Creating A Second Brain For The Enterprise: ChatGPT Enterprise Deployment for a Global RPO Leader

The Challenge

The client’s leadership understood the potential of AI—but lacked a strategy to make it usable, secure, and enterprise-aligned. They faced five core challenges:

• Data Leakage Risks: Employees were using public ChatGPT interfaces to process proprietary candidate and client data

• Lack of Contextual Awareness: GPTs couldn’t access the ATS, HRIS, CRM, or ticketing tools where key data lived

• Knowledge Silo Paralysis: SOPs, HR policies, and process docs were locked away in SharePoint and Confluence, with no easy way to retrieve them

• Operational Drag: HR and IT teams were overrun by repetitive queries, reducing capacity for strategic work

• No Governance or Scalability Model: There was no controlled way to connect AI with business systems, nor manage context delivery to the model

The Solution

thinkbridge architected and implemented a modular AI system built around secure access, real-time data, and domain context orchestration.

This included:

1. Secured AI Infrastructure

• Deployed ChatGPT Enterprise within a private Azure network

• Integrated SAML-based SSO for role-based access• Auditable logs and full observability for IT and compliance teams

2. Real-Time Business System Plugins

We built OpenAPI-compatible plugins to securely connect ChatGPT with:

• ATS – for candidate status and requisition tracking

• HRIS – for onboarding, employee, and benefits queries

• CRM – for client engagement and sales data

• ITSM tools – for ticket lookups and support status

This allowed natural-language interactions such as:

3. Unstructured Knowledge Retrieval (RAG + Pinecone). We implemented a Retrieval-Augmented Generation (RAG) pipeline using:

• OpenAI embeddings

• LangChain orchestration

• Pinecone vector DB

This made internal documents—from policies to process guides—searchable via semantic queries.

ChatGPT now responded with relevant, cited content, like: “Who approves international relocation packages?” or “Summarize the background check escalation SOP.”

4. High-Speed Caching Layer for Cost Optimization. To reduce inference overhead, we implemented:

• Redis for fast memory caching

• PostgreSQL for structured query logs

• Frequently asked queries were cached—cutting redundant API calls by over 60% and improving latency across the platform