaded_files: list[str]) -> str:
        """Determine highest classification level of GPT data."""
        highest_level = "public"
        levels = ["public", "internal", "confidential", "restricted"]

        for file in uploaded_files:
            file_level = self._classify_file(file)
            if levels.index(file_level) > levels.index(highest_level):
                highest_level = file_level

        return highest_level

    def _classify_file(self, filename: str) -> str:
        """Classify individual file - implement your logic."""
        # Check filename patterns, content, metadata
        pass

Monitoring and Audit

# Custom GPT usage monitoring (conceptual - actual implementation
# depends on ChatGPT Enterprise admin features)

from dataclasses import dataclass
from datetime import datetime

@dataclass
class GPTUsageEvent:
    timestamp: datetime
    user_email: str
    gpt_name: str
    conversation_id: str
    message_count: int
    files_accessed: list[str]

class GPTAuditLogger:
    def __init__(self, storage):
        self.storage = storage

    async def log_usage(self, event: GPTUsageEvent):
        """Log GPT usage for audit trail."""
        await self.storage.append("gpt_audit_log", {
            **event.__dict__,
            "timestamp": event.timestamp.isoformat()
        })

    async def generate_usage_report(
        self,
        start_date: datetime,
        end_date: datetime
    ) -> dict:
        """Generate usage report for compliance."""

        events = await self.storage.query(
            "gpt_audit_log",
            filter=f"timestamp >= '{start_date}' AND timestamp <= '{end_date}'"
        )

        return {
            "period": f"{start_date} to {end_date}",
            "total_conversations": len(set(e["conversation_id"] for e in events)),
            "unique_users": len(set(e["user_email"] for e in events)),
            "by_gpt": self._group_by_gpt(events),
            "top_users": self._top_users(events, 10),
            "sensitive_file_access": self._sensitive_access(events)
        }

Best Practices for GPT Creation

1. Clear Scope Definition

# Template: GPT Scope Document

## Purpose
[One sentence describing what this GPT does]

## Target Users
[Who should use this GPT]

## Capabilities
- [Specific thing it can do]
- [Another capability]

## Limitations
- [What it cannot/should not do]
- [Topics it should decline]

## Data Sources
- [Document 1] - Classification: [Level]
- [Document 2] - Classification: [Level]

## Escalation Paths
- [Topic] -> [Person/Team]

## Review Schedule
- Owner: [Name]
- Review frequency: [Weekly/Monthly/Quarterly]

2. Effective Instructions

# Good Instructions Structure

## Identity
Who you are and your expertise level

## Knowledge Context
What documents you have access to and how to use them

## Task Guidelines
Step-by-step approach for common tasks

## Output Format
How to structure responses

## Boundaries
What to do and not do

## Tone and Style
How to communicate

3. Testing Protocol

Before deploying a Custom GPT:

1. Functional testing - Does it answer correctly?
2. Edge case testing - How does it handle unusual inputs?
3. Security testing - Can it be jailbroken?
4. Accuracy testing - Are citations correct?
5. User acceptance - Does it meet user needs?

Rollout Strategy

graph LR
    A[Pilot: 5-10 users] --> B[Limited: Department]
    B --> C[General: All employees]
    C --> D[Optimization: Iterate]

Each phase should include:

• User feedback collection
• Usage analytics review
• Instruction refinement
• Security assessment

Conclusion

Custom GPTs democratize AI tool creation but require enterprise governance. Balance empowerment with control through clear policies, data classification, and monitoring. Start with low-risk internal tools and expand as you build confidence and processes.

The organizations that master Custom GPT governance will unlock significant productivity gains while managing risk effectively.