Xebia Demonstrates Enterprise-Ready AI Solution at India AI Impact Summit

Feb 20: Xebia, a 25‑year global leader in digital transformation and technology consulting, participated in the AI Impact Summit 2026, showcasing their enterprise-ready AI accelerators built to significantly shorten modernization timelines, enhance AI reliability, and streamline critical business operations.

What Xebia Revealed at the Summit

1. Agentic Data Pipeline Migrator — From Multi‑Year Migrations to Weeks

Xebia demonstrated how its Agentic Data Pipeline Migrator compresses traditionally long, manual pipeline modernization programs into weeks instead of years.
Key capabilities include:

• Autonomous discovery, analysis, translation, validation, and deployment of legacy pipelines.
• High translation accuracy using multi-agent workflows and continuous self-healing.
• Support for modern platforms such as Databricks, AWS, Azure, and GCP.
• Up to 90% reduction in manual engineering effort, improving migration speed and lowering risk.

This solution drew major attention from data leaders aiming to fast‑track AI adoption.

2. Xebia AI Document Parser — Turning Unstructured Data Into AI‑Trusted Intelligence

Responding to the industry-wide challenge of AI trust, Xebia showcased its high-scale AI Document Parser, capable of converting unstructured enterprise content into structured, machine-readable data.
Summit highlights included:

• Multi-format and multi-modal parsing across PDFs, images, emails, audio, video, and more.
• Enterprise-scale processing of 60,000+ documents or 200GB+ datasets.
• Seamless integrations with SharePoint, Azure Blob, S3, and Azure AI Search.
• Up to 80% reduction in manual data cleanup, accelerating time-to-insight.

Journalists focused on enterprise AI readiness, data quality, and GenAI reliability will find this particularly relevant.

3. GenAI-Powered HR Policy Management Tool — Agentic HR Support at Scale

Xebia also demonstrated its GenAI-powered HR Policy Management Tool, already deployed across its global HR environment.
The system allows employees to get verified, context-aware answers instantly through an agentic architecture.
Key features showcased:

• RAG-based policy lookups with source citations for accuracy and explainability.
• Integration with HRMS for real-time leave balances, attendance reports, and holiday mapping.
• Reduction of HR inbox load by automating hundreds of weekly queries

This tool resonated strongly with media covering HR tech, workplace innovation, and GenAI in enterprise operations.

4. AI in SDLC — Xebia’s AI‑Native Software Engineering Framework

• Transforms the entire Software Development Lifecycle by applying AI‑driven automation from requirements analysis through deployment.
• Increases software delivery speed and productivity significantly, helping teams build and modernize faster.
• Helps organizations shift from experimentation to measurable AI impact, improving engineering outputs and timelines.
• Designed to support enterprises across industries—banking, healthcare, travel, and technology—seeking scalable SDLC modernization

5. Super Agent — Xebia’s Agentic AI Assistant

• Deploys intelligent role‑specific AI agents that automate multi‑step workflows.
• Provides real‑time insights and executes tasks autonomously across functions.
• Strengthens enterprise decision‑making and operational agility.

On this Anand Sahay, Global CEO, Xebia, said,

 “The real breakthrough in AI isn’t experimentation it’s impact. At the AI Impact Summit, we demonstrated agentic systems that compress multi‑year modernization into weeks, convert unstructured content into AI‑trusted data at scale, and deliver accurate, contextual support in real time. Together with our AI‑Native SDLC and Super-Agent capabilities, this is production‑grade AI that helps enterprises move faster, reduce manual effort, and modernize with confidence.”

At the AI Impact Summit, Xebia reinforced that Agentic AI is redefining enterprise transformation, delivering outcomes once considered impossible modernization at unprecedented speed, trusted AI outputs, and operational scalability without added overhead.

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