Case Study: Modernization

Rescuing Legacy Data with
Intelligent AI.

We transformed a 20-year-old "Black Box" application facing shutdown into a modern, queryable cloud asset using Azure Cognitive Search and OpenAI.

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The "Black Box" Dilemma

Legacy Risk

A business-critical application developed 2 decades ago was facing forced shutdown. No documentation existed, creating a massive risk for data loss.

Scope Ambiguity

Lack of signed-off scope and system understanding posed a challenge for cloud migration, with unstructured documents trapped in the old system.

The AI Transformation

We replaced manual digging with an intelligent, automated pipeline.

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Structure the Unstructured

Using Azure Form Recognizer, we ingested decades of PDF/Word documents and transformed them into structured, readable data formats.

Ingestion Phase

Cognitive Indexing

Implemented Azure Cognitive Search to create a powerful index. This allows users to filter, explore, and "google" their own internal project data instantly.

Processing Phase

Conversational Querying

Integrated OpenAI Playground. Users can now ask questions like "What was the budget for Project X?" and get accurate answers with citations.

Interaction Phase

Measurable Impact

🔐

Single Sign-On (SSO)

Enhanced security and user convenience. Data is now protected behind modern auth standards, replacing the legacy insecure login.

Speech-to-Text

Added accessibility layer allowing users to query data via voice commands.

Citation & Trust

Every AI response includes a direct link to the source document, ensuring 100% transparency.

Economic Sustainability

Automatic scaling mechanisms optimize resource utilization. We moved from fixed legacy server costs to a flexible, pay-as-you-go cloud model.

Azure
Scalability
Auto
Resource Mgmt