Mezgeb.
Document intelligence · Built for Ethiopian workflows

Your documents hold the information. We make it usable.

From document capture to structured data, with validation where it matters.

Mezgeb AI helps organizations turn paper archives and scanned documents into structured, searchable information — including documents written in Amharic, English, and both.

Discuss a pilotExplore how it works

mezgeb.ai / workbench

Illustrative samples · fictional institutional data · not production metrics

source → extract → validate → review

source document

page 01 of 04

የንግድ ምዝገባ እና የፈቃድ ማረጋገጫ ሰነድ

Commercial Registration & Compliance Record

ቁጥር / Ref No.

CR-AA/10492/16

ቀን / Date

14 / 06 / 2016 ዓ.ም

የድርጅቱ ስም / Enterprise Name

FIELD_01 · LEGAL_ENTITYአዋሽ ኢንዱስትሪያል እና ሎጂስቲክስ ኃ/የተ/የግ/ማህበር

የግብር ከፋይ መለያ ቁጥር / TIN

FIELD_02 · TIN_NUMBER0048291054

የተመዘገበ ካፒታል / Registered Capital (ETB)

FIELD_03 · REGISTERED_CAPITAL14,500,000.00 ብር
ተ.ቁየክፍያ ዓይነት / Descriptionየሰነድ ቁጥር / Doc Refመጠን (ብር) / Amount
01የንግድ ፈቃድ እድሳት / License RenewalREV-882112,400.00
02የአገልግሎት ክፍያ / Archival Service FeeREV-88223,250.00
ድምር / Total15,650.00
OFFICIAL STAMP

Authorized signature

extracted record · schema v2.4

Field keyExtracted & normalizedStatus

document_ref

CR-AA/10492/16

Verified

issue_date_et

14/06/2016 ዓ.ም → 2024-02-22

Ethiopian calendar normalized

Normalized

entity_name_am

አዋሽ ኢንዱስትሪያል እና ሎጂስቲክስ ኃ/የተ/የግ/ማህበር

Verified

tin_identifier

0048291054

10-digit format check

Rule passed

line_items_total

15,650.00 ETB

Sum matches line items

Math verified

registered_capital

14,500,000.00 ETB

Stamp overlap detected

Needs review

export preview

{
  "document_ref": "CR-AA/10492/16",
  "issue_date": "2024-02-22",
  "tin": "0048291054",
  "line_items_total_etb": 15650.00,
  "review_flags": ["stamp_occlusion"]
}

The information is already there

The problem isn't a lack of information. It's getting it out of the documents.

Important records often remain trapped in paper files, scanned PDFs, and inconsistent forms. Finding one field can mean opening a document, reading it manually, and entering the same information into another system.

At scale, that creates repetitive work, slower retrieval, and more opportunities for data-entry mistakes.

01

Information stays buried

Records are difficult to search, compare, and retrieve when their contents remain locked inside documents.

02

Manual work accumulates

Teams repeatedly read, copy, verify, and re-enter information that already exists on paper.

03

Every document is different

Layouts, scan quality, languages, and field formats make consistent extraction difficult.

From documents to decisions

One workflow. From raw pages to usable records.

Mezgeb AI is designed to help institutions move beyond storing scanned documents toward extracting, checking, and organizing the information inside them.

Stage 01 — Prepare

Make documents ready to process

Prepare scanned pages and PDFs for downstream analysis, with attention to page quality, orientation, and document structure.

Stage 02 — Extract

Turn pages into structured fields

Identify relevant information across forms and records, including mixed Amharic and English content, then organize it into a consistent structure.

Stage 03 — Validate

Check before you rely on it

Apply field rules and consistency checks to help identify missing, inconsistent, or uncertain information that may need human review.

Stage 04 — Deliver

Put extracted data to work

Organize approved results into structured records that can support search, reporting, exports, and downstream systems.

This is the intended workflow and product approach — stages may be scoped differently depending on document types and institutional requirements.

Designed around local document realities

Documents don't always fit a standard template.

Ethiopian institutional records can combine Amharic, English, numbers, tables, stamps, and different page layouts. A useful document-intelligence workflow needs to account for that complexity instead of assuming every document follows the same format.

  • Amharic and English content
  • Forms, tables, and multi-page records
  • Different layouts and scan conditions
  • Structured extraction with validation and human review where needed

Start with the documents your team actually handles.

Discuss your document workflow

Mixed-language document · Illustrative

የማመልከቻ ቅጽ / Application Form

የአገልግሎት መረጃ

Service Information Record

ስም / Full name

ሃና ገብረዮሐንስ · Hana Gebreyohannes

ቀን / Date

12 / 03 / 2016 ዓ.ም

ቋንቋ / Language

አማርኛ + EN

Where document intelligence can help

Built for the work hidden inside your records.

Different institutions handle different documents. The common challenge is turning those documents into information that teams can find, verify, and use.

Financial services

Make records easier to process

Support workflows involving applications, supporting documents, customer records, and other information-heavy files.

Government and public services

Bring structure to institutional archives

Help organize forms, administrative records, case files, and documents that need consistent retrieval and processing.

Education

Organize academic records

Explore structured extraction from student records, enrollment documents, transcripts, and institutional forms.

Operations and administration

Reduce repetitive document work

Turn recurring forms and internal records into structured information that can feed existing operational workflows.

Use cases are illustrative. The appropriate workflow depends on document types, data quality, and institutional requirements.

Useful data needs more than extraction

Extracting information is only half the job.

A field can be extracted and still be wrong. A document can be readable and still contain missing or inconsistent information.

Mezgeb AI's approach emphasizes structured outputs, validation rules, and review of cases that require attention — so teams can build a workflow around the information, not just the OCR result.

Illustrative interface

Field review

Flagged

registered_capital

14,500,000.00 ብር

14,500,000.00 ETB

Needs review

Stamp overlap detected on registered capital field.

Rule check

  • ✓TIN format check passed
  • ✓Line-item sum matches total
  • !Stamp occlusion on capital field

Structure

Organize extracted information into consistent fields and records.

Validation

Check required fields and apply rules appropriate to the workflow.

Human review

Flag information that needs a person to inspect or confirm it.

Start with a real workflow

Bring us a document problem worth solving.

You don't need to redesign your entire records process to begin. Start with one document type, one recurring task, and a clear definition of what a useful result looks like.

Mezgeb AI can work with your team to understand the documents, define the fields that matter, and scope a focused pilot.

  1. 01

    Understand

    Identify the documents, current workflow, and main bottleneck.

  2. 02

    Define

    Agree on the information to extract, validation requirements, and how results will be evaluated.

  3. 03

    Evaluate

    Test a focused workflow against representative documents and review the results together.

Tell us what your team processes today. We'll start there.

Discuss a pilot

This form is for demonstration. Submissions are validated in the browser only and are not delivered to Mezgeb AI.

FAQ

Questions worth answering early.

Clear expectations matter more than inflated promises — especially when documents vary.

Your records are full of information. Let's make it useful.

Tell us about the documents your team works with and the process you'd like to improve.

Discuss a pilot

Mezgeb AI · Document intelligence for Ethiopian workflows.