We are a multinational technology consultancy, custom software development and engineering firm. We are ready to connect, engage, and drive businesses' digital transformation. We have helped over 100 clients worldwide, including top US Fortune 500 companies, to become and remain leaders in their fields. Today, we are providing premium end-to-end tailored Artificial Intelligence solutions and technology services (Optical character Recognition (OCR), Machine Learning, Behavioral Analysis) in collaboration of different disciplines, including Staff Augmentation, custom Software development (Software outsourcing companies), Smart Devices Engineering, Internet of Things, Data Management, and Customer Experience. We accelerate the digital transformation for businesses and corporations by developing scalable and forward-thinking projects to achieve operational excellence, improve customer engagement, and unlock new growth opportunities. Our teams of highly skilled engineers, creative thinkers, and industry-specific experts from 7 locations across the globe delivered more than 250 innovative projects. To date, we served 80M+ users and contributed to 7 US patents. Find us in Beirut, Lebanon, in Dubai, United Arab Emirates, in California, Unites States of America, in Mendoza, Argentina, in Pune, India, in Stockholm, Sweden and in Guangdong, China. If you believe in the transformative power of technology: Join us in Reimagining Everything!

DATA PLATFORMS & ANALYTICS​

PRODUCTION-READY DATA. AI-RELIABLE.​

No brittle pipelines. No analytics theater.​ We engineer data platforms that run in live enterprise systems and safely power AI in production. 

Most data initiatives fail for structural reasons. Pipelines break, ownership is unclear, and trust erodes.​ We treat data as core infrastructure. It must integrate with live systems, operate reliably, and support AI with auditability and control.​

How we approach data

Start from reality​

We begin with existing platforms, data sources, constraints, and operating models. Legacy systems, regulatory requirements, and current consumers shape the design.​

Build strong data foundations​

Reliable ingestion, transformation, and storage come first. Data quality, security, and governance are enforced consistently across the platform.​

Deliver incrementally​

New capabilities are introduced alongside existing systems to reduce risk and maintain business continuity.​

Design for operation and adoption​

Platforms are designed to be owned and operated by internal teams, with clear responsibility models, cost visibility, and knowledge transfer built into delivery.​

In practice

Here’s how our data engineering approach shows up in real delivery.

Aligned with operational value​

Business framing​

  • Data work anchored to concrete operational and decision-making needs.​
  • Data products defined with explicit ownership and measurable outcomes.​
  • Elimination of outputs that have no clear consumer or action.​
Designed for complexity​

Architecture design​

  • Coexistence with legacy systems and vendor platforms assumed to remain in place.​
  • Multiple ingestion and processing patterns supported within the same platform.​
  • Architectures evolved incrementally, without rewrites or downtime.​​
Built to run at scale​

Data engineering​

  • Ingestion designed as part of orchestrated workflows, not ad hoc jobs.​
  • Pipelines built with retries, failure handling, and alerts by default.​
  • Transformations resilient to schema change and data drift.​
  • Pipelines designed to run unattended, with built-in recovery and alerting.
Governed by code

Data quality and governance​

  • Data validation and exclusion reporting enforced inside pipelines.​
  • Lineage, auditability, and traceability generated by default.​
  • PII handling and masking embedded by design.​
Intelligence inside the flow​

Embedded ML and LLMs​

  • Predictive models fed directly by production data pipelines. ​
  • AI-assisted normalization, schema mapping, and text-heavy processing with auditability.​
  • AI embedded inside data flows, not bolted on downstream.​
  • Model outputs and data quality signals captured directly in pipeline execution.
Operated, not abandoned​

End-to-end lifecycle​

  • Runtime monitoring and visibility.​
  • Cost tracking and performance control as operational signals.​
  • Clear operating and handover models, so systems do not depend on CME after delivery.

How we engineer it differently

We build control into the data flow​

Data remains traceable, auditable, and compliant as it moves across systems, without relying on external process or manual enforcement.​

Production first, not reporting first​

Pipelines are resilient, monitored, and built to handle schema change, data drift, and performance under load, with cost and reliability tracked as first class signals.​

We think platforms, not isolated pipelines​

We engineer complete data platforms as a single operable system. Each layer is observable, versioned, and designed to support applications and AI in production.​

We work in live, legacy enterprise systems​

We modernize in place and handle backward compatibility so data systems evolve without disrupting live operations.​

In Action

Here are examples of how we helped our clients turn their own data into business gold.​

Energy
Building data capabilities from the ground up​

We helped one of the largest electricity providers in the world optimize energy production and implement advanced data-centric strategies, involving:

+ more
  • Enhancing the data governance framework to align with operational goals​
  • Rebuilding the data history by organizing scattered and unstructured data​
  • Creating a data center for unified data processing and real-time monitoring​
  • Integrating data into workflows to improve operational efficiency​
  • Developing visualization tools for secure access and actionable insights​
Dental
Linking multiple data pools for seamless operations​

We developed a full-scale solution that connects to multiple data pools across 80 dental practices in real-time, involving:​

+ more
  • Implementing data governance policies that comply with industry standards​
  • Consolidating fragmented and unstructured historical data to enhance decision-making​
  • Designing automated data pipelines to actively improve data freshness​
  • Building a centralized data stream hub to enable a real-time view of operations and business metrics​
Market research
Consolidating insights into a centralized data warehouse

We created a data warehouse from 8 connected sources to standardize responses based on over 2 million panelists’ attributes, involving:​

+ more
  • Automating routines for cleaning and processing incoming data to improve the accuracy of research insights​
  • Integrating compliance checks into the ETL workflows to ensure adherence to healthcare regulations​
  • Applying a scalable ETL architecture to expand research capabilities without performance degradation​
  • Establishing a unified data model to integrate diverse data sets from various healthcare research platforms​

Insights

Connected & Intelligent Devices

Manufacturing-Grade Test Frameworks That Protect You From Hardware Failures

AI

Real AI Disruption Is Happening Below the Surface

AI

AI’s New Rules for CIOs – Recap from Gartner CIO Dubai

AI

Walking Through AI Disruption With A Blindfold

NEXT CAPABILITY

AI & GenAI

Learn More

Let’s Reimagine Together!

Take a leap into the future, harness the power of innovation and accelerate your transformation to unlock new opportunities.

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