Getting Started: How Labeeb Works¶
Business Value
Labeeb automates the entire fact-checking lifecycle — from content collection to evidence-backed verdicts — reducing manual verification costs by 90% and delivering results in seconds across Arabic and English. This page walks you through how the platform works, what each part does, and where to go next.
1. What Is Labeeb?¶
Think of Labeeb as a digital newsroom that never sleeps.
In a traditional newsroom, stories go through several hands before publication:
- A field reporter gathers stories from various sources.
- An editor reviews and organizes them.
- A fact-checker investigates whether the claims are accurate.
- A librarian archives everything so it can be found later.
- The front page presents the final, verified stories to readers.
Labeeb automates this entire workflow using specialized services that work together seamlessly.
Labeeb takes news articles from across the web, reads them for important claims, searches for evidence that supports or contradicts those claims, and produces a credibility assessment — all automatically.
The result? Trustworthy, evidence-backed news analysis delivered in seconds, in both Arabic and English.
- Journalists who need fast, reliable fact-checking.
- Newsrooms looking to scale verification without adding headcount.
- Researchers exploring media credibility at scale.
- Organizations that need to monitor information quality across sources.
2. How It Works: The Big Picture¶
Every article that enters Labeeb travels through a clear, automated journey — from collection to delivery.
flowchart LR
A[Collect] --> B[Quality Check]
B --> C[Analyze]
C --> D[Find Evidence]
D --> E[Verify]
E --> F[Deliver]| Phase | What Happens | Why It Matters |
|---|---|---|
| Collect | News is gathered from RSS feeds, websites, and partner APIs | Ensures broad, diverse coverage |
| Quality Check | Each article is evaluated for completeness and relevance | Prevents low-quality content from wasting resources |
| Analyze | AI identifies claims, people, organizations, and topics | Turns raw text into structured, searchable information |
| Find Evidence | The platform searches its archive for related articles | Builds the evidentiary basis for each claim |
| Verify | Each claim is scored based on how well evidence supports it | Produces transparent, auditable credibility assessments |
| Deliver | Results are served through the interface and API | Makes verified insights available to users and applications |
3. The Journey of an Article¶
Here's a closer look at what happens at each stage. No technical background required — just follow the story.
What happens here
The platform continuously monitors hundreds of news sources — think of it as subscribing to every major publication at once. When new content appears, Labeeb's Scraper fetches the article, cleans up the text, and hands it off for processing.
Key ideas:
- Sources are defined by profiles — each one tells the system where to look and what to extract.
- Articles are normalized so that content from different sources looks consistent internally.
- Duplicates are automatically detected and skipped, so the same story isn't processed twice.
What happens here
Before any AI analysis begins, each article passes through a Quality Gate — an automated editor that evaluates whether the content meets minimum standards. Articles that are too short, irrelevant, or poorly structured are filtered out.
Key ideas:
- The gate scores each article on quality, language confidence, and completeness.
- Accepted articles move forward; blocked articles are logged with a reason.
- This prevents the platform from spending resources on content that won't produce useful results.
What happens here
Accepted articles are enriched by AI. The platform reads the article and extracts claims (statements that can be fact-checked), entities (people, organizations, places), and topics. The enriched article is then indexed so it can be searched later.
Key ideas:
- Claim detection identifies sentences worth verifying — not every sentence is a claim.
- Entity extraction recognizes names, organizations, and locations mentioned in the text.
- The article is indexed in the search engine, making it discoverable for future queries.
What happens here
For each claim, Labeeb searches its entire archive for evidence — other articles that discuss the same topic. It then classifies whether the evidence supports, refutes, or is neutral toward the claim, and produces a final credibility verdict.
Key ideas:
- Evidence retrieval uses both keyword matching and semantic understanding for thorough results.
- Stance detection determines the relationship between a claim and each piece of evidence.
- The final verdict is transparent — users can see exactly which evidence led to the conclusion.
4. Meet the Platform¶
Each part of Labeeb has a specific role, just like members of a newsroom team.
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Scraper — The Field Reporter
Gathers content from hundreds of sources across the web. It fetches, cleans, and forwards articles into the platform on a continuous schedule.
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API Service — The Editor-in-Chief
The central coordinator. It receives articles, validates them, routes them through the processing pipeline, and serves results to users and applications.
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AI-Box — The Fact-Checker
The intelligence engine. It retrieves evidence, ranks relevance, classifies stances, and produces credibility assessments for each claim.
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Search Service — The Librarian
Powers the platform's ability to find articles by meaning, not just keywords. Combines traditional search with AI-powered semantic understanding.
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SinaTools — The Language Specialist
Handles Arabic-specific natural language processing — morphology, named entity recognition, and linguistic analysis that general-purpose tools miss.
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Frontend — The Front Page
The user-facing interface where analysts browse articles, review evidence, and explore fact-check results. Fully bilingual (Arabic and English) with right-to-left support.
5. Behind the Scenes¶
The platform runs on a set of foundational systems that keep everything reliable and fast.
Storage — Where Everything Lives
Labeeb uses two complementary storage systems:
- A relational database stores structured records — articles, claims, entities, sources, and their relationships.
- A search engine stores the same content in a format optimized for fast, intelligent retrieval.
Together, they ensure that data is both well-organized and instantly searchable.
Task Queue — How Work Gets Done
When an article arrives, the platform doesn't process it immediately in-line. Instead, it places a task on a queue — a to-do list for background workers. This design keeps the system responsive even when thousands of articles arrive at once.
Each processing stage (quality check, analysis, evidence retrieval, verdict) is a separate task that runs independently, making the pipeline resilient and scalable.
Global Delivery — Reaching Users Worldwide
Results are delivered through an edge network with 300+ locations worldwide. This means users in any region experience fast response times without the platform needing servers everywhere.
The edge layer also provides security protections, caching, and traffic management automatically.
Infrastructure as Code — Consistent and Repeatable
The platform's cloud resources — servers, databases, networking — are defined in configuration files rather than set up manually. This ensures that every environment (development, staging, production) is consistent and that changes are tracked and reversible.
6. Where to Explore Next¶
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Architecture Deep Dive
Explore how all the services connect, the data flows between them, and the infrastructure that supports them.
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API Reference
See the full list of endpoints, request formats, and response schemas for integrating with Labeeb.
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Search & Retrieval
Understand how hybrid search works — combining keyword precision with semantic understanding for better results.
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Business Overview
A strategic summary of the platform's capabilities, market position, and operational advantages.