Overview
A telecommunications major needed a solution that could counter the delays and inconsistencies in delivery due to high volumes of daily pipeline requests consuming significant engineering capacity, slowing discovery, extending cycle times, and making onboarding inefficient.
MoreA telecommunications major needed a solution that could counter the delays and inconsistencies in delivery due to high volumes of daily pipeline requests consuming significant engineering capacity, slowing discovery, extending cycle times, and making onboarding inefficient.
We implemented an AI-driven data engineering solution powered by Azure’s generative AI capabilities to address these roadblocks. The solution automates code and pipeline generation, accelerating delivery and reducing overall engineering effort.
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Client Background and Challenges
- Siloed documentation and standards leading to slow discovery, inconsistent delivery
- High engineering effort for data pipelines, resulting in increased cycle time per request
- Need for standardization and automation to scale pipeline delivery
- High skill dependency resulting in delivery risk and slower onboarding
Our Scalable, Intelligence-led Solution
We built and deployed an agentic AI–based autonomous pipeline builder across DevOps, enabling seamless delivery from UAT to production:
- Generate code and configurations (YAML/SQL/PySpark) from natural-language pipeline requests
- Use RAG-based Q&A for EDH guidance and patterns
- Auto-derive and validate parameters (mandatory/derived/default) with approval workflow
Business and Community Impact
The AI pipeline automation significantly improved the client’s data engineering operations. By automating, it delivers substantial business value:
- Up to 50% faster pipeline development
- 3–5 hours saved per request with use of agentic AI systems driving operational efficiency and cost savings across the team
- Shorter wait times for pipeline automation, improving SLA performance and reducing engineer workload