AI Modernization Services

AI modernization means modernizing legacy software using AI, then restructuring it so AI can work on it afterwards. In practice that means comprehension first, recovering business rules out of undocumented systems, followed by AI-assisted refactoring, generated regression tests, and documented interfaces that make legacy data reachable by models and agents. 

It assesses your legacy estate, recovers the business rules hidden in undocumented code, re-engineers the system in incremental slices, and validates that the modernized version behaves identically to the original. A capable partner also tells you which systems to leave alone.

Saigon Technology's published rate for implementation work is $22–$46 per hour, senior-led, with monthly costs per full-time engineer from $3,200 to $10,500 depending on seniority and specialization. Total cost depends on estate size and how much of the original behaviour must be preserved exactly. We scope from an assessment, not a guess.

An AI-readiness assessment typically runs two to four weeks. Beyond that, timelines depend on estate size and how much undocumented logic must be recovered first. We deliver in incremental slices so value lands before the full programme completes, rather than in one high-risk cutover.

An AI readiness assessment inventories your applications, data, and infrastructure, then scores each system against what your AI roadmap requires. It identifies where data is unreachable, where technical debt blocks change, and which systems are worth modernizing first, producing a ranked plan rather than a general recommendation.

Measure your own baseline first. Most vendor claims in this market are unsourced percentages, and none of them describe your estate. The only comparison that means anything is the one between your system before the work and the same system after it.

Capture these before any code changes: 

• Comprehension time - how long it currently takes an engineer to answer "what does this module do?" This is where AI-powered business operations automation and code-analysis tooling move the number most.
• Change lead time and failure rate - how long a small change takes to reach production, and how often it is rolled back.
• Annual maintenance cost reduction - licence, infrastructure, and support spend on the legacy estate, itemized so savings are attributable later.
• Incident volume and mean time to recovery, giving you the real-time production visibility to see whether reliability actually improved.

Then instrument the modernized system so operational efficiency is observable rather than asserted: deployment frequency, test coverage against recovered legacy behaviour, and API latency at the new integration surfaces.

Measuring AI ROI honestly means holding those structured metrics against a pre-modernization baseline. Practical frameworks beat vendor benchmarks here, your baseline is the only comparison that reflects your estate, and they are what turn data-driven decision-making into a defensible business case for the next phase. That is how a modernized platform becomes one of your future-ready platforms on the evidence rather than on the brochure.

Legacy estates we read: COBOL and mainframe batch systems, legacy .NET Framework and classic ASP, older Java and J2EE, monolithic PHP, stored-procedure-heavy SQL Server and Oracle, and the middleware layers wired between them. Naming the source stack matters more than naming the target one, the constraint on any modernization is what can be understood, not what it can become.

Target stack - backend: .NET, Java, Node.js, Python · Frontend: React, Angular, Vue.js, TypeScript · AI/ML: Python, TensorFlow, PyTorch, scikit-learn, LLMs, applied machine learning · Cloud and DevOps: AWS, Azure, Google Cloud, Docker, Kubernetes, Terraform, Jenkins, the cloud-native toolchain we re-platform onto.

Standards and compliance: ISO 9001 (quality) and ISO 27001 (information security), both issued by BSI (UK). Engineering practice aligned to GDPR and PDPA for data protection and privacy, HIPAA and HL7 where healthcare data is in scope, and PCI-DSS and SOC 2 control expectations for payment and financial workloads. Compliance controls - NDAs, role-based access, audit logging, and secure SDLC, apply to every engagement, not only regulated ones.

We generate test suites from the legacy system's actual observed behaviour, not from a specification, then run the modernized build against them. Combined with data-integrity validation and staged cutover with rollback readiness, that gives you evidence of equivalence instead of assurances. There is no single AI modernization framework that removes this step.