Welcome to CNTXT AI Product Lab
We design and deploy custom AI that improves your workflows, solves your toughest challenges and delivers measurable results.
About Lab
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.
ShipFaster
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.
Our Technology Approach
Built for Reality, Not Theory
Our methodology ensures your AI solution delivers business value through planning, execution, and measurable outcomes.
01
Design for Context
We build regional-first models shaped by regional data and real constraints, refined through open collaboration.
02
Stress-Test in the Wild
We pressure-test prototypes against dialect variation, noise, edge cases and security risks, breaking limits before advancing.
03
Prove in Production
Only what works in the Lab goes live. We monitored and adapted for real-world reliability.
Enhacing 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.
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.
Try Munsit Platform
Munsit Voice-to-Text App
Turn Arabic audio into instant, precise transcripts. Built for creators, journalists, and everyday pros.
Try Munsit App
AI Validation Platform
Trust what you build. Test, evaluate, and audit AI systems for bias, reliability, and compliance before they reach production.
Try Test AI
Patents & Publications
Innovation & Research Spotlight
Highlighting research contributions that are shaping the future of Arabic artificial intelligence across diverse applications
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
From Our Insight Hub
Deep dives into AI, data strategy, and real-world tech trends.
