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Generating the Pipeline Was Never the Hard Part
Generation solved the part that was never the bottleneck. What still takes months is proving a converted pipeline behaves like the one it replaced, and knowing within minutes rather than quarters when it stops. Xoriant works alongside data engineering teams to close that gap, generating from the contract at the source, gating promotion on parity, and binding detection, diagnosis and remediation into one loop that runs without a person watching it.
Under Contract. Verified. Self-Running.
Every estate carries a conversion backlog estimated in people-years, because every pipeline is read by hand before it is rewritten. Generation changes that arithmetic and then stalls in the same place: a converted pipeline nobody will promote, because nothing proves it behaves like the one it replaced.
Xoriant pairs data engineers who have modernised estates under audit with the ORIAN 10x delivery framework, putting Human Ingenuity and AI (HI/AI) to work on the source contracts, parity harnesses and bounded remediation loops that turn a generated pipeline into a promoted one.
Overarching philosophy of HI/AI
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Where Engineering Becomes a Repeatable Capability
Pipeline Modernisation Under Contract
Autonomous Pipeline Operations
Data Under Contract
Engineering Capacity Release
Pipeline Modernisation Under Contract
Pipeline Modernisation Under Contract
Conversion generated from the contract at the source, with an evaluation harness and parity checks in the build, so a breaking change fails the pipeline rather than surfacing in a report.
Autonomous Pipeline Operations
Autonomous Pipeline Operations
Detection, diagnosis and remediation bound into one loop with a defined blast radius, and promotion gated on parity rather than on whoever is on shift.
Data Under Contract
Data Under Contract
Every source lands with schema, freshness, ownership and sensitivity declared at the door, so what arrives can be trusted before anything is built on it.
Engineering Capacity Release
Engineering Capacity Release
Estate-specific conventions fed to the generator and effort modelled across the estate, converting maintenance load into build capacity instead of a second backlog.
Case Study
Fortifying Application Security for a B2B Data Provider Using the SSDLC Approach
Robust app security results in secured, compliant and risk-free apps.
Case Study
Fortifying the Banking Frontlines with Improved Network and Security
Automated device hardening from weeks to minutes without errors.
Case Study
Faster Big Data Testing and Migration with Metadata-driven Framework
60% Improved Delivery Efficiency Using Test Automation During Migration From SQL Server to Snowflake.
Accelerators
Built to Accelerate What Matters Most
Data.Transform
Generates and converts pipelines from the contract at the source, turning work estimated in people-years into a build problem whose result is checkable rather than merely finished.
Data.Test
Tests the change rather than the output: an evaluation harness and parity checks in the build, so a breaking change never reaches a consumer to be discovered there.
Data.Agent
Binds detection, diagnosis and remediation into one loop instead of three tools and one tired engineer, with what the loop may change bounded so it is acceptable to run.
Data.Observe
Puts pipeline and data-flow observability in place with promotion gated on parity, so quality does not depend on who is on shift.
Achievements
Real Performance, Backed by Numbers
5 to 7x
deployment velocity
30%+
fewer quality issues
65%
compliance effort automated
Keeping You Updated
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FAQ
AI for Data Engineering
What is AI-Led Data Engineering?
AI-Led Data Engineering uses AI to accelerate pipeline development, validation, monitoring, and remediation, helping organizations deliver trusted data faster.
How is AI changing modern data engineering?
AI can automate pipeline conversion, testing, observability, and issue resolution, reducing manual effort while improving data quality and reliability.
How does Xoriant help modernize data estates?
Xoriant combines experienced data engineers, AI accelerators, and the ORIAN 10x framework to modernize pipelines while maintaining governance, parity, and trust.
What are data contracts and why are they important?
Data contracts define schema, ownership, quality, and freshness requirements upfront, helping teams build reliable pipelines and reduce downstream issues.
How does Xoriant ensure pipeline quality during modernization?
Xoriant uses automated parity testing, observability, and validation frameworks to ensure modernized pipelines behave as intended before promotion.
What outcomes can organizations expect from AI-Led Data Engineering?
Organizations can accelerate delivery, improve data quality, automate operations, and increase engineering productivity while reducing maintenance effort.
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