Modernize. Migrate. Maximize Value

Modernization & Migration Factory

Modernize. Migrate. Maximize Value.

‍Legacy platforms often hold back your business from scaling, innovating, and integrating AI. Aptus Data Labs' Modernization & Migration Factory offers a structured, outcome-focused approach to revamp legacy systems and migrate them to next-gen, cloud-native platforms, enabling agility, resilience, and AI-readiness.

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Modernizing the Data Platform

A robust data platform is the backbone of any AI-powered enterprise.
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Our Key Capabilities:

Legacy Database or Data Warehouse or Cloud Database to Cloud Lakehouse Migration
Migrate from on-premise systems like Oracle, Teradata, Netezza, SQL Server to Cloud Platforms like Snowflake, Databricks, or BigQuery.
Real-time Streaming & Event-Driven Architecture
Introduce Kafka, Kinesis, and Apache Flink pipelines for high-velocity, real-time data processing.
Modern Data Stack Implementation
Leverage tools like dbt, Airbyte, Fivetran, and Delta Lake for modular, scalable data engineering.
Data Mesh & Domain Ownership Models
Shift to decentralized, business-domain-led data ownership for agility and alignment.
DataOps & Observability
Establish end-to-end automation, lineage, monitoring, and CI/CD across data pipelines.

Modernizing the Analytics Platform

We empower business users, analysts, and data scientists with the latest self-service, real-time, and embedded analytics capabilities.
Key Capabilities:

BI Tools Modernization
Migrate from legacy dashboards (e.g., Cognos, BO, SSRS) to Power BI, Tableau, or Looker.
Embedded & Conversational Analytics
Enable AI chatbots to fetch data and insights via natural language queries.
Data Virtualization & Smart Caching
Platforms such as Presto, Dremio, or Starburst are used to reduce data movement and optimize performance.
ML-Integrated Insights
Machine Learning tech is infused into dashboards for predictive, anomaly detection, and simulation use cases.
Analytics Governance & Metrics Layer
Reusable semantic layers are implemented for consistent KPIs across tools and teams.

Modernizing the AI Platform

A modern AI platform enables scalable experimentation, deployment, and monitoring of both predictive models and generative agents.
Key Capabilities:

Cloud-Native AI Workbenches
SageMaker, Vertex AI, Azure ML are leveraged for scalable training and model management.
MLOps & LLMOps Pipelines
We undertake automated model training, validation, drift monitoring with feedback loops.
Model Registry & Feature Stores
Version control, reuse, and deployment of ML components are centralised.
Responsible AI Governance
Enable fairness, explainability, security, and regulatory compliance across AI workflows.
AutoML & AI Copilots
Democratize AI with low-code/no-code interfaces and prompt-based modeling assistants.

aptAIHub – The AI Hub for GenAI & Agentic Use Cases & Workloads

aptAIHub is our enterprise AI playground to ideate, build, test, and deploy Generative + Agentic AI use cases on defined AI infrastructure and AI governance

Key Features:

Multi-LLM Orchestration

Integrate OpenAI, Anthropic, Mistral, LLaMA, and custom-tuned models in a unified workspace.

RAG, PromptOps & LangChain Integration

Build production-ready GenAI apps using modular building blocks.

Agent Studio

Design AI agents with memory, tools, and goal-based workflows—deployed as APIs, bots, or UI widgets.

Security-First GenAI

Enable PII redaction, audit logs, and trust scoring to safely deploy GenAI in enterprise environments.

Real-time Monitoring & Analytics

Observe prompts, outputs, hallucination rates, and agent performance via interactive dashboards.

Why Aptus Data Labs for AI-Powered Modernization?

Factory Accelerators for faster, low-risk migration and modernization.

AI-Embedded Everything – from data prep to decision-making.

Automation-Driven Execution – eliminate manual steps, reduce TCO.

We are Vendor-Neutral & Cloud-Agnostic – we use Azure, AWS, GCP, Snowflake, Databricks, and more.

End-to-End Lifecycle Ownership – from current state assessment to post-deployment optimization.

Case Studies

Featured Success Stories

Data Engineering

A Banking Big Data & Analytics Platform with 24x7 Support

100–300% improvement in query performance

USD 15M+ ROI in Phase 1 of implementation

10+ AI/ML use cases delivered across key functions

99.6% SLA achieved with 24x7 infrastructure support

Cloud Computing

Enterprise Data Lake and Analytics implementation for a large Pharmaceutical Company in India on AWS platform

30–40% Reduction in IT Costs

Accelerated Analytics with 3X Faster Reporting

AI/ML-Ready Infrastructure

Manual Work Reduction

Analytics Modernization & Migration

Data, Process , Batch jobs and Work flow migration to Hadoop Platform

40% Reduction in IT Infrastructure Costs

60% Improvement in Data Processing Speeds

4X Scalability Boost

70% Reduction in Downtime

Analytics & Artificial Intelligence

Supply Chain Scheduling & Route Optimization for an Oil & Gas company

Accurately forecasted voyage schedules

Minimized total logistics cost

Fully automated scheduling system

Improved cost visibility and planning accuracy

Analytics & Artificial Intelligence

Enabled a leading mortgage financing company to predict credit risk accurately and automate their process

85–90% model accuracy

Complete automation of the credit risk process

Improved risk visibility and strategy formulation

Consistent model performance

See More Success Stories

Modernization & Migration Factory

Modernizing the Data Platform

Modernizing the Analytics Platform

Modernizing the AI Platform