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Legacy-to-AI modernization: your strategic path to AI-ready systems

AI efficiency comes at a price, and the price is legacy system modernization for AI. For decades, Azati has been turning legacy architectures into cloud-native and then AI-native platforms. What we do isn’t just about speeding up systems for AI adoption. Azati excels at operationalizing decision-making, that is, translating it into automated, validated, and traceable workflows. Expect Azati’s hands-on expertise in regulated BFSI and Energy industries to be embedded in your infrastructure, as we engineer with regulatory requirements in mind, from DORA and NIS2 to the EU AI Act.

Check my AI readiness
8–16 weeks
to an AI-ready architecture
60–87%
fewer critical bugs
100%
of AI decisions logged with a full audit trail

Signals you need to modernize legacy systems for AI

Azati has witnessed tens of rigid systems killing AI adoption. The reality is that the software landscape has shifted, and simple maintenance or lift-and-shift cloud migrations are no longer a feasible path. Legacy to AI modernization becomes imperative as a surefire springboard to AI efficiency.

80%

of enterprises will use AI-native development platforms by 2027, as Gartner predicts

74%

of midsize companies identify legacy software as their primary operational bottleneck

When to consider AI-ready legacy modernization

Honesty time. The Azati team can say from the outset that your AI efforts will hit the buffers without legacy-to-AI modernization. From our experience, most big companies that fail to win from AI implementation struggle with:

  • Having legacy systems with a monolithic architecture that just can't scale
  • No real-time data flowing into their systems
  • Poor data quality, so AI models can't even get started
  • Inability to embed AI into workflows in a meaningful way
  • Absence of a compliant AI infrastructure (DORA, NIS2, or EU AI Act-aware AI systems)

Why trust Azati: milestones that bridge successful AI modernizations

2002

Azati was created

50+

successful AI projects

380+

projects in the portfolio

40+

legacy modernization projects

Europe & USA

delivery regions covered

BFSI, Energy, Oil & Gas, Life Sciences

Top active market sectors

NIS2, DORA, KYC, AML, GDPR

regulatory requirements in mind

ISO 27001, 27701, 9001

accreditation ETA 2026

Azati is a strategic AI-ready legacy modernization partner

For corporate-grade giants, mid-market business disruptors, and beyond, Azati modernizes legacy systems for AI, accelerates AI deployment in enterprise, and ensures API-first architectures and AI systems are designed for the EU AI Act requirements.

Azati doesn’t just modernize legacy code for AI. We operationalize AI decisions

Most legacy-to-AI modernization projects stop at the architecture level. Azati goes further. We ensure that AI actually powers real enterprise workflows. This is beyond demos. This workflow-level AI Integration teaches your system to make reliable decisions.

  • AI outputs are validated against business rules
  • Workflows are orchestrated across systems
  • Decisions are traceable and auditable
  • Human-in-the-loop is in, exactly where required
Assess ROI from strategic legacy to AI modernization

The Azati team specializes in environments where failure breaks the bank. Our job is to gear your AI systems up for regulatory requirements, audits, and operational resilience.

Azati’s specialty is modernizing high‑risk, AI-driven systems

The major part of Azati’s experience is engagements across regulated industries where teams modernize mission-critical platforms. From banking, fintech, and insurance, to oil and gas, healthcare, and more enterprise systems, we have your back.

Improvement snapshot

Critical bug reduction

60–87% fewer

Regression speed

2–3X faster

Time to production AI

30–50% reduced

System performance

~20% faster

response time in complex queries

Explore Azati’s track record

What Azati has delivered

Get a benchmark of your platform against real modernization and AI readiness metrics.

See how your system compares

Azati’s industry-specific AI-ready legacy modernization solutions

BFSI & Oil & Gas

Banking, Financial Services, Insurance & Petroleum

Typical issue

COBOL systems blocking digital upgrades, alongside strict regulatory requirements.

Azati’s solution

  • Phased AI modernization of legacy systems, preserving business continuity
  • Compliance-conscious architecture (DORA, NIS2, EU AI Act)
  • Data pipeline architecture for AI from legacy databases
  • Detailed operational resilience documentation

Your outcome

Modern, auditable, and AI-capable systems without regulatory risk.

Enterprise Software

Enterprise software companies

Typical issue

AI adoption challenges enterprises face are that monolithic architectures prevent AI feature integration, besides poor data readiness for AI.

Azati’s solution

  • Monolith to microservices migration
  • API-first architecture
  • AI agent integration layers
  • Scalable cloud infrastructure

Your outcome

Product differentiation through AI capabilities that your enterprise-grade competitors can’t match.

Zooming in on Azati’s legacy modernization and AI expertise

Azati’s AI workflow automation services aren’t about building AI from the ground up. Instead, our team helps clients flag vulnerabilities, fine-tune the processes, fix what’s wrecked, and build exactly what’s required.

Legacy Accounting System Modernization
Insurance & Enterprise Systems

Legacy Accounting System Modernization

3X faster system performance
50%+ reduction in manual operations
0 downtime during migration and rollout
  • PHP
  • JavaScript
  • MySQL
  • REST APIs
  • Legacy modernization

⚡ Pain Points We Tackled

The client relied on a legacy accounting system built on outdated PHP architecture, which limited scalability, slowed down performance, and required significant manual effort for routine operations. The system struggled with a rigid monolithic structure, slow data processing and reporting, high dependency on manual workflows, and difficulty integrating with modern tools and services. Any attempt to modernize carried a high risk of disruption to business-critical financial operations.

Our Approach

Azati re-architected the legacy system into a modern, modular platform, ensuring continuity of operations while enabling future scalability and AI readiness. The modernization strategy focused on gradual refactoring instead of full replacement, introducing API-based integrations, improving data processing pipelines, and preserving business logic while upgrading architecture. The transition went without downtime, allowing the client to continue operations seamlessly.

Applied Methods and Practices

  • Incremental modernization: Refactored the monolithic system step by step to reduce risk and avoid downtime.
  • API layer introduction: Built REST APIs to enable integration with external systems and future AI components.
  • Data workflow optimization: Improved data handling and processing speed across accounting operations.
  • Manual process reduction: Identified and automated repetitive accounting tasks to reduce operational overhead.
  • System stabilization: Ensured consistent performance and reliability during and after modernization.

Solution Features

  • Modernized Architecture: Transition from a legacy monolith to a more flexible, maintainable system architecture.
  • Improved performance: Faster data processing and reporting across accounting workflows.
  • Integration-ready platform: API-first approach enabling future integrations and AI adoption.
  • Reduced operational load: Less manual work required for routine accounting processes.
  • Zero-downtime migration: Continuous system availability during the entire modernization process.
Read Full Case Study
AI Process Orchestration for a Legacy Claims Workflow
Banking & Finance

AI Process Orchestration for a Legacy Claims Workflow

3X better case throughput
61% faster case resolution
100% of AI decisions with a full audit trail
  • Python
  • Elasticsearch
  • OpenAI
  • Oracle DB
  • REST APIs

⚡ Pain Points We Tackled

The loan amendment process at this regional bank was a mess. Case officers had too much on their plates: juggling three legacy systems, with tons of manual effort every time the work changed hands. Most of their routine consisted of pulling data and double-checking details between systems, while they could actually review cases and make decisions. Add to this the restriction to automate anything without messing with the main platforms.

Our Approach

Azati developed AI process automation middleware that connects all three existing platforms, using APIs and RPA bridges to manage the data flow. The AI handles and packages each case from start to finish, enabling human officers to still make the final call on any regulated decisions. The team kicked off automation with one standard case type and, as the compliance team got more comfortable, rolled out support for new case types every quarter.

Applied Methods and Practices

  • Connects multiple systems using APIs and RPA to keep everything in sync.
  • Screens eligibility with both rules and machine learning.
  • Keeps humans involved wherever the rules say they’re needed—no skipping the important checkpoints.
  • Rolls out automation gradually, case by case, so changes aren’t overwhelming.

Solution Features

  • Pulls data from different sources, double-checks it, and puts everything together for each case.
  • Uses AI to help judge eligibility and risk, and flags anything fishy for review.
  • Routes approvals automatically and tracks progress to make sure nothing falls behind.
  • Keeps an unchangeable audit log for every case, ready to hand over if regulators ask.
Read Full Case Study
Secure, locally hosted, corporate-ready LLM
Professional Services

Secure, locally hosted, corporate-ready LLM

100% data within controlled infrastructure
40% faster access to internal knowledge
0 exposure of sensitive information
  • Gen AI
  • ML Engineering
  • Secure APIs
  • Private LLM
  • Open WebUI

⚡ Pain Points We Tackled

The client needed to improve internal knowledge access across teams, but faced strict requirements around data security, confidentiality, and compliance. Traditional approaches (public LLM APIs or external AI tools) were not viable due to the risk of sensitive data exposure, lack of control over data processing, audit limitations, and fragmented internal knowledge sources. The challenge was to enable AI-powered knowledge retrieval without compromising security.

Our Approach

Azati developed a regulations-aware, secure, private LLM-based system deployed within the client’s controlled environment. The solution focused on: keeping all data and processing inside a secure infrastructure, enabling fast semantic search across internal knowledge bases, structuring data for AI consumption, ensuring traceability and controlled access to information. This allowed teams to use AI capabilities while maintaining full ownership and control of data.

Applied Methods and Practices

  • Private LLM deployment: Implemented a secure language model within the client’s infrastructure to eliminate exposure of external data.
  • Vector-based search: Enabled semantic search across documents and internal data sources for faster information retrieval.
  • Access control & security layers: Ensured only authorized users could access specific data and AI-generated outputs.
  • Data structuring for AI: Prepared and organized internal data to improve the relevance and accuracy of responses.
  • API-based integration: Connected the system with internal tools and workflows for seamless adoption.

Solution Features

  • Secure AI environment: All data processing happens within controlled infrastructure — no external leakage.
  • Fast knowledge retrieval: AI-powered semantic search reduces time to find critical information.
  • Controlled access: Role-based access ensures that sensitive data is visible only to authorized users.
  • Auditability: Traceable interactions and data usage for regulatory requirements and governance.
  • Integration-ready: Easily connects with internal systems and enterprise workflows.
Read Full Case Study
AI-Powered Data Recognition for an Oil & Gas Enterprise
Energy, Oil & Gas

AI-Powered Data Recognition for an Oil & Gas Enterprise

40% better data recognition accuracy
30% less manual data processing effort
2X faster data extraction and analysis
  • Python
  • Machine Learning
  • Computer Vision
  • Elasticsearch
  • REST APIs

⚡ Pain Points We Tackled

The client needed to process large volumes of unstructured industrial data (documents, schemes, technical records), but faced low accuracy of manual data extraction, time-consuming processing workflows, inconsistent data formats, and difficulty integrating extracted data into operational systems. These issues slowed down decision-making and increased operational costs.

Our Approach

Azati developed an AI-powered data recognition system to automate the extraction, structuring, and validation of industrial data. The solution focused on: applying machine learning and computer vision for data extraction, structuring unstructured data into usable formats, integrating outputs into existing workflows and systems, continuously improving accuracy through validation and feedback loops. This enabled faster and more reliable data processing at scale.

Applied Methods and Practices

  • AI-based data extraction: Used machine learning and computer vision to extract information from complex documents and technical files.
  • Data structuring & normalization: Converted unstructured inputs into standardized, system-ready formats.
  • Validation & feedback loops: Improved model accuracy over time through continuous validation and refinement.
  • Workflow integration: Embedded AI outputs into operational systems via APIs.
  • Manual and automated QA: Combined automated processing with manual validation for critical data points.

Solution Features

  • Automated data recognition: AI extracts and processes data from diverse industrial sources.
  • Improved accuracy: Higher reliability compared to manual or rule-based approaches.
  • Faster processing: Reduced time required for data extraction and analysis.
  • Integration-ready outputs: Structured data ready for downstream systems and workflows.
  • Scalable architecture: Handles growing volumes of industrial data efficiently.
Read Full Case Study
Legacy E-Health Portal Modernization for an International Software Integrator
Healthcare

Legacy E-Health Portal Modernization for an International Software Integrator

50% reduced manual data handling
35% faster cross-system data exchange
100% requirements-aligned data flow across integrations
  • Java
  • REST APIs
  • HL7 FHIR
  • Spring Security
  • Enterprise Integration

⚡ Pain Points We Tackled

The client needed to integrate multiple healthcare systems and data sources into a unified eHealth platform. However, they faced fragmented systems with incompatible data formats, manual data transfers between platforms, strict compliance requirements for sensitive health data, and unreliable cross-system synchronization. These issues created delays, increased operational overhead, and risked data inconsistencies in critical healthcare workflows.

Our Approach

Azati designed and implemented a secure integration layer that connects disparate healthcare systems into a unified platform. The solution standardizes data exchange using healthcare protocols, enables real-time synchronization across systems, ensures secure handling of sensitive medical data, and reduces manual intervention through automation. This creates a reliable, regulations-aware, and scalable integration ecosystem.

Applied Methods and Practices

  • Standards-based integration: Implemented HL7 FHIR protocols to ensure interoperability between healthcare systems.
  • API-driven architecture: Built REST APIs for seamless and scalable system communication.
  • Data validation & consistency checks: Ensured accuracy and synchronization of patient and operational data across systems.
  • Security & regulations controls: Applied strict access control and data protection measures for sensitive information.
  • Workflow automation: Reduced manual data handling through automated data exchange processes.

Solution Features

  • Unified data exchange layer: Centralized integration across multiple healthcare systems.
  • Real-time synchronization: Consistent and up-to-date data across platforms.
  • Compliance-conscious architecture: Secure handling of sensitive healthcare data aligned with regulatory requirements.
  • Reduced manual work: Automated processes replacing manual data transfers.
  • Scalable integration framework: Supports future system extensions and integrations.
Read Full Case Study

Azati delivers tangible strategic advantages

To Azati and our clients, AI isn't a feature anymore; it's a core infrastructure. We're well aware that winning companies treat modernization as an AI architecture foundation, not a cost center. They tend to partner with vendors who understand business outcomes, not just code. Ultimately, leaders prioritize compliance-conscious AI architecture from day one, choosing partners with clear risk mitigation strategies.

1. Azati ensures EU-based AI deployment, with local LLMs

  • Deploy AI, making sure your data doesn't leave the EU
  • Comply with GDPR
  • Embed your LLMs in Europe, right where you need them, with continuity guarantees

Our approach focuses on demonstrating how AI-ready infrastructures behave in practice, helping clients assess expected performance rather than relying on assumptions.

Andrew Babkin
Andrew Babkin Azati Co-Founder

Azati's EU and US legacy-to-AI modernization strategies

EU

Azati guarantees compliance-conscious architectures and AI-ready legacy modernization in Europe

Champion your EU regulations-aware architecture advantage. Azati's EU legal entity and distributed teams provide risk mitigation and regulatory continuity you can't get from US-only, Asian or LatAm vendors.

Azati's EU legacy-to-AI modernization focus: Java ecosystems, BFSI sector (banking, finance services, and insurance), and engineering with regulatory requirements in mind.

Challenges Azati helps handle in the EU

  • Problems with monolithic architectures and COBOL systems that block digital modernization.
  • Design for EU AI Act requirements: Within high-risk sectors, AI requires documentation, audit trails, and human oversight.
  • DORA- and NIS2-aligned architectures: From 2024/2025 on, every financial entity must prove operational resilience.
  • No established AI governance framework in place, making EU AI Act readiness difficult to demonstrate

Azati's EU legacy-to-AI value proposition

Secure EU AI Act-aware Java infrastructure modernization.

What Azati delivers to modernize legacy to AI

  • Long-term support: Continuous engagement model with retained engineering knowledge across audit cycles
  • Compliance-conscious AI architectures: Governance frameworks aligned with DORA, NIS2, and EU AI Act requirements.
  • Local cloud migration: EU-based infrastructure ensuring data privacy, data residency and handling.
  • EU infrastructure advantage: EU-based AI deployment without US cloud data transfer.
US

Azati's speed-to-market modernization for the US

Gain a sprinter release speed. Azati doesn't sell ideational potential. Ours is to deliver AI operating models already proven in production environments.

Azati's US legacy-to-AI modernization focus: .NET ecosystems, Azure integration, and rapid AI agent deployment.

Challenges Azati helps handle in the USA

  • Need to deploy AI agents into production now, not next quarter
  • Azure-native architectures that require seamless AI integration
  • Competitive pressure demanding 40%+ faster time-to-market

Azati's US legacy-to-AI value proposition

Accelerate AI agent deployment in your Azure stack by 40%.

What Azati delivers to modernize legacy systems to AI

  • .NET modernization squads: Teams that turn legacy systems into AI-ready platforms.
  • Ruby for rapid prototyping: Launch products "yesterday" with operational patterns we've shipped across 50+ AI projects.
  • Azure-native AI integration: Seamless deployment of AI agents into existing .NET infrastructures.
  • Production-ready AI: Not sheer experiments, but measurable ROI, from the get-go.

How to modernize a legacy system for AI adoption, fast?

Azati helps gain a quick understanding via architecture assessment, AI readiness evaluation, and a clear modernization plan.

Get your AI modernization roadmap

The Azati engagement model difference: strategic co-innovation

What makes Azati your modernization-to-AI partner for building advanced AI ecosystems? First, we are not an AI consultancy without implementation capability, or a US-only vendor. As your EU-based co-innovator with US market expertise, Azati aligns with your business goals and focuses on production with measurable ROI, from day one.

Azati doesn't sell developer hours; it provides business outcomes

For modernization engagements, we operate as a co-innovation partner, combining engineering, architecture, and AI expertise into integrated squads.

Azati model: co-innovation

  • Deploy modernization squads that upgrade systems
  • Understand business goals and architect solutions
  • Deliver measurable ROI and AI readiness
  • Strategic partnership

Azati’s proven modernization-to-AI framework

The Azati modernization squad takes your old systems and turns them into the foundation of an AI-ready enterprise. Meaning, we get AI out of the lab and into production, and break up your slow, monolithic system into loose, flexible services.

  1. Audit

    AI readiness audit

    • Legacy system analysis (COBOL, monoliths, technical debt)
    • AI capability gap identification
    • Architecture review against EU AI Act, DORA, NIS2
    • ROI projection and business case development
  2. Team

    Right modernization squad

    • Solution architects
    • Cloud migration specialists
    • AI integration engineers
    • Senior architects familiar with regulatory frameworks
  3. Delivery

    AI-ready platform delivery

    • Microservices architecture
    • Cloud-native infrastructure (EU or Azure-native US)
    • AI agent integration points
  4. Optimization

    Implementation & optimization

    • AI agent deployment
    • Human-in-the-loop workflows (EU AI Act aware)
    • Performance monitoring and ROI measurement
    • Continuous optimization

Get the hang of where your system stands and what it takes to make it AI-ready

  • Architecture and risk analysis
  • Data and AI readiness evaluation
  • A clear modernization roadmap
Start with a modernization & AI readiness assessment

Why enterprises trust Azati to get it right

Enterprises choose Azati for legacy-to-AI modernization because of our combined engineering depth and AI/ML expertise built up across 23 years of regulated-industry delivery.
Clutch Badge
"What might take me three or four days to figure out how to do, Azati can do in a couple of hours because of their existing knowledge."
Axel Sturmann
IT Manager, Venstar Exchange
5 star rating
"The app has never crashed, which is a testament to the quality of the code they put into this."
Enrique Franklin
Co-founder at HOPNBR
5 star rating
"Azati shows great experience, knowledge, and professionalism. We are well satisfied with their work and are planning to include Azati in future development and operation."
Sebastian Schmidt
COO at MusicDNA
5 star rating
"Our users love the portal, which is the reason we've been successful."
Martin Goffman
CEO at SequenceBase
5 star rating
"They expected pain points and responded to our needs, flexibly adapting to the ever-changing scope or requirements."
Liza Sudareva
CMO at LoveMobile
5 star rating

Azati’s AI talent recognition

Our track record in the toughest spaces has Azati making waves in every AI dimension that contributes to a modern API-driven enterprise.

Clutch Top Company 2023 — Natural Language Processing Clutch Top Company 2023 — Artificial Intelligence Clutch Global Fall 2023

From legacy systems to AI-ready platforms

Up for modernizing legacy into AI-ready? The companies winning in 2026-2027 are those who treat AI as infrastructure, not an experiment. Legacy modernization isn't a prerequisite for AI. Instead, it is an AI strategy. Every quarter you delay is market share lost to competitors who modernized first.

Azati can help with the first steps:

  1. 1

    Schedule your AI readiness audit

    30-minute consultation to assess your modernization needs.

  2. 2

    Review your regulatory requirements gaps

    Where are you exposed? Are you EU AI Act-, DORA-, and NIS2-aware?

  3. 3

    See our production AI models

    Case studies with real ROI metrics.

  4. 4

    Deploy your modernization squad

    Turn legacy systems into AI-ready platforms in 90 days.

Common questions answered

In a nutshell, we rework the architecture, fine-tune the data pipelines, and make sure AI integration is seamless from the get-go.

In our experience, it all starts with having the right architecture in place, proper data pipelines, and the right approach to integration.

The short answer is to implement governance, auditability, and human oversight in AI systems.

To Azati, the typical timeline of legacy modernization for AI adoption is 8–16 weeks for initial upgrades.

Azati operates as a co-innovation partner. Our cross-functional team includes architecture, QA, AI, and backend engineers who deliver ownership of outcomes, not just tasks. That is, Azati's endgame is your product and business goals, supercharged with faster delivery, fewer handoffs, and higher accountability.

Your strategic wins:

  • AI initiatives that actually reach production
  • Minimized operational risk
  • Faster time-to-value from legacy to AI modernization
  • AI-ready systems adapted for long-term scaling

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