What we do/Software Modernization Services/AI-Ready Architecture

AI-Ready Architecture

Most Platforms Are Not Ready for AI. Most Teams Do Not Know Exactly Why.

Adding AI capability to an existing platform is not primarily a model selection problem or a data science problem. It is an architecture problem. The reason most enterprise AI initiatives stall between proof of concept and production is not the AI layer. It is the platform underneath it.Adding AI capability to an existing platform is not primarily a model selection problem or a data science problem. It is an architecture problem. The reason most enterprise AI initiatives stall between proof of concept and production is not the AI layer. It is the platform underneath it.

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AI-Ready Architecture Capabilities for Enterprise Platforms and SaaS Products

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Data Architecture for AI
Design and implementation of data infrastructure that supports real-time signal ingestion, feature engineering, and continuous model inference at scale. Covers event streaming architecture, data pipeline design, feature store implementation, and data quality frameworks that ensure AI systems operate on clean, current, and contextually relevant data. Data architecture designed for AI workloads from the ground up rather than retrofitted from batch-oriented reporting infrastructure.
API Design for LLM Integration
Assessment and redesign of API layers to support LLM integration patterns. Context assembly, prompt engineering infrastructure, response handling, streaming output support, token management, and rate limit architecture for production LLM deployments. API design that treats LLM integration as a first-class architectural concern rather than a point integration bolted onto existing endpoints.
Infrastructure for Real-Time Inference
Infrastructure architecture and implementation for AI systems requiring low-latency inference at production scale. GPU compute provisioning, model serving infrastructure, inference pipeline optimisation, auto-scaling configuration, and cost management for variable AI workloads. Infrastructure designed to support inference latency requirements without over-provisioning for peak load.
Agentic Workflow Architecture
Architecture design for systems that need to support autonomous AI agents operating across multiple tools, data sources, and APIs. Covers agent orchestration design, tool integration patterns, context management across multi-step workflows, state persistence, error handling and recovery, and governance frameworks that maintain oversight and auditability as agents take action within defined boundaries.
Vector Database & RAG Infrastructure
Design and implementation of retrieval augmented generation infrastructure for AI systems that need to ground outputs in enterprise-specific knowledge. Vector database selection and configuration, embedding pipeline design, document ingestion and chunking strategy, retrieval optimisation, and hybrid search architecture combining semantic and keyword retrieval. RAG infrastructure built for production accuracy and retrieval latency requirements, not proof of concept performance.
Migration from Legacy to AI-Ready Stack
Sequenced migration planning and execution for platforms where legacy architectural patterns are blocking AI adoption. Cubet designs incremental migration paths that move legacy systems toward AI-ready architecture without big bang replacement, using strangler fig patterns, parallel run strategies, and phased data migration approaches that allow AI capabilities to be introduced progressively as the underlying architecture is modernised.

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Closing the Gap Between Your Platform and AI

Modern Cloud Platform for Lease Auditing

Transformed legacy system using Laravel, Vue.js, AWS—scalable, efficient, audit-ready solution.

Laravel Migration Ensures Scalable School Management Platform

Seamlessly migrated legacy PHP system to Laravel—minimal downtime, boosted performance, future‑ready architecture.

Built with the Right Stack, Engineered for Impact

From performance to scalability, the right tech stack makes all the difference. We build with proven frameworks to deliver secure, fast, and future-ready solutions.

FRONTEND

Frontend designed for usability, performance, and scalability, enabling seamless user interactions and smooth AI-powered experiences across devices and platforms.

ReactJS
Angular
Vue.js
Next.js
Nuxt 3

BACKEND

Backend built for scalability, security, and AI integration, ensuring reliable performance, efficient processing, and seamless connectivity across enterprise systems.

.NET 8
Node.js
Python
Pinecone
LangChain
LlamaIndex
OpenAI API
Anthropic API
HF Transformers

Reduction in AI Integration Failures

Faster AI Production Deployment

Decrease in Inference Latency

Architecture Built for AI

We close the gap between your current platform and what production AI capability actually requires.

Architecture Built for AI

We close the gap between your current platform and what production AI capability actually requires.

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WHY CHOOSE US?

AI-First Architecture
Zero Rebuild Disruption
Production-Grade Infrastructure
Legacy Migration Expertise
End-to-End Delivery
Measurable AI Readiness

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Results & strategies from projects that drove success

Every challenge taught us something. Every solution made a difference. Explore case studies and white papers that capture the results we've achieved together with our clients.

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How We Taught an Enterprise Compliance Platform to Guide Its Own Users

This case study explores how AI was embedded across both orchestration logic and the user experience layer to align system intelligence with user guidance. It shows how enterprise platforms can reduce complexity, improve adoption, and support users without compromising control or scale.

Artificial Intelligence

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How We Built a Platform That Knows Who You Are Before You Say a Word

This case study explores how role-aware personalization was implemented as a native AI capability within enterprise systems. It shows how software can adapt to users in real time, surface relevant actions, and remain powerful without becoming complex.

Artificial Intelligence

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Intelligent Automation as a Native Enterprise Capability: Moving Enterprise Operations Beyond Scripts and Manual Effort

This case study explores how intelligent automation was implemented as a native enterprise capability rather than a collection of scripts or RPA flows. It shows how AI-driven orchestration can reduce operational effort, improve reliability, and keep human judgment where it matters most.

Artificial Intelligence

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Making Voice AI a Native Enterprise Capability: Architecting Conversational AI for Real Operations, Not Experiments

This case study explores how Voice AI was architected as a core enterprise capability rather than a surface-level tool. It shows how conversational intelligence can move beyond pilots to become a dependable part of daily operations in healthcare and elderly care environments.

Artificial Intelligence

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The Experience we create with Technology is Everything!

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Case Studies

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FAQs

Our assessment is scoped directly against your AI capability targets, not general best practices. We evaluate your data infrastructure, API design, infrastructure patterns, and system architecture through the lens of what your specific AI use cases require at production scale — producing a prioritised gap analysis that distinguishes blockers from noise and sequences remediation against your actual timeline.

Yes, and this is precisely where most organisations need the most clarity. We design incremental migration paths using strangler fig patterns, parallel run strategies, and phased data migration approaches that allow AI capabilities to be introduced progressively. Legacy systems continue operating while the underlying architecture is modernised in sequenced stages — avoiding the risk and cost of a big bang replacement.

Existing APIs are typically designed around synchronous request-response patterns optimised for human-speed interactions. LLM integration introduces fundamentally different requirements — context assembly, streaming output handling, token management, prompt engineering infrastructure, and rate limit architecture. Reusing existing endpoints as point integrations creates fragility and performance ceilings that only surface under production load. We redesign the API layer to treat LLM integration as a first-class architectural concern from the outset.

Inconsistent and unstructured enterprise data is the norm, not the exception. Our RAG infrastructure design includes document ingestion and chunking strategy, embedding pipeline design, data quality frameworks, and hybrid search architecture combining semantic and keyword retrieval. The retrieval system is built and evaluated against production accuracy and latency requirements — not proof of concept performance — with data quality remediation addressed as part of the implementation rather than assumed as a prerequisite.

Agentic workflows introduce architectural requirements that standard API integration does not address — multi-step context management, state persistence across tool calls, error handling and recovery when agents encounter unexpected outputs, and auditability of actions taken autonomously within enterprise systems. Our agentic architecture design covers orchestration patterns, tool integration, and governance frameworks that maintain oversight and control as agents operate across your data sources and applications — ensuring autonomous capability does not come at the cost of visibility or accountability.

Get in touch

Drop us your requirements, and we’ll come back with a clear plan on how we can support you.

“Choosing Cubet was the turning point in our project.”

Tech lead, Jendamark

"Working with Cubet felt like adding an extension to our core team."

CTO, SNO

"They understood our goals better than we did."

CEO, Trust Cyprus

“Choosing Cubet was the turning point in our project.”

Tech lead, Jendamark

"Working with Cubet felt like adding an extension to our core team."

CTO, SNO

"They understood our goals better than we did."

CEO, Trust Cyprus

Hear from our clients about their experience!

Testimonials & Success Stories

Committed Tech Partner

Our experience with Cubet has been awesome, and we are very thankful for their passion, flexibility, and commitment. We highly recommend them.

Frances Barkley, Head of DevOps & Strategic Marketing

Testimonials & Success Stories

A Decade of Dependability

Over 10 years with Cubet, we’ve seen consistent dedication, adaptability, and a can-do attitude. Their creativity and problem-solving approach have significantly boosted our agility and operational efficiency, making them an invaluable extension of our team.

John Stuart, CEO-Fuell

Testimonials & Success Stories

Skilled and Solution-Oriented

Cubet consistently delivers high-quality work and exceeds expectations. Building a remote vision control system for robotics is complex, but their team brought the right skills, patience, and drive—making the implementation process smooth and successful.

Tommy Jonsson, CTO, ByteMotion

Testimonials & Success Stories

Agile, Insightful, Cost-Conscious

Cubet is our trusted partner, providing fast agile development and ongoing support for our BMJ platforms. They understand our needs and improve them with best practices. As a non-profit, we value the cost efficiency without sacrificing quality

Ruth Staunton , Head-Corporate Marketing, BMJ

Testimonials & Success Stories

Consistent, Timely, Innovative

We’ve partnered with Cubet for years on a Medical EMR project. Their project management is consistently excellent, always meeting deadlines. Their developers actively look for innovative ways to improve our designs, adding real value to the collaboration.

Dr Hassan Shafeeq, CEO, BloomEMR

Testimonials & Success Stories

Well-Orchestrated Collaboration

Cubet played a key role in building our mobile apps in 2020. What stood out was their tight coordination every phase, from planning to delivery, felt smooth, collaborative, and well-managed across months of focused teamwork.

Lyric Jain, CEO, Logically

Testimonials & Success Stories

Trusted Growth Partner

My partnership with Cubet began six years ago and has steadily flourished. Collaborating with them on both personal product development and client projects has been a smooth experience, marked by professionalism, consistency, and shared success.

Anik Devaughan, CEO, Ozone

Testimonials & Success Stories

Reliable Technology Partnership

Cubet has been a reliable technology partner for Odin Education. Their proactive approach, technical expertise, and commitment to delivering quality solutions have played a key role in supporting our mission to transform education through technology