Ampor Hub — AI document translation operations.

The client and the problem

Ampor Translation is a translation and consultancy business. It does not only receive clean Word documents. Clients send visa applications, official forms, legal contracts, certificates, scans, phone photos, low-quality PDFs, and mixed-format material. A useful system had to work with the messy front door of the business, not only with ideal inputs. The operational requirement was broader than translation accuracy — before Ampor Hub, the workflow was fragmented at every step.

• ▸Files arrived through chat, email, phone photos, and walk-in requests — no single intake point.
• ▸Scanned documents required manual reading or separate OCR tools outside the workflow.
• ▸Translation output often lost document structure — tables, headings, field positions.
• ▸Important details — dates, passport numbers, legal references, names, IDs — could be damaged with no automated preservation checks.
• ▸Review happened outside the system, making status and quality hard to track.
• ▸Client records, job history, invoices, and delivery were spread across separate tools with no linkage.
• ▸Customer questions and booking requests relied on manual response time with no capture.

Our role — ten workstreams

The work spanned product, engineering, AI workflow design, deployment, security, and operational handoff — ten workstreams from first intake to customer support, designed as an integrated system where each feeds the next.

The human stays in control

AI accelerates the work; staff still own the final output. No translated document reaches a client without human approval — and the QAF tells staff exactly where to look first. Before translation the system analyses and classifies each document; after translation the 7-dimension Quality Assessment Framework evaluates every segment before it reaches the review surface.

Documents that look like documents — and the back office around them

Many translation jobs are not plain text. Official documents need layout sensitivity: forms, certificates, tables, headings, seals, margins, and field positions. The system analyses the original layout, maps translated content back into regions by section and field position, lets staff adjust in an editing surface, and exports a DOCX or PDF suitable for client delivery. For official documents, layout fidelity is not cosmetic — it is the delivery standard the client expects. Around that, Ampor Hub also carries the business operations: client records and job history, a services catalogue, and invoices in USD and KHR with deposits, discounts, and VAT — every invoice linked to its job, every job to its client. No separate spreadsheet, no separate invoicing tool, no separate file store.

Language coverage & continuous improvement

The platform supports a 12-language registry with a configurable language-hub rule: every job must involve one of the configured hub languages on one side. That lets the business handle multilingual demand while keeping the operational centre of gravity aligned to the markets it serves. The hub model governs which pairs are valid; review-driven feedback makes every subsequent job smarter.

How the system learns from every job

1. 01

   Job intake

   Document enters; pipeline classifies and extracts content. Active glossary, templates, and patterns applied.

2. 02

   AI translation

   Segments translated using the current knowledge base. QAF evaluates every segment before review.

3. 03

   Human review + edit

   Staff correct errors, edit segments, flag issues. Every edit is a signal the system captures.

4. 04

   Corrections captured

   Terminology errors, missed patterns, register issues, and glossary gaps identified and logged.

5. 05

   Knowledge base updated

   Glossary extended, protected patterns refined, template instructions improved.

6. 06

   Every future job benefits

   Updated controls applied to all subsequent translations — compounding accuracy over time.

Delivery, reliability, and a serious security posture

Deliverables

Major artifacts produced across the ten workstreams — from translation pipeline to production operations.

1. 01Production web application for translation operations — job dashboard, status tracking, and staff workflow
2. 02DOCX, digital PDF, scanned PDF, image, and browser-scan intake pipeline with automatic routing
3. 03AI Vision OCR pipeline — per-page recognition for scanned PDFs, phone photos, and browser scans
4. 04Document classification system — legal, official, form, report, general — with register-aware translation
5. 057-dimension Quality Assessment Framework (QAF) — applied per segment, per job, before human review
6. 06Segment confidence scoring — high / medium / low tiers driving quality-gate decisions
7. 0712-language registry with configurable language-hub rule and server-side pair validation
8. 08Script-integrity guardrails, protected-pattern handling, place-name and entity protection, document-type terminology controls
9. 09Glossary and template controls — accumulated and refined through the continuous-improvement loop
10. 10Layout editor and final DOCX/PDF export path for formatted official documents
11. 11Client records, job history, and notes — linked throughout the platform
12. 12Invoice builder — services catalogue, USD/KHR, VAT, deposits, discounts, PDF preview
13. 13Token-gated client portal — view, download, invoices, scoped to one client per token
14. 14Background worker — atomic job claiming, heartbeat, restart handling, recovery strategies
15. 15Production deployment path, backup and restore runbooks, security and handoff documentation
16. 16Telegram AI receptionist — RAG knowledge base, FAQs, booking, document triage, staff handoff, owner notifications