Welcome to CNTXT AI Product Lab

Where the region’s toughest challenges become AI solutions.
 Our Lab transforms bold ideas into products : rapidly built, tested in real-world conditions and ready to create lasting impact.

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Trusted by global innovators shaping the future of AI.

About

CNTXT AI Lab

We built this Lab to close the gap between what the region needs and what generic global AI cannot deliver. Our team of engineers, researchers, and domain experts design and deploy AI-first products, built to power the ambitions of people, businesses and governments across EMEA and beyond.

Ship

Faster!

Our mission is to turn bold ideas into AI-first products, built to tackle industry challenges, understand complex languages and adapt to the needs of our region.

ENHANCING YOUR ECOSYSTEM

AI Products and Apps Built to Deliver Value

We unite your existing tools with custom AI that understands your business, creating one powerful ecosystem that drives results.

Munsit

Speech Intelligence Platform

Voice-to-insight. Built for Arabic.
Need integrations, bulk transcription, on-prem, or API access? Explore Munsit for accuracy, compliance, and scale.

Discover Munsit

Munsit App

Voice-to-Text App

Turn Arabic audio into instant, precise transcripts. Built for creators, journalists, and everyday pros.

Discover Munsit

TestAI

AI Validation Platform

Trust what you build. Test, evaluate, and audit AI systems for bias, reliability, and compliance before they reach production.

Discover Test AI

CNTXT

Patents & PubBlications

Innovation & Research Spotlight

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.

Read the research paper    →

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.

Read the research paper    →

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

Read the research paper    →

Our Technology Approach

We don’t believe in perfect solutions out of the box. Our method is simple: build fast, break what doesn’t work, and keep improving until it does.

Design for Context

We build regional-first models shaped by regional data and real constraints, refined through open collaboration.

Stress-Test in the wild

We pressure-test prototypes against dialect variation, noise, edge cases and security risks, breaking limits before advancing.

Prove in Production

Only what works in the Lab goes live. We monitored and adapted for real-world reliability.

NEWS

CNTXT AI in the Media

See how regional and global media are covering our journey.

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