tafsirtool

Private AI that translates centuries of classical Arabic Qur'anic commentary into clear modern English — and quality-checks every line before readers see it.

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tafsirtool.com

Before the build

There is more classical Arabic commentary than any team could ever translate by hand. Each line needs deep expertise, and checking the work one piece at a time was the part that never sped up.

System shipped

Private AI that translates centuries of classical Arabic Qur'anic commentary into clear modern English — and quality-checks every line before readers see it.

What changed

Decades of specialist translation work, done for the price of a graphics card. By hand it would cost six figures and take years. Here it runs on private hardware, with every translation checked automatically.

System map

The workflow behind the proof.

Workflow modules

More timeMore money

tafsirtool

Private AI, Running In-House

Workflow: Private AI, Running In-House

Before: Renting AI from a big cloud provider would mean unpredictable bills, handing sensitive religious text to a third party, and speed limits that make overnight runs impractical.

Outcome: New commentary keeps reaching tafsirtool.com, fast enough to cover the whole library, at the cost of a graphics card instead of a never-ending rental bill.

System: The AI runs entirely on one machine in-house, working on eight passages at once. It splits the text cleanly so no verse is ever cut in half, translates it, and tidies the result — all without sending anything outside. 15 classical scholars are already covered in the live product.

Proof: Powerful AI that runs on its own private hardware — no data sent to outside companies, no usage bills, no limits on how much it can translate.

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Lower riskMore time

tafsirtool

Every Translation, Checked Automatically

Workflow: Every Translation, Checked Automatically

Before: Translating fast is useless if mistakes slip through. People could never review every line by hand fast enough, and bad output would quietly pile up.

Outcome: Every line is checked, so only translations that pass reach scholars and readers on tafsirtool.com.

System: After each translation, a separate AI reviews it against the original Arabic, records a clear pass or fail, and sends anything weak back to be redone. Nothing reaches readers until it passes.

Proof: A second AI checks the first. Every translation is graded against the Arabic source and gets a clear pass or fail before anyone sees it.

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Less adminLower risk

tafsirtool

Turning Old Scans Into Clean Text

Workflow: Turning Old Scans Into Clean Text

Before: The source texts are old scans with messy layouts and mixed scripts. Ordinary text-reading tools choke on them and pass mistakes straight through.

Outcome: The translation step always starts from clean, sound text instead of raw scan errors.

System: The scans are read and turned into clean, structured Arabic text. First, a 425-line check went through the documents and logged roughly 105 problems — odd headers, broken page breaks, text running the wrong way — so they could be fixed before any translation started.

Proof: Reads scanned classical manuscripts and pulls out clean, structured Arabic text — after a check that found and logged the problems first.

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More timeLower riskClearer operations

tafsirtool

Runs Overnight Without a Babysitter

Workflow: Runs Overnight Without a Babysitter

Before: Long jobs on a single machine can get stuck repeating themselves, wander off track, or crash the hardware — and any of those can ruin a night's work unnoticed.

Outcome: Big overnight jobs finish on their own — the system handles the usual failures itself instead of needing someone to watch for them.

System: Every job is wrapped in safety checks. It spots when the AI starts repeating itself and retries, rejects answers that are the wrong length, and a watchdog restarts the machine on its own if it freezes or runs out of memory. Progress is saved as it goes, so a restart picks up where it left off.

Proof: Safety checks and a self-restarting watchdog keep big overnight jobs finishing on their own, with nobody watching the screen.

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Proof it is real

Scholars wired into the live product: 15
Problems found and logged before translating: ~105
Lines in the quality-check script: 425

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