Mohammad Abu Sheikh, founder and CEO of Cntxt AI

Speaking to us this week is Mohammad Abu Sheikh (LinkedIn), founder and CEO of Cntxt AI, as well as several other ventures he has since exited, and managing ...

Mohammad Abu Sheikh, Founder & CEO of CNTXT AI

Mohammad Abu Sheikh is transforming the AI landscape in the MENA region, driving a shift from passive consumption to sovereign innovation.

The Middle East’s AI visionary: How Mohammad Abu Sheikh is redefining innovation

By developing a digital library that centres on the Arabic language, Abu Sheikh ensures that Arabic remains a core component of the AI revolution

RAGMeter Framework Available on the Python Package Index: rag-meter 0.1.1

RAGMeter is a universal evaluation toolkit designed to assess the performance of any Retrieval-Augmented Generation (RAG) system

Munsit at NADI 2025 Shared Task 2: Pushing the Boundaries of Multidialectal Arabic ASR

A scalable Arabic ASR pipeline using weak supervision and fine-tuning achieves state-of-the-art accuracy across diverse dialects despite limited data.

Advancing Arabic Speech Recognition Through Large-Scale Weakly Supervised Learning

Using weak supervision, we trained a Conformer-based Arabic ASR on 15,000 hours of unlabeled data, achieving state-of-the-art accuracy.

Audit Trails and Compliance Documentation: Meeting Regulatory Requirements in AI Development

AI regulation demands proof of accountability. This article outlines how proper documentation across SOC 2, ISO 27001, and HIPAA frameworks strengthens compliance, tracks data lineage, and prepares organizations for audits. It shows how structured record-keeping reduces legal risk and speeds up regulatory approvals.

Video Analysis at Scale: Insights from Frame-by-Frame Annotation

Video data holds immense untapped value across manufacturing, sports, and security. This article explains how frame-by-frame annotation turns raw footage into actionable intelligence, explores the computational and operational challenges of large-scale video analysis, and outlines how AI-assisted tools drive measurable ROI through accuracy, efficiency, and insight generation.

Prompt Engineering and Response Ranking: The New Frontier in LLM Optimization

Prompt optimization and response ranking define how large language models deliver value in real-world use. This article explains how systematic prompt refinement, evaluation metrics, and ranking methods improve accuracy, efficiency, and alignment with business goals, turning AI output from trial-and-error experimentation into measurable enterprise performance.

Bias Detection and Mitigation in Training Data: Building Fair and Inclusive AI Systems

AI bias is data truth showing through. This article explains where bias originates, how it damages credibility and revenue, and what frameworks can help organizations detect, mitigate, and prevent it. It outlines practical steps to build fair, accountable, and transparent AI systems that earn long-term trust.

Cross-Border Data Transfers in AI Development

AI depends on global data, but privacy laws complicate how that data moves. This article explains how organizations can handle cross-border data transfers, use tools like SCCs and federated learning, and manage vendor compliance to build AI responsibly while meeting international data protection standards.

Minimizing AI Hallucinations: The Role of High-Quality Training Data in Model Reliability

AI hallucinations expose a hard truth: unreliable data builds unreliable systems. When models invent facts, the reputational, financial, and legal fallout can be immediate. From Air Canada’s chatbot liability to Google’s market loss, the cost of inaccuracy is clear. The solution lies in disciplined data curation, grounded architectures, and continuous oversight.

The Future of AI Governance: Preparing Your Data Strategy for Emerging Regulations

AI regulation is advancing faster than most organizations can adapt. The EU mandates strict data governance under the AI Act, while the US and Asia pursue divergent paths. To stay compliant, enterprises need adaptive data strategies, integrating lineage, bias detection, cross-functional oversight, and modular architectures that evolve with policy.

What's the difference between LLM and SLM, and which do I need?

The “bigger is better” era in AI is ending. Enterprises now face a strategic choice: deploy massive, general-purpose Large Language Models or lean, task-focused Small Language Models. This piece breaks down architecture, cost, performance, and governance trade-offs to show why the future of enterprise AI lies in smart specialization.

AI Agents in Enterprise Operations: From Automation to Autonomous Decision-Making

AI agents mark the next step in enterprise automation—systems that act, not react. Unlike rule-based bots, they interpret context, make decisions, and learn from results. Their rise demands more than technical readiness. It requires disciplined governance, secure data access, and a shift toward human-AI collaboration built on trust and accountability.

Data Collection Ethics: Building AI Responsibly in an Era of Privacy Concerns

Every AI system depends on data, but how that data is gathered defines whether the outcome builds trust or backlash. Responsible data collection protects privacy, strengthens reputation, and ensures AI systems remain effective, accountable, and sustainable.

The ROI of Cultural Precision: How Localized AI Data Unlocks New Revenue Streams

Global AI models stumble when faced with local nuance. Expanding into regions like the Middle East and Africa demands more than translation it requires cultural precision. Systems that understand dialect, tone, and context earn trust, strengthen engagement, and unlock measurable growth. Localization, done with discipline, becomes the foundation of sustainable global AI deployment.

AI in Finance: The Shift from Automation to Intelligence

Finance is built on one principle: whoever understands risk faster wins. AI is changing the speed and scale of that understanding. It reads contracts, predicts defaults, and tracks fraud before it happens. The challenge is how to better control. Trust now depends on how clearly institutions govern what their algorithms decide.

The Role of AI in Telecommunications

Telecom networks now operate at a scale that manual systems can’t manage. AI processes live network data, predicts congestion, allocates spectrum, and prevents outages before they happen. The same intelligence personalizes customer interactions and optimizes cost structures. What was once network management has become a discipline of predictive control.

Is Your Data Strategy Ahead or Already Falling Behind?

Most enterprises claim to be data-driven but operate on fragmented, outdated systems. The gap between ambition and reality limits AI adoption. Modern data strategy is accountability. The next advantage lies in traceability, governance, and the ability to explain how data shapes every decision and every prediction.

Data Lifecycles: Protecting Data with Privacy First Principles

Every data-driven decision carries a hidden cost: trust. As privacy laws expand and breaches grow more expensive, the real differentiator is how deeply privacy is engineered into systems.

Human-in-the-Loop: Data Quality with Human and Machine Collaboration

Every smart AI still needs a human mind behind it. Human-in-the-Loop systems show why the best models don’t replace people—they learn from them. From fraud detection to medical imaging, HITL reveals how human judgment fills the blind spots of automation and decides whether AI stays accurate or goes astray.

The Arabic AI Performance Gap: Why Global Models Fail in MEA

Arabic is the world’s fifth most spoken language yet remains digitally underrepresented, holding back AI performance across MENA. This piece reveals how regional leaders are closing that gap, proving the next wave of AI power will be Arabic-native, not English-adapted.

8 Things to Consider When Introducing AI in Healthcare

Billions are flowing into healthcare AI, but success remains rare. From IBM Watson’s $4B failure to HIMSS 2024’s mixed results, this guide explains what works, what breaks, and what every healthcare CIO must fix: data, compliance, privacy, interoperability, ethics, and workflow before AI can safely reach clinical reality.

Data Preparation Before AI Deployment— FAQ with Sibghat

CNTXT AI’s Sibghat Ullah breaks down why data determines AI success. He exposes the hidden pitfalls in poor data prep, from “data swamps” to model drift, and explains how ownership, governance, and continuous QA protect ROI. A must-read for leaders tired of pilot purgatory and failed AI projects.

What is Data Annotation?

Data annotation defines how AI learns, interprets, and performs. This guide explains structured labeling across images, text, audio, and video, and why consistent quality control, governance, and automation shape reliable enterprise AI. It also explores how synthetic data and automated review are changing large-scale annotation methods.

Data Annotation Best Practices for Enterprise AI

Data annotation decides how machines see the world—accurately or not. When done with engineering discipline, it becomes the difference between models that guess and systems that know.

How Data Annotation Drives the Future of Artificial Intelligence

AI learns through human-labeled data. This article reveals why data annotation is the hidden infrastructure of AI and how governance, quality control, and contextual precision determine model reliability. It argues that the next frontier in AI progress depends on responsible, continuously managed annotation.

4 Key Applications of AI in Modern Banking

AI is changing how banks make decisions. Risk models no longer depend on quarterly reviews; they update by the second. Fraud systems learn from live data, not static rules. Compliance checks run continuously instead of after the fact. The outcome is tighter control, faster action, and fewer blind spots.

AI in Insurance: Risk Assessment and Claims

Risk models built on live data predict outcomes with precision, while automated claims systems cut settlement time from days to minutes. Fraud detection, pricing, and customer engagement now operate through one continuous, data-driven cycle that keeps insurers adaptive and accountable.

The Rise of AI Agents: Why They’re Changing the Way We Work

From managing support tickets to coordinating team schedules, AI agents are becoming a silent force behind how modern businesses operate. But what exactly are AI agents, and why are they becoming essential?