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Regulatory Reporting Excellence: How a Global Bank Improved
Accelerating Basel III Compliance
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From Pilots to Payback
Most AI initiatives do not fail on the model. They fail on the journey from pilot to payback - on governance gaps, siloed pipelines and data foundations the business cannot trust. Xoriant builds the backbone that completes that journey, moving enterprises from experimentation to production and from production to agentic intelligence. Every engagement is designed with payback in mind from the first architecture decision, not as an afterthought once the model is live.
The Data & AI Foundation That Powers Every Step Forward.
The enterprises moving fastest with AI are not the ones with the most capable models. They are the ones with strong foundations - governed data, trusted pipelines and well-integrated AI systems which are built to act, not just inform. Xoriant works alongside data & AI teams to build those foundations, combining data modernization, intelligent system design and agentic AI capabilities into a single, coherent discipline that takes enterprises from working pilots to real business outcomes.
We combine deep data engineering expertise with OrianTM platform capabilities and the Orian 10x delivery framework, putting human judgement at the centre of every deployment so AI does not just automate tasks, but drives decisions the business can trust, act on and scale with confidence.
Overarching philosophy of HI/AI
House of Xfactors make it happen
How can we help?
How We Build the Data Foundation for AI-First Enterprises
Data Foundation for AI
Agentic Process Transformation
AI for Data Engineering
AI Trust and Governance
Decision Intelligence
Intelligent Operations
Data Foundation for AI
Multimodal Data Management
Bring structure to complexity: managing structured, unstructured, real-time, streaming data and audit logs within a single governed layer so every data type remains accessible, traceable, and ready to serve AI workloads at enterprise scale.
Ontologies and Metadata Curation
Give your data meaning, not just storage. Ontology modelling, metadata management, semantic mapping, and lineage tracking combine to create an AI-ready knowledge foundation where enterprise data is organised, discoverable, and understood by both humans and agents.
Adoption and Change
Drive AI adoption through change management, upskilling, and communication strategies that embed AI into how teams work every day.
MDM and Data Quality
AI is only as trustworthy as the data behind it. Master data management, golden record creation, deduplication, and continuous validation establish a governed data baseline ensuring the records that feed AI models and analytics are accurate, consistent, and complete.
Context and Knowledge Graphs
Engineer the semantic layer and knowledge graph that give agents shared context, so retrieval is grounded in meaning, not just keywords.
Vectors and Memory
Build the vector and memory stores that let agents retrieve, recall, and reason across sessions instead of starting from zero every time.
Semantic Contracts and Lineage
Set semantic contracts and governed lineage so every data product carries clear meaning and a traceable record agents can be held accountable to.
AI-Led Data Engineering
Stop managing pipelines manually. AI-driven ingestion, integration, transformation, and pipeline orchestration cut engineering overhead, accelerate data delivery, and keep performance consistent as data volumes and complexity scale.
AI Cost Optimization
Make every AI dollar accountable. Token optimization, prompt engineering, model selection, workflow redesign, and resource right-sizing work together, backed by continuous cost analytics that give engineering and finance full visibility into AI spend and the controls to manage it.
Enterprise Meaning Model
Every metric resolves once, for every consumer, because the vocabulary of the business is agreed with a named owner and then served rather than re-derived in each report.
Knowledge Graph and Entity Resolution
Relationships are held as objects rather than joins rebuilt per report, and the same entity under four spellings in five systems is resolved rather than quietly tolerated.
Data as a Product
Two decades of logic is inventoried and published with a named owner, a live SLA and a residency answer, so it is consumed rather than requested.
Agentic Process Transformation
Process Reimagined
Cycle time falls because agents own whole steps of the process rather than tasks inside it, the exception path included. That path is where every earlier automation programme quietly stopped.
AI Economics
Routing, context budget, model choice and data residency are decided at design time by policy, so the bill stops scaling with success instead of being discovered on the invoice.
Software Displacement and Rebuild
A licence retires or a system nobody will touch gets rebuilt, with equivalence proven on a signed golden set before the change window rather than argued after it.
Forward Engineering
Software ships faster with a provenance trail the risk function accepts, so speed does not cost you the sign-off at the end of the cycle.
AI Quality
An AI system can be changed quickly without discovering later that behaviour nobody documented has broken, because the quality bar is a runnable test set in the pipeline.
Compounding AI Accuracy
Every correction a person makes is captured and fed back, so the system is measurably better this quarter than last instead of plateauing the week after go-live.
AI for Data Engineering
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
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
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
Estate-specific conventions fed to the generator and effort modelled across the estate, converting maintenance load into build capacity instead of a second backlog.
AI Trust and Governance
Decision Evidence
Any decision can be reproduced and defended on demand, from a record generated as the work runs rather than assembled by hand when someone finally asks for it.
Continuous Assurance
One definition of healthy across agentic, data and ML flows, validated before it ships, re-validated on a schedule, with every correction fed back.
Governed Meaning
A metric or definition cannot drift without a named owner approving the change, so reporting and agents finally return the same answer to the same question.
Decision Intelligence
Governed Metrics and Decision Models
The decision is modelled, not just the dashboard, so what the business does when a number crosses a line is as governed as the number itself.
Causal and Prescriptive Analytics
What actually moved the number, and what to do next, with effect estimates published alongside their confidence and assumptions rather than a correlation from last quarter.
Decision Auditability
How a decision was reached is retained as versioned reasoning, so an after-the-fact review is answered from a record rather than reconstructed from memory.
Intelligent Operations
Agent Lifecycle Management
Everything running carries a named owner, a risk tier and a review date, and the delivery pipeline refuses to ship an agent missing any of the three.
FinOps for AI
A cost per completed unit of work, attributed to a team and a process, with budgets enforced at the gateway where they bite rather than reported after the fact.
Agent Operations and Vulnerability
Run effort falls, and a newly disclosed weakness is found, assessed and fixed across the estate at pace with regression proof attached to every automated fix.
Release Assurance
A rebuild reaches its change window with proof that it behaves like the thing it replaces, so the change board gives a first-pass approval instead of a deferral.
Case Study
Regulatory Reporting Excellence: How a Global Bank Improved
FR Y-14 Compliance
How a global bank improved FR Y-14 reporting, enhanced adjustment traceability, and strengthened consent order compliance with a governed data framework.
Case Study
Modernizing Financial Systems for a Global Bank
Ahead of Bulgaria’s legally mandated Euro (EUR) adoption on 1 January 2026, a leading global bank faced a non-negotiable regulatory deadline requiring enterprise-wide currency conversion.
Case Study
Accelerating Basel III Compliance
with First Time Right Regulatory Submissions
How a global bank leveraged GenAI to automate 7,000+ rules, standardize reporting, and minimize penalty risk across jurisdictions
Accelerators
Built to Accelerate What Matters Most
Achievements
Real Numbers, Real Impact
95%
RCA Accuracy
99.7%
Faster Debugging
$7M
Incremental revenue via production AIOps
Keeping You Updated
FAQ
Data & AI
What do data and AI services include for large enterprises?
Enterprise data and AI services cover data modernisation, AI model development, and production operations combining cloud engineering, platform engineering, and DevOps to build scalable, secure, and reliable data and AI platforms.
What is AI-first enterprise transformation and how does it differ from traditional analytics?
AI-first transformation re-architects workflows for AI adoption from the ground up. It integrates AI strategy and implementation with cloud modernisation, then operationalises AI with MLOps and monitoring so models perform in production, not just in pilots.
How do enterprise data and AI solutions unify data to power trustworthy AI?
Governed data foundations built on modern integration, automated data quality, cataloging, and lineage enable reusable datasets across systems giving AI the trusted, unified inputs it needs to produce reliable, auditable outcomes.
What capabilities are needed to scale AI-driven analytics from pilot to production?
Scaling AI-driven analytics requires DataOps and MLOps pipelines, observability, and automated testing. Production guardrails, governance workflows, and performance tuning keep systems stable and continuously improving over time.
What does enterprise AI modernisation mean for organisations with legacy data platforms?
Enterprise AI modernisation starts with migrating legacy platforms to cloud-native, AI-ready architecture. Security by design, performance engineering, and automated quality practices reduce risk while accelerating delivery across the data estate.
How should companies structure AI strategy and implementation for end-to-end success?
End-to-end AI transformation runs from strategy through implementation integrating modernisation, cloud, data governance, and AI deployment. Repeatable accelerators and quality engineering reduce time to value while ensuring enterprise-grade reliability.
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