Data, Analytics & Knowledge
Text tools that actually read Arabic, not just English translated into it
Most NLP tooling is built for English first and Arabic as an afterthought. We build the reverse: models tuned on Saudi dialect, contract language and mixed-script documents, so your team stops manually re-reading what a machine should have flagged.
2 to 4
weeks to a working first version
2
languages handled natively, Arabic and English
24/7
processing, no manual queue
The direct answer
Arabic NLP is a set of text-understanding systems built to read, classify, tag and extract information from Arabic and mixed Arabic-English documents, correspondence and customer feedback. It's for Saudi companies whose real business text, contracts, WhatsApp complaints, call transcripts, government filings, doesn't fit the English-only tools most off-the-shelf software ships with.
What this removes.
Contracts read manually, clause by clause
Today
Legal or ops staff read every Arabic contract by hand to find payment terms, penalty clauses and renewal dates.
With the system
Key clauses get pulled out and flagged automatically, with a person confirming the ones that matter.
Customer feedback nobody tags
Today
Arabic complaints and reviews pile up in inboxes and WhatsApp threads with no consistent categorization.
With the system
Feedback gets sorted by topic and urgency as it arrives, so patterns show up in weeks, not annual reviews.
English-only tools miss dialect and mixed script
Today
Off-the-shelf sentiment and classification tools misread Saudi dialect, transliteration and code-switched sentences.
With the system
Models trained on the way your customers and staff actually write, not formal MSA that nobody speaks.
Search that only finds exact matches
Today
Finding a past regulatory filing or old contract means remembering the exact Arabic wording used.
With the system
Search understands meaning and synonyms, so a rough description finds the right document.
What lands in your hands.
Text classification models
Tuned on your documents, not generic public datasets.
Entity and clause extraction
Pulls names, dates, amounts and clauses into structured fields.
Sentiment and complaint tagging
Flags urgency and topic on feedback as it comes in.
Arabic OCR and cleanup pipeline
Turns scanned Arabic paperwork into clean, searchable text.
Integration into your existing systems
Feeds results into your CRM, ERP or ticketing tool.
A review dashboard for your team
Shows what the model flagged and lets staff confirm or correct it.
Systems and platforms we work with
- OpenAI
- Anthropic
- Google Gemini
- Meta
Systems and platforms we work with
- React
- Next.js
- TypeScript
- Node.js
- Python
- Flutter
- PostgreSQL
- Supabase
- Tailwind CSS
- Docker
- GitHub
- Google Cloud
- Figma
Five stages. You sign off every one.
Read each stage as a small contract: what we need from you, what lands in your hands, and the sentence that has to be true before we move on.
- Scope and sample3 to 5 days
- Model selection and pilot build1 to 2 weeks
- Full build and integration1 to 3 weeks
- Testing on live volume1 to 2 weeks
- Live operation and supportongoing
Scope and sample
3 to 5 days
We look at real examples of your documents or text and agree what the system needs to classify, extract or tag.
- Share a sample set of real documents
- Name the fields or categories that matter
- A written scope with exact inputs and outputs
- A cost and timeline estimate
We move on when you've agreed the scope and sample data in writing before any build starts.
Model selection and pilot build
1 to 2 weeks
We test a small set of models against your sample data and build a working pilot on the fields you named.
- Answer questions on edge cases we find
- Review the pilot output
- A pilot pipeline running on your sample set
- Accuracy numbers per field, not just an overall score
We move on when the pilot hits an accuracy level you sign off on for the fields that matter most.
Full build and integration
1 to 3 weeks
We scale the pilot to your full document volume and connect it to wherever the output needs to land.
- Give access to the target system (CRM, ERP, ticketing)
- Confirm the output format
- A production pipeline connected to your systems
- A review interface for edge cases
We move on when the pipeline runs end to end on real volume without manual intervention on the standard cases.
Testing on live volume
1 to 2 weeks
We run the system alongside your existing manual process so you can compare results before switching over fully.
- Spot-check a sample of live results daily
- Flag anything that looks wrong
- A side-by-side accuracy report
- Fixes to any patterns that came up in testing
We move on when results match or beat the manual process on the cases you checked.
Live operation and support
ongoing
The system runs on your live document and text flow. We monitor accuracy and retrain when your document types shift.
- Report new document types or edge cases as they appear
- Use the review dashboard for flagged items
- Monthly accuracy monitoring
- Retraining when patterns drift
We move on when this is the ongoing state. We keep the system tuned as your document mix changes.
Asked before signing.
How is this priced?
Pricing is scoped per project after we see a sample of your actual documents, since accuracy work and volume vary a lot. A short contract-extraction pilot costs less than a full feedback-tagging pipeline running across every customer channel. We quote after the scoping stage, not before.
Does this handle Saudi dialect, not just formal Arabic?
Yes, that's the point of building this locally rather than buying an English-first tool. We train and test against dialect, transliteration and code-switched Arabic-English text, because that's what actually shows up in WhatsApp complaints, call transcripts and internal correspondence.
What happens to our data under PDPL?
Your documents and text stay within the scope we agree with you, and we don't use client data to train models for other customers. We can work within your own infrastructure or a dedicated environment depending on how sensitive the material is. We'll confirm data handling terms in writing before any documents move.
Can this connect to our existing CRM or ERP?
Yes, that's part of the standard build, not an add-on. We connect the output into whatever system already holds your customer records or documents, whether that's Odoo, SAP, a ticketing tool or an internal database, so results land where your team already works instead of a separate dashboard nobody opens.
Text your team already has, finally structured
Send a sample of the documents or feedback you're dealing with and we'll tell you honestly what's realistic to automate and what still needs a person.