Case study
The toolchain said no.
A partner needed 400 pages of desktop publishing across 7 languages. Five ran on the standard workflow, one ran on Arabic, and one — Punjabi — hit a toolchain that does not support it.
400 - 7 - 5 languages
400
Pages delivered
7
Languages
5 languages
Standard workflow
Measured outcomes
DTP, Punjabi encoding
Project overview
What landed, and what made it hard.
A partner needed 400 pages of desktop publishing across 7 languages. Five ran on the standard workflow, one ran on Arabic, and one — Punjabi — hit a toolchain that does not support it.
Delivery snapshot
DTP, Punjabi encoding
- Client
- confidential travel technology partner
- Service
- Multilingual desktop publishing
- Volume
- 400 pages
- Languages
- 7, including Arabic (SA) and Punjabi
- Key issue
- Punjabi PDF encoding not natively supported
Why this mattered
Outcome before process.
PDF accessibility tooling does not natively handle Punjabi's encoding. That is a tooling limitation rather than a language problem, and it does not resolve itself by trying harder or waiting for a patch.
The practical consequence is that the reading order — the sequence assistive technology uses to traverse a document — cannot be derived automatically for Punjabi pages.
MoniSa handled the work under ISO 9001:2015 for process control and ISO 27001:2022 for information handling. ISO 17100:2015 is scoped to translation, so it is not claimed for this work.
The delivery question was therefore not linguistic. It was whether one unsupported language would delay a 400-page multi-language delivery, and the answer was no.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Desktop publishing is where multilingual projects fail after the translation is already correct. The text can be perfect and the document still be unusable.
The challenge
The problem to solve
Encoding support is the least visible version of that failure. It does not surface as a wrong word; it surfaces as a document that renders acceptably to a sighted reader and traverses incorrectly for anyone using assistive technology.
Punjabi specifically is not natively supported by the PDF accessibility toolchain, so the automatic reading-order derivation that works for the other six languages simply does not apply.
That leaves three options, and two of them are bad: drop the language, delay the whole delivery until tooling changes, or solve it manually. Only the third preserves both scope and schedule.
A 7-language batch also has a coupling problem. If one language blocks, a partner usually receives nothing rather than six-sevenths, because delivery is organized as a batch.
Arabic (SA) added right-to-left layout handling alongside everything else, which is well-supported but still a distinct workflow from the Latin-script languages in the same batch.
Operating response
What MoniSa changed
Five languages were delivered on the standard DTP workflow without modification, so the majority of the 400 pages was never at risk from the Punjabi issue.
- Isolate the blocked language Five languages ran the standard workflow untouched, so one unsupported language could not put the other 400-page batch at risk.
- Manual reading-order repair Punjabi reading order was fixed by hand, preserving the assistive-technology traversal the toolchain could not derive.
- RTL as its own workflow Arabic (SA) was handled with right-to-left layout handling rather than pushed through the Latin-script path.
- Decoupled delivery The batch was structured so a blocked language delayed itself, not the other six.
Results
Measured outcomes from this engagement.
400 pages were delivered across 7 languages, with the Punjabi encoding limitation solved rather than escalated.
| Pages delivered | 400 |
|---|---|
| Languages | 7 |
| Standard workflow | 5 languages |
| Manual intervention | Punjabi reading order |
| Schedule impact | None — delivered without delay |
Selection logic
What protected the result.
The selection came down to whether MoniSa could source and review the work at standard, and whether that would hold across the full run.
Why the fit was real
Why the fit was real
The work needed multilingual DTP capacity plus willingness to hand-solve an encoding limitation rather than escalate it back to the partner.
What decided the result
What decided the result
Manual reading-order repair preserved accessibility for the one language the toolchain could not handle, without delaying the other six.
What buyers can reuse
What buyers can reuse
- Ask which languages your DTP and accessibility toolchain natively supports. The unsupported ones are where a multilingual document delivery fails after translation is already correct.
- Reading order is an accessibility property, not a formatting preference. A document can look right and traverse wrongly for assistive technology.
- Structure multi-language batches so one blocked language delays itself, not the delivery. Coupled batches turn one limitation into a total slip.
- Right-to-left languages need their own workflow. Forcing them through a Latin-script path produces layout defects that survive review.
- Ask what a vendor does when tooling does not support a language: drop it, delay it, or solve it manually. The answer tells you what kind of partner they are.
- A useful DTP brief names the languages, the accessibility standard, the toolchain, and the fallback for unsupported encodings.
- Unglamorous manual work is often the deliverable. A partner unwilling to do it will hand the problem back to you.
Continue from this proof
Useful comparisons for the same problem.
Use these links to compare the case with the matching service, buyer guide, and language coverage.
Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- Punjabi
- Arabic (SA)
- PDF accessibility
- Reading order repair
More proof
Related proof
Compare this case with Enterprise localization terminology recovery and Document AI and OCR annotation to judge whether the operating pattern fits your brief.
case evidence
Nearest proof pattern.
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Buyer questions
Ask the questions weak vendors avoid.
Short answers for buyers checking fit, coverage, quality method, and next-step readiness.
What was delivered on this engagement?
Pages delivered: 400. Languages: 7. Standard workflow: 5 languages
What control kept the work stable?
Manual reading-order repair preserved accessibility for the one language the toolchain could not handle, without delaying the other six.
Where should similar work go next?
Use Localization services for the delivery model, Localization QA buyer guide for buyer-side evaluation, and the contact page for a scoped brief.
Similar brief
Send the constraint behind the metric.
A useful follow-up to a case study names the language mix, review model, deadline, and what proof your buyer team needs before approval.
Production-ready brief
01Closest matching challenge from this case02Language pair, dialect, and script coverage03Volume, cadence, or hours to deliver04Reviewer model and acceptance criteria05Security or platform constraints06Proof needed for stakeholder approvalCapability at a glance
The answers most briefs open by asking for.
Buyers rarely start with who we are. They start with a list of fields to fill. Here are ours, so the first email can be about the work instead.
- Languages and locales
- 300+ languages and 4,500+ dialects, quoted per locale rather than per language — because the dialect decides whether a dataset is usable, whether a market accepts a release, and which specialist the work goes to.
- Specialist network
- 110,000+ verified language specialists — linguists, annotators, and reviewers — plus voice talent and subtitlers, matched to the language, domain and task before assignment.
- Capacity and mobilisation
- Named availability confirmed per pair before scoping. Coverage is reported as staffed today or needing a recruitment window, in writing, before a launch date or release window is agreed.
- Sourcing constraints
- Specialists can be sourced against geographic, residency, locale and demographic requirements — including native-only, in-country, and speaker-diversity quotas where a data programme demands them.
- Deliverables and specs
- Work is delivered to the receiving specification: structured formats and schemas for data and annotation work, and timed-text, audio and platform conformance for media — subtitle reading speed, line limits, cue timing, channel and sample-rate requirements included.
- Comparable work
- 62 documented case studies stating the scope, the constraint that made it difficult, and the measured result — across AI data programmes, partner overflow, and media releases. 2,000+ AI projects delivered and 1,000+ brands served since 2015.
- Certifications
- ISO 9001:2015 quality management, ISO 27001:2022 information security, and ISO 17100 translation services — scoped to translation specifically, and stated that way rather than implied across every line.
- Commercial basis
- Quoted in the unit the work is measured in — per word, per audio hour, per approved hour, per finished minute, per batch, per item — with what the unit includes stated alongside it, whether the quote is for you or for a client you quote onward.
Need this against your own template? Convert your scope between units and check the deadline, then send the brief with your language list, content type, volume and deadline, and the acceptance criteria you will judge the output against — those four decide feasibility, and the reply addresses them directly.