What you get

Messy real-world documents, returned as data and decisions you can trust.

Handwritten intake forms. Multi-column tables a general model quietly mangles. We build the extraction pipeline and verify the output by hand. Then we can turn that data into a dashboard and analysis, or do the same for data you already have.

Examples

Before & after. The extraction, not just the pitch.

Source document beside the structured output produced from it.

French · printed table, 3-level merged header
French printed table 'Villes de France' with a three-level merged header (region, half, group)
Source
The same table flattened into a single-header-row spreadsheet, each merged header level repeated per column
Extracted, flattened for spreadsheet use
Arabic · handwritten name
Handwritten Arabic name sample, demonstrating the handwriting recognition the studio works with
Source
Corrected, verified reading of the handwritten name
Verified reading: “Sidi Al-Dhahir” (سيدي الظاهر)
What we build

Two capabilities. Use them together, or on their own.

The spine

Document extraction

OCR, handwritten text recognition, and table extraction across regional scripts, verified by a person before it ships.

On your data

Document analysis & dashboards

A dashboard and analysis built from what we extract for you, or from data you already have. Either way, something a decision-maker can actually use.

Why not just use a general AI model

General models are confident exactly where they're wrong.

Messy handwriting

Accuracy collapses on real hands, not the clean samples in a demo.

Non-Latin & regional scripts

Arabic and mixed-script pages are where the gap is widest.

Merged & multi-column tables

Row / column association silently breaks; the numbers still look plausible.

No confidence signal

You can't tell which 3% is wrong, so you can't trust any of it.

Our answer is a human-in-the-loop verification layer: every delivery is checked against the source by a person before it reaches you, with a documented QA method per project.

What clients say
Who we are
Riva Malik, co-founder
Riva Malik
Co-founder

My work sits at the intersection of two things I care about: building AI systems that hold up outside the lab, and teaching people how those systems actually work.

I build across computer vision, NLP, and generative AI, often under real constraints, with limited data and inputs that look nothing like a clean benchmark. I also teach.

Both halves of the work come from the same place: I like taking something complicated and making it usable, whether that's a model that needs to survive contact with real data or a concept someone has been stuck on for weeks.

Shaheema, co-founder
Shaheema
Co-founder

Experienced Data Scientist and Co-Founder with a strong background in developing and delivering innovative AI and machine learning solutions. Proven ability to lead the development of innovative AI solutions, translate complex technical challenges into practical products, and create technology that improves operational efficiency and delivers measurable business value.

Start a conversation

Tell us about the documents

What they are, roughly how many, and what you need out the other end: a structured dataset, an API, or a dashboard. A founder replies, and scope and pricing are worked out off-site.

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