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- 20+ Years in Business
- 350+ IT Experts
- 750+ Enterprise Clients
- CMMi Level 3 Certified
- ISO 9001:2015
- Top 1% on TrustPilot
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4.8 on Clutch
Our AI modernization services for intelligent enterprise transformation
Our AI modernization services transform legacy systems, processes, and data into intelligent, scalable environments. We integrate AI where it improves operational efficiency, strengthens decision-making, and establishes a future-ready foundation for continuous innovation, sustainable growth, and long-term enterprise performance.
Legacy System Assessment
We map your systems end-to-end: dependencies, technical debt, security gaps, and undocumented logic. You get a prioritized modernization plan: what to fix first, what can wait, and a realistic cost and timeline. No guesswork, no generic recommendations, just a roadmap based on what's actually in your environment. Every recommendation ties back to a specific finding, not a template.
Automated Code Conversion
We extract business logic from legacy code (COBOL, VB6, old Java) using AI-assisted tools, then convert it to modern languages and frameworks. Tests are generated alongside the conversion, so you can verify behavior didn't change before deployment. Faster than manual rewrites, with far less risk of silent errors. Conversion happens in phases, so you're never running a full rewrite blind.
Application Re-Architecture
We break monolithic applications into independently deployable services connected through APIs, not tight internal dependencies. Mainframe workloads get rehosted, refactored, or rewritten based on what the system actually needs. Result: teams can ship changes without breaking unrelated features, and the architecture can scale with demand. New services get added without touching what's already working.
Data Platform Upgrade
We migrate legacy databases and batch-driven pipelines to platforms built for real-time data access, cleansing and structuring data along the way for AI and analytics. We establish data readiness before models are introduced and embed governance throughout the migration, including ownership, access controls, quality checks, and compliance, creating a reliable foundation for enterprise AI.
Integration Re-engineering
We replace fragile point-to-point connections and overnight batch transfers with event-driven architecture and proper APIs. Systems exchange data in real time, and adding a new connection no longer means reworking existing ones. This cuts integration failures and makes your architecture easier to extend going forward. One broken connection stops taking down the rest of the pipeline with it.
AI-enabled Workflows
We identify where AI can deliver measurable value within existing processes, from anomaly detection and predictive routing to automated approvals, and integrate it directly into the relevant workflows. Rather than adding AI as a standalone layer, we focus on use cases that improve speed, accuracy, and decision-making, with performance measured continuously against defined business outcomes.
Security & Compliance Modernization
We remediate vulnerabilities specific to legacy code: hardcoded credentials, outdated encryption, missing access controls, and build in audit trails your systems currently lack. This brings old systems up to current regulatory and security standards, so compliance reviews stop being a scramble every time one comes around. Fixes are documented as they happen, so audits have a paper trail ready.
DevOps & CI/CD Enablement
We containerize legacy workloads and build automated CI/CD pipelines around them, replacing manual deployment processes with tested, repeatable ones. Releases move from hours or days to minutes, with automated testing gates catching issues before they reach production. Your team ships more often, with less risk each time. Rollbacks become a one-step process instead of a fire drill.
Ongoing Optimization & Support
Post-launch, we monitor performance, identify issues early, and continuously optimize the system as usage evolves. Regular reviews keep priorities aligned with changing business needs, while ongoing tuning prevents recurring technical debt. This ensures the system remains reliable, efficient, and effective over time, continuously improving as new requirements, usage patterns, and operational priorities emerge.
AI-driven modernization for enterprise transformation
From strategy to deployment, we manage the entire modernization lifecycle, upgrading legacy systems and workflows with AI-driven solutions aligned with your operational requirements, compliance standards, and business objectives to solve complex enterprise challenges.
Banking & Finance
Digital Banking Platforms, Fraud Detection Systems, Intelligent Lending Solutions, Regulatory Compliance Automation, Financial Analytics Platforms, AI Agent for Fraud Detection
Education
Adaptive Learning Platforms, AI Tutoring Systems, Virtual Classroom Solutions, Student Information Systems, Learning Analytics Platforms, AI in Education
Healthcare
Clinical Workflow Automation, AI-Powered Diagnostics, Patient Engagement Platforms, Telehealth Solutions, Healthcare Analytics Systems, AI Agent for Healthcare, Generative AI in Healthcare
Retail
Personalized Shopping Experiences, Inventory Optimization Solutions, Customer Analytics Systems, Point of Sale Integration, Demand Forecasting Solutions, AI Agent for Customer Service
Logistics
Fleet Management Solutions, Route Optimization Platforms, Logistics Automation Systems, Shipment Tracking Solutions, Last-Mile Delivery Applications, AI in Supply Chain and Logistics
Travel & Tourism
Travel Booking Platforms, AI Trip Planning Solutions, Hospitality Management Systems, Customer Experience Applications, Location Intelligence Services
Automotive
Connected Vehicle Platforms, Predictive Maintenance Solutions, Fleet Management Systems, Mobility Applications, Automotive AI Solutions, AI in Automotive Industry
Real Estate
Property Management Platforms, AI Property Valuation, Virtual Property Tours, Lease Management Systems, Real Estate Marketplaces, AI in Real Estate
Entertainment
OTT Streaming Platforms, Content Recommendation Engines, Audience Engagement Solutions, Media Distribution Systems, Content Monetization Platforms
Manufacturing
Smart Factory Solutions, Predictive Maintenance Systems, Quality Inspection Automation, Supply Chain Intelligence, Digital Twin Platforms, AI in Manufacturing
Insurance
Policy Administration Automation, Claims Management Platforms, Underwriting Intelligence Systems, Policyholder Engagement Solutions, Risk Assessment Tools, AI in Insurance
LET’S BUILD TOGETHER
Modernize your systems with our AI transformation expertise.
At Xicom, we assess your existing infrastructure and integrate AI-driven modernization aligned with your technical goals and business requirements, ensuring faster delivery and sustained growth.
AI Solutions Engineered for Enterprise Scale
150+
AI Engineers & Data Scientists
300+
AI Solutions Delivered
ISO 9001 Certified
NASSCOM & STPI Accreditation
NASSCOM & STPI Accreditation
100+
AI Models in Production
30+
Industries Served
Natural Language Processing (NLP)
We use NLP to modernize how legacy systems handle unstructured data: contracts, emails, support tickets, scanned records, turning text your systems previously couldn't process into structured, usable information. This lets modernized applications extract meaning, classify content, and search intelligently, instead of treating text as static, unreadable data.
Machine Learning (ML)
We embed ML into enterprise systems to replace static, rule-based logic with models that learn from data and improve over time. This means legacy applications that once just recorded information can now predict outcomes, flag anomalies, and support decisions, forecasting demand, detecting fraud, or identifying risk as part of daily operations.
Deep Learning
When modernization involves complex, high-dimensional data like images, speech, intricate patterns, we apply deep learning to give systems that capability. This technology lets modernized applications handle tasks traditional rule-based logic never could, like visual inspection or language generation, extending what legacy systems are able to process.
Computer Vision
We use computer vision to modernize processes that previously relied on manual visual review like document verification, quality inspection, defect detection. By integrating this technology into enterprise systems, visual data gets interpreted automatically and fed directly into existing workflows, removing a manual bottleneck that legacy systems had no way to handle.
Cloud Computing
We migrate modernized systems onto cloud infrastructure to replace the fixed capacity and hardware limits of on-premise environments. This technology gives modernized applications the ability to scale on demand, run AI workloads without hardware constraints, and reduce the operational overhead of maintaining physical servers your legacy systems used to depend on.
Containerization
We containerize legacy applications as a core part of modernization, packaging them with their dependencies so they run consistently across environments. This technology removes the "it worked in testing" problem that legacy deployments were prone to, and makes modernized systems portable across cloud providers instead of locked into one setup.
Distributed Computing
We apply distributed computing to modernize systems that need to process large data volumes beyond what a single server can handle. This technology splits processing across multiple machines, giving modernized applications the throughput required for real-time AI workloads; something legacy, single-server architectures were never built to support at scale.
In-memory Computing
We use in-memory computing to modernize systems where speed is critical: fraud checks, live recommendations, real-time dashboards. This technology processes data directly in RAM instead of reading from disk, closing the performance gap that legacy batch-oriented systems have when real-time AI responsiveness is what the business actually needs.
Data Streaming
We use data streaming to modernize legacy batch processes, replacing overnight data transfers with continuous, real-time flow between systems. This technology lets modernized applications react to events as they happen, feeding AI models fresh data instantly instead of forcing them to work with information that's already hours old.
Predictive Analytics
We apply predictive analytics to modernize systems that previously only reported on what already happened. This technology uses historical and real-time data to forecast what's likely to happen next: equipment failure, demand shifts, customer risk, turning modernized systems from passive record-keepers into tools that support proactive decisions.
Case studies showcasing the value delivered to clients through our solutions
Explore how we partner with clients across industries to deliver tailored AI solutions that improve efficiency, enhance customer experiences, reduce costs, and drive long-term value.
BoxxDocks AI Asset Tracking Platform
The Challenge
Asset tracking was manual, error-prone, and scattered across disconnected systems. The client had no centralized visibility into asset movement, no real-time location data, and no way to generate instant reports across multiple enterprise clients from a single platform.
Our Approach
We built BoxxDocks as an AI-enabled asset lifecycle platform using QR/NFC scanning, real-time dashboards, geolocation tracking, and a RAG-powered conversational engine that lets users query live operational data in plain English instead of navigating dashboards.
The Result
- Real-time inventory tracking across warehouses, transit, and delivery
- AI query engine for natural language insights
- LLM-powered reporting with anomaly detection
- Complete audit trail with location, timestamp, and status
Parity AI AI Finance Platform
The Challenge
Personalized financial advice was expensive and limited to periodic meetings. Users had no way to consolidate banking, investments, debt, and insurance into one platform with real-time, AI-driven guidance.
Our Approach
We built Parity AI as an always-on financial wellness platform powered by AI. It securely pulls data from multiple sources, analyzes user behavior, forecasts outcomes, and delivers personalized recommendations — acting as a digital financial advisor, not a budgeting app.
The Result
- AI-powered financial planning across banking, investments, and debt
- Personalized recommendations based on life events and behavior
- Integrated financial education with actionable insights
IELTS AI-Powered Speaking Evaluator
The Challenge
Human IELTS tutors cost $30-$80/hour with limited availability. Generic speaking apps lack authentic Part 1/2/3 exam structure, pronunciation feedback, and band-aligned scoring rubrics. Learners had no way to get unlimited, exam-authentic practice at scale.
Our Approach
We built a full-stack AI platform with a lifelike AI Avatar Examiner using LangGraph + Gemini conversation engine, Google Cloud TTS with viseme lip-sync, Faster-Whisper ASR, and a 6-layer IELTS evaluation pipeline scoring all 4 criteria with ±0.5-1.0 band accuracy.
The Result
- 1,000+ concurrent users with 2–3s response latency
- 10x lower cost than human examiners
- 6-service AI microservice with model preloading
- No-code admin panel for rapid content updates
Health E Livin AI Wellness Platform
The Challenge
People struggling with mental health had no single platform to access trusted information, take confidential assessments, and find providers — all without stigma or privacy concerns.
Our Approach
We built HealtheLivin as an AI-powered mental wellness app with a conversational AI assistant, anonymous screenings, evidence-based resource library, and geolocation-based provider discovery — all on privacy-first cloud infrastructure.
The Result
- AI knowledgebase for natural language mental health support
- Confidential assessments with personalized wellness insights
- Location-based provider discovery for professional care
- Privacy-first architecture with encrypted data management
Today Fit Fitness App
The Challenge
Gyms, trainers, and users were stuck with disconnected tools for scheduling, payments, and fitness tracking. There was no single platform that combined personalized fitness with business management.
Our Approach
We developed TodayFit as an AI-driven fitness platform with trainer booking, personalized tracking, community features, and a SaaS layer for gym and trainer business management.
The Result
- 30% increase in user engagement
- 4.7+ average rating on the App Store and Play Store
- 60% growth in trainer bookings
- 45% increase in subscription conversions
SmartVend AI Smart Vending Platform
The Challenge
Managing a distributed vending machine network meant manual inventory tracking, disconnected payment systems, no remote management capability, and zero real-time visibility into machine health or sales performance across locations.
Our Approach
We built SmartVend as a cloud-connected IoT platform combining an Android kiosk app, centralized web portal, real-time inventory sync, hardware integration for payments and dispensing, and a digital advertising layer — all managed from a single dashboard.
The Result
- Real-time inventory sync with low-stock alerts
- Remote machine management across unlimited locations
- IoT integration for payments, dispensing, and monitoring
- Centralized sales analytics and revenue reporting
Modernizing systems with proven frameworks and tools
Our AI experts work across modern machine learning, data engineering, cloud, automation, and deployment technologies, selecting the right tools and frameworks to modernize your existing systems into scalable, secure, and production-ready AI-driven infrastructure.
Python
R Programming
Java
C++
TypeScript
Claude
GPT-5.6
Gemini
Llama
Mistral
LangGraph
CrewAI
AutoGen
PydanticAI
PyTorch
TensorFlow
Hugging Face
Scikit-learn
JAX
LangChain 
LlamaIndex 
Pinecone
Weaviate 
pgvector
AWS (Bedrock)
Azure
Vertex AI
Databricks
Snowflake
Ensure Intelligent Operations
Embed AI into everyday enterprise workflows to help teams process information, make decisions, and execute repetitive or complex tasks more effectively. AI can augment existing processes with intelligent recommendations, automation, and analysis, enabling employees to work with greater speed and accuracy while improving how established systems function.
Turn Enterprise Data into Action
Connect AI with existing enterprise data sources to make information more accessible, contextual, and actionable across business functions. Instead of allowing valuable data to remain confined within disconnected systems, AI can help organizations extract insights, identify patterns, support faster decisions, and make information more useful within workflows.
Improve Operational Efficiency
Apply AI to time-intensive processes to reduce manual effort, accelerate execution, and improve operational performance. By identifying activities suited to intelligent automation, enterprises can streamline workflows, reduce repetitive work, improve processing speed, and enable employees to focus on higher-value responsibilities, creating measurable efficiency gains.
Adapt to Changing Business Needs
Modernized systems can incorporate new AI capabilities more easily, giving enterprises greater flexibility as business requirements evolve. AI-enabled modernization helps organizations respond to changing customer expectations, market conditions, regulatory requirements, and operational priorities without repeatedly redesigning core systems.
Enable Continuous Innovation
AI modernization creates a more adaptable technology foundation for introducing new capabilities over time. Rather than treating modernization as a one-time transformation, enterprises can progressively enhance systems as technologies, business requirements, and opportunities evolve,reducing the need for repeated large-scale redevelopment.
Boost Business Value
Modernize existing applications and processes with AI without replacing technology investments that continue to deliver business value. By introducing intelligent capabilities into established environments, enterprises can improve functionality, automate processes, and address current limitations while preserving proven systems, while minimizing disruption.
Legacy Logic Verification
The biggest fear in modernization isn't the new system; it's losing business logic nobody ever documented properly. We extract and verify that logic before touching a single line of code, then test old and new systems side-by-side until every output matches exactly. You catch discrepancies before launch, not after go-live, when fixes get expensive and disruptive.
Phased Rollouts
We don't migrate everything at once and hope it holds. Systems move in controlled stages, with old and new environments running in parallel until each phase is thoroughly tested and proven stable. If a module needs adjustment, we address it within that phase, without putting live operations at risk or requiring a disruptive full-system rollback or widespread rework.
Open Standards, No Lock-In
We modernize toward open standards and your target architecture, not a proprietary format built around our internal tools. Your systems stay portable across AWS, Azure, or GCP, and remain maintainable by any engineering team afterward, not just ours. You're not trading one vendor dependency for another one further down the line.
Uptime-first Planning
Some of what we modernize runs mission-critical operations: banking cores, healthcare records, logistics platforms that can't tolerate downtime. We plan every migration around your actual uptime requirements, not a generic playbook, because "just redeploy it" isn't an option when failure has real consequences for your customers, your revenue, and your reputation.
Built-In Security & Compliance
Vulnerability remediation, access controls, and audit trails get built directly into the modernization process itself, not patched on afterward as an afterthought once something goes wrong. That means fewer findings when your next audit rolls around, and a system that meets current regulatory and security standards from the very first day it runs in production.
Long-term Technical Ownership
Modernization doesn't end the day a system goes live, and neither does our involvement in it. We monitor performance, and keep tuning the system as your usage grows and business requirements shift over time. Clients get an ongoing technical partner invested in outcomes, not a vendor who disappears once handoff documentation gets signed.
Our modernization approach for successful enterprise AI adoption
We follow a structured process to modernize enterprise systems with AI, bringing consistency and clarity to every stage of transformation. Our approach aligns technology with business priorities while creating scalable, intelligent, adaptable, and future-ready enterprise environments.
1
Assess
We audit your systems, architecture, dependencies, and data quality, before making recommendations. This produces a modernization roadmap based on your actual environment.
2
Plan
We define a modernization strategy, including what gets rehosted, refactored, or replaced, along with a phased roadmap. This aligns technical decisions with what matters most to your business.
3
Modernize
We execute the migration in phases: converting code, re-architecting applications, and rebuilding integrations. Old and new systems run in parallel where needed, so operations stay uninterrupted.
4
Integrate AI
Once the modernized foundation is ready, we embed AI where it creates measurable value, such as prediction and automation. Every feature is tied to a specific workflow.
5
Support & Optimize
After go-live, we monitor performance, catch regressions early, and keep tuning the system as requirements evolve, so it keeps improving over time.
Our engagement models models for AI modernization services
We offer flexible engagement models for modernizing multiple systems, fixed-price for one scoped upgrade, or pay-as-you-go while you figure out what's worth modernizing first, matched to your actual need, not a generic package.
Fixed Price Model
Best for well-defined modernization projects, this model ensures clear scope, budget predictability, and timely delivery without surprises.
- Upfront agreed cost and project scope
- Milestone-based progress tracking
- No hidden charges or overheads
- Reliable delivery timelines and outcomes
Most Popular
Dedicated Teams Model
Ideal for businesses seeking long-term AI modernization, this model provides a dedicated team of AI engineers working exclusively on your systems.
- Full control over team structure and workflows
- Highly scalable and cost-effective
- Direct communication with developers
- Increased focus and faster turnaround
Time & Material Model
Perfect for modernization projects with dynamic requirements, this model offers agility, cost control, and adaptability to continuous innovation.
- Flexible billing based on actual efforts
- Adjust resources and scope anytime
- Ideal for iterative and evolving projects
- Faster implementation and continuous optimization
Client testimonials and reviews showcasing the value we consistently deliver
Explore how our clients describe their journey with us, reflecting strong collaboration, effective execution, and consistent outcomes delivered across engagements. See how our delivery framework ensures consistency from initiation through to successful completion.
❝
Parity AI
We came to Xicom with an ambitious goal: make institutional-grade financial planning accessible to everyone through AI. Xicom went beyond the brief, blending conversational AI and predictive analytics into an effortless platform. Parity AI is exactly what we envisioned.
❝
RadioFX
Xicom took the time to understand what our stations and listeners actually need, building something tailored rather than generic. Live chat and polls are now core to how we connect with our audience — the app has become a true extension of our on-air presence.
❝
StudyBuddy
Xicom built exactly what our students needed: a secure space to connect, collaborate, and find tutoring support in one place. Their understanding of student behavior shows in how naturally everything fits together — StudyBuddy is something students genuinely rely on.
❝
BoxxDocks
Xicom helped us bring BoxxDocks to life exactly as we envisioned it. Their team understood the complexity of AI powered real-time asset tracking and delivered a platform that's both powerful and genuinely easy to use.
❝
TodayFit
We are very happy with their services. I cannot recommend them highly enough. The value, ability, and delivery that we have seen has been superb, and we look forward to continuing this relationship.
❝
SmartVend AI
Their team built SmartVend from the ground up, combining hardware integration, real-time inventory sync, and a centralized dashboard that completely changed how we operate. We couldn't have asked for a more capable technical partner.
❝
Tap Retail
Partnering with Xicom to develop our Tap Retail app and website was one of the best decisions we made. Their team demonstrated exceptional professionalism, technical expertise, and a deep understanding of our business goals.
❝
GoGmGo
Partnering with Xicom was a game-changer for us. They didn't just build software, they helped us redefine how we run our business. Their proactive support and deep domain knowledge gave us the confidence to innovate faster.
❝
DENVEN
Working with Xicom has been a transformative experience. Their team quickly understood our challenges and delivered a scalable, intuitive solution. I highly recommend Xicom to any organization seeking future-ready, high-performance digital products.
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Frequently asked questions
What are AI modernization services?
AI modernization services upgrade existing business systems, workflows, and infrastructure with AI capabilities, without requiring a full rebuild. This includes integrating AI into legacy applications, automating manual processes, and restructuring data pipelines so AI tools can use them effectively. The approach starts with your current systems, not a blank slate.
How is AI modernization different from a full system rebuild?
AI modernization upgrades and extends existing systems, while a rebuild replaces them entirely. Modernization is faster, lower-risk, and less disruptive to daily operations, and is the right fit for most businesses. A full rebuild is only recommended when existing infrastructure is too outdated or fragile to extend safely.
Will AI modernization disrupt current business operations?
No, AI modernization is designed to happen without disrupting existing operations. It follows a phased, incremental rollout based on mapped system dependencies, so core workflows continue running while the transformation happens in the background. A stop-and-rebuild approach is rarely necessary.
Can legacy or poorly documented systems be modernized with AI?
Yes, legacy and undocumented systems can be modernized with AI. The process begins with an audit of the existing codebase and infrastructure to understand what's in place before scoping any modernization work. Undocumented systems require more discovery time upfront, which is factored into project planning.
How long does an AI modernization project take?
AI modernization project timelines vary based on scope, ranging from a few weeks for a single workflow automation to several months for modernizing multiple interconnected systems. An accurate timeline is provided only after an initial systems assessment, since scope depends on what's uncovered in existing infrastructure.
How much does AI modernization cost?
AI modernization costs depend on the engagement model and project scope. Fixed-price models suit well-defined, single-scope projects; dedicated team and time & material models suit ongoing or multi-system modernization where scope may shift as legacy issues surface. Costs are scoped after an initial assessment, not quoted generically.
What is the first step in an AI modernization roadmap?
The first step in AI modernization is an assessment of existing workflows, systems, and data to identify where AI adds the most value with the least risk. This assessment determines prioritization, before any development work begins, rather than starting with the technically easiest task.
Is my business data secure during AI modernization?
Yes, data protection and confidentiality are maintained throughout an AI modernization engagement, including during migration and integration phases, and are covered under NDA.
Do I need to replace my existing tech stack to adopt AI?
No, most AI modernization work integrates with existing infrastructure rather than replacing it. Tech stack replacement is only recommended when the current systems genuinely cannot support the intended AI capabilities.
What support is available after an AI modernization project is delivered?
Post-deployment support monitors system performance and addresses issues as workflows adjust to new AI-driven processes. The scope of ongoing support depends on the engagement model selected for the project.
Every award marks a milestone in our journey of excellence
As AI-first digital engineering company, Xicom has earned global recognition for delivering innovative, scalable, and high-performing technology solutions. Our awards reflect the trust of clients and industry leaders alike.
