Artificial Intelligence

Enterprise AI Platform with Secure Multi-Model LLM Access

Read Time5 minutes

IndustryArtificial Intelligence

Technologies

ReactNode.jsPostgreSQLOpenAIPythonDocker

Building a Secure AI Gateway for the Enterprise

Organizations want the productivity gains that large language models deliver, but they cannot risk exposing sensitive data to external AI providers. We built an enterprise AI platform that gives every employee access to GPT-4, GPT-4O, and Perplexity through a single secure interface, with role-based controls, local prompt screening, and complete audit trails.

Background

As LLMs became mainstream, employees across departments started using public AI tools for everything from drafting emails to analyzing data. The productivity benefits were clear, but so were the risks. Sensitive client data, internal strategies, and proprietary code were being pasted into tools with no organizational oversight.

The client needed a platform that could deliver the same LLM capabilities employees were already using, but within a controlled, auditable environment that met enterprise security and compliance standards.

The Challenge

Adopting AI at the organizational level introduced risks that individual tool usage could not address:

Data Exposure Risk: Employees using public AI tools like ChatGPT were pasting sensitive company data, client information, and proprietary code into external services with no visibility or control.
No Central Governance: Every department adopted different AI tools independently, creating a fragmented landscape with no audit trail, cost tracking, or usage policies.
One-Size-Fits-All Limitations: Public LLMs lack context about the organization, its terminology, compliance requirements, and role-specific workflows.
Compliance Gaps: Regulated industries and enterprise clients demanded proof that sensitive data never left controlled boundaries, which public AI tools could not guarantee.
Cost Sprawl: Individual subscriptions across teams made it impossible to track actual AI spending or allocate costs to departments.

The Solution: A Unified AI Platform

We built a centralized platform that acts as a secure gateway between the organization and multiple LLM providers. Every interaction passes through a local screening layer before reaching any external model.

Multi-Model Interface: A single, unified chat interface where users can switch between GPT-4, GPT-4O, and Perplexity depending on the task. Complex reasoning tasks route to GPT-4, faster queries use GPT-4O, and research tasks leverage Perplexity for web-grounded answers.
Local Prompt Screening: Before any prompt reaches an external LLM, a locally hosted screening model inspects it for sensitive data, PII, proprietary terms, and policy violations. Flagged prompts are blocked or redacted before transmission.
Role-Based Access Controls: Administrators configure access by department, team, or individual. Each role gets tailored permissions, model access, and usage quotas that align with their function and security clearance.
Custom System Prompts: Every role can have pre-configured system prompts that shape the AI's behavior. Legal teams get compliance-aware responses, engineering teams get code-focused outputs, and marketing teams get brand-aligned suggestions.
Data Boundary Controls: Strict rules govern what data can be sent to which model. Sensitive categories are defined at the organization level, and the screening layer enforces these boundaries on every interaction.

Governance and Compliance

Enterprise adoption requires more than access. It requires proof of control. The platform provides comprehensive governance capabilities:

Full Audit Trail: Every prompt, response, model selection, and user action is logged with timestamps. Compliance teams can review interactions, investigate incidents, and generate reports on demand.
Token Tracking and Cost Allocation: Usage is tracked at the user, team, and department level. Token consumption maps directly to cost, making it easy to allocate AI spending across business units.
Usage Analytics Dashboard: Leadership gets visibility into adoption patterns, popular use cases, model preferences, and cost trends. This data helps inform policy decisions and investment in AI capabilities.
Conversation History Management: Users can access their conversation history, organize threads by project, and resume previous sessions. Administrators can set retention policies and purge data when needed.

Results

The platform delivered measurable impact across the organization:

Secure AI access deployed across the entire organization without data exposure risk
Manual analysis reduced from days to minutes by routing tasks to the right LLM model
Complete audit trail satisfying compliance and regulatory requirements
Centralized cost tracking replacing scattered individual AI subscriptions
Department-specific AI behaviors through custom system prompts, improving output relevance
Zero sensitive data leakage through the local screening layer

Technologies and Tools

Built with a modern stack designed for security, scalability, and seamless integration with multiple LLM providers.

React

Node.js

PostgreSQL

OpenAI

Python

Docker

Results & Impact

Measurable outcomes that drove real business value

LLM Models

GPT-4, GPT-4O, and Perplexity available in one interface

Data Control

All prompts screened locally before reaching external LLMs

Access Model

Department-level controls with per-role system prompts

Audit Trail

Full logging of every interaction across the platform

Client Feedback

"We developed a dating mobile application with Angular Minds, using Ionic and Node.js. The app has brought immense satisfaction, redefining trust in relationships and empowering users to form deeper connections. Angular Minds has exceeded our expectations, and we recommend their services!"

David G.•Founder of Baelist LLC.

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