Move decades of data without carrying decades of problems into the new system.
Legacy migrations are rarely just a transfer problem. Years of duplicated records, inconsistent structures, missing relationships, obsolete formats and previous migration errors can follow an organisation from platform to platform. AO uses AI-assisted data engineering to discover, map, clean, reconcile, transform and validate complex legacy datasets before and during migration into modern systems.
What we're solving
- Enterprise technology modernisation often exposes decades of accumulated data problems.
- A new ERP, core platform, claims system, CRM or enterprise application may be technically ready, while the information required to operate it remains fragmented across databases, files, archives and predecessor systems.
- Previous migrations may have copied historical problems rather than resolving them.
- The result can include incorrect customer information, duplicate records, broken relationships, billing discrepancies, claims issues, inconsistent statements, missing history and extensive manual reconciliation.
- Traditional migration approaches can become extremely slow when the organisation is dealing with very large datasets, undocumented source systems and compressed transformation deadlines.
How we deliver
AO deploys an AI-assisted data migration and modernisation capability into an architecture approved for the customer's environment. The system combines data engineering, automated analysis and AI-assisted reasoning to inspect source information at scale, understand relationships and patterns, propose mappings between legacy and target structures, identify anomalies, support data cleansing and reconciliation, and continuously validate the migration result. AI does not blindly rewrite production information. Transformation rules, confidence thresholds, exception handling, approval workflows and reconciliation controls are designed around the engagement. High-risk or ambiguous records can be isolated for human investigation rather than silently migrated. The result is a controlled migration process designed to improve the quality of the information entering the new platform instead of simply transferring historical problems into it.
What you receive
Legacy data discovery and profiling
Automated schema and field analysis
Source-to-target data mapping
Duplicate and anomaly detection
Record matching and entity resolution
Data cleansing and standardisation
Transformation-rule generation
Relationship and dependency discovery
Historical-data reconciliation
Exception and confidence management
Migration simulation and dry runs
Automated migration validation
Before-and-after reconciliation
Data-quality scoring
Migration dashboards and audit evidence
Data lineage and transformation documentation
Support for structured and, where appropriate, unstructured information
Large-volume and multi-source migration programmes
Business outcomes
- 01Reduce the manual effort required to understand complex legacy information.
- 02Improve the quality of data entering new enterprise systems.
- 03Identify historical inconsistencies before they affect the new platform.
- 04Reduce migration and reconciliation risk.
- 05Accelerate migration programmes with extremely large datasets.
- 06Create an auditable record of mapping, transformation and validation decisions.
- 07Reduce reliance on undocumented institutional knowledge.
- 08Give business and technology teams visibility into exceptions instead of hiding uncertainty.
Stack & partners
Your enterprise data does not need to become public AI training data.
AO can design AI migration solutions around private cloud, customer-controlled infrastructure, approved enterprise AI services or on-premise environments depending on the customer's security, regulatory and architectural requirements. Customer information is only processed within explicitly approved boundaries. Every engagement is architected around appropriate data residency, identity, access control, encryption, logging, auditability and model-governance requirements.
- Customer-approved AI infrastructure and data-governance requirements
- Explicitly authorised access to systems and information
- Data residency, retention and logging defined per engagement
- Human approval over transformation and remediation decisions
More than 100 years of business history. More than 7 TB of data. One compressed migration window.
A century-old organisation entered its third major technology refresh after previous system migrations had left historical information inconsistently mapped across predecessor platforms. Following implementation of the latest system, legacy data issues were contributing to downstream operational problems affecting areas such as billing, claims and statements. AO deployed an AI-assisted data migration and data-quality capability inside the customer's controlled technology environment to analyse, map, clean, reconcile and prepare more than seven terabytes of historical information within a highly compressed programme timeline. The objective was not simply to move the data again. It was to understand and improve the information before it entered the next stage of the organisation's technology estate.
Frequently asked
Can AO migrate data from multiple legacy systems?
Yes. Multi-source programmes are common. AO profiles each source system, resolves overlapping and conflicting records across them, and produces a consolidated source-to-target mapping with reconciliation evidence for each source.
Can this work with very large datasets?
The approach is designed for large volumes. Analysis, mapping and validation are automated and run in batches, which is where AI-assisted engineering makes the largest difference against compressed timelines. Actual throughput depends on the source systems, infrastructure and agreed scope.
Does customer data have to be sent to a public AI platform?
No. The architecture is designed around your requirements. Private cloud, customer-controlled infrastructure, approved enterprise AI services or on-premise deployment models can be supported depending on the engagement, and processing stays within explicitly approved boundaries.
How does AO handle uncertain mappings?
Mappings carry confidence levels. Ambiguous or high-risk records are routed to an exception queue for human investigation and approval rather than being migrated silently.
Can AO help if a previous migration was unsuccessful?
Yes. Remediating a prior migration is a frequent starting point. AO profiles what actually landed in the current platform, compares it against predecessor sources and identifies where mapping, truncation or de-duplication problems occurred.
Does AO only move data, or can it clean and reconcile it?
Cleansing, standardisation, entity resolution and reconciliation are part of the capability. The objective is to improve the information before it enters the new platform, not simply transfer it.
Can the process be audited?
Yes. Mapping decisions, transformation rules, exceptions, approvals and before-and-after reconciliation results are captured as evidence and can be reported to programme governance and auditors.