Introduction & Client Context

The client is a top-tier global professional services network providing audit, tax, and advisory services to massive enterprises and government entities. Operating in a sector where data confidentiality is paramount, the client faces rigorous regulatory frameworks and compliance standards.

With the rapid emergence of Generative AI, the client identified a critical market need: their enterprise customers wanted the productivity gains of Large Language Models (LLMs) but could not risk using public tools like ChatGPT due to data leakage concerns and GDPR requirements. The client needed a partner to engineer a secure, multi-tenant platform that guaranteed data sovereignty ensuring no sensitive information ever left the European Union. TIAC partnered with the client to deliver this commercial-grade solution, bridging the gap between innovation and strict compliance.

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The Challenge

The primary business obstacle was Data Sovereignty and Security. Enterprise clients required absolute assurance that their proprietary data would not be used to train public models or processed on servers outside the EU.

Specific operational challenges included:

• Regulatory Compliance: The solution had to enforce "EU-only" data residency. If a specific model (such as a live web search tool) required data to leave the secure boundary, the system needed to explicitly warn users that confidentiality could not be guaranteed.
• ‍Integration Complexity: The market is fragmented. Clients needed a unified interface to access various models from GPT for reasoning to DALL-E for image generation without managing multiple vendor contracts.
• ‍Tenant Isolation: The architecture had to support individual deployments, allowing end-clients to host the application within their own Azure subscriptions. This ensured they retained full control and visibility over both the source code and their data.

The Team

TIAC integrated directly into the client's operation as a dedicated technical extension of their product team. Since the partnership began in January 2023, the team has scaled from four to seven specialists:

• 6 Developers: Responsible for backend Python engineering, Azure infrastructure management, and complex API integrations.
• ‍1 QA Engineer: Ensuring rigorous testing of data flows and UI functionality.

Notably, the TIAC team operates with high autonomy. There is no TIAC-side Project Manager; instead, our engineers collaborate directly with the client’s Product Owner and DevOps leads. This structure allows for rapid technical decision-making and efficient delivery of complex features without administrative bottlenecks.

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The Solution

The team engineered a robust GenAI platform hosted on Microsoft Azure, designed for flexibility. It can be deployed on the client's central infrastructure or directly into an end-client's private cloud environment.

Multi-Model Integration Strategy Rather than locking users into a single AI provider, the solution aggregates multiple top-tier models via secure API calls. The system filters availability based on compliance needs:

• Reasoning: Azure OpenAI (GPT family) and Mistral.
• ‍Search & Context: Google Gemini and Perplexity (utilized for internet-connected queries).
• ‍Specialized Capabilities: DALL-E for image generation and custom tools for transcription.

Key Business Modules The platform goes beyond simple chat, offering specialized commercial modules ("Use Cases") tailored for professional services:

• Offering Bot: Automates the creation of project proposals and drafts.
• ‍Tailored AI: Enables "Chat with your Data" by loading documents into context for specific queries.
• ‍Document Analysis: Automatically highlights formatting errors or inconsistencies in PDF documents.
• ‍AI Librarian: Powered by Azure AI Search to index and retrieve internal knowledge.
• Autonomous Agents: for multi-step tasks and a direct API Gateway to allow clients to integrate the secure engine into their own internal software.

Engineering for Security The system logic strictly enforces compliance. If a user selects a model that requires data to leave the EU, the interface triggers a mandatory "No Guarantee of Confidentiality" warning, ensuring users make informed risk decisions.

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The Results

The platform has successfully transitioned from a pilot project to a mature commercial product with steady growth over two years.

• ‍Widespread Enterprise Adoption: The platform is currently deployed to approximately 20 enterprise organizations.
• ‍High-Volume Scale: One major deployment serves a consortium of roughly 20 academic faculties in Austria, demonstrating the system's ability to handle complex, multi-user environments.
• ‍Market Penetration: The client has successfully secured contracts in strictly regulated sectors (such as insurance) where AI adoption was previously blocked by data residency concerns.

Tech Stack

Backend & AI

• Python (Flask) served via Gunicorn
• Azure OpenAI, Mistral, Gemini, Perplexity APIs,
• LangChain

‍Cloud & DevOps (Microsoft Azure)

• Azure Container Apps (with dynamic scaling)
• Azure AI Search (formerly Cognitive Search)
• Azure SQL & Azure Managed Redis,
• Azure Blob Storage & Application Gateway
• Log Analytics Workspace

Frontend

• React

Quality Assurance

• Pytest (Backend)
• Cypress (Frontend)

Lessons Learned & Future Direction

The "Wrapper" Myth A key engineering insight was that "standardized" AI libraries often fail in production. While tools like LangChain promise a unified interface, the team found that every model provider (OpenAI vs. Mistral vs. Gemini) handles token counting and cost calculations differently. True enterprise integration requires custom engineering layers rather than relying solely on off-the-shelf wrappers.

Future Roadmap The product roadmap focuses on deeper integration. The team is currently developing Autonomous Agents capable of executing complex workflows independently. Additionally, a direct API Gateway is being built to allow enterprise clients to programmatically call the secure AI platform from their own business systems, bypassing the user interface entirely.

At TIAC, we don't just build software; we navigate complex regulatory and technical landscapes to deliver resilient enterprise solutions. Whether you need to secure your data while using the latest AI models or scale a mission-critical platform, our engineers are ready to partner with you.

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Real people. Real expertise. Real results.

Let’s start a conversation about your next project. Contact us today.

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