Every enterprise we talk to hits the same two walls.
The first is the coordination tax on consistency.
Your Android app is built by one team. Your web app by another. Your marketing site, your docs, your internal tooling – each owned by a different team, each with its own release cadence, its own reviewers, its own shipping pipeline.
Legacy tools can share translation memory and glossaries across projects. Workspaces exist. Org-level assets exist. But shared is not enforced. A term in the shared glossary is a suggestion to the translator, not a constraint on the model. Consistency across teams becomes a discipline. Someone keeps the glossary aligned. Someone resolves the term-of-art conflicts between teams. Someone chases the team that translates a call-to-action one way while another team ships it differently. The consistency is possible. The maintenance is continuous.
Inside each team's project, the drift compounds further. Translation memory holds consistency as long as segments don't change. In a codebase that refactors every week, segments change every week. Our RAL research measures how fast terminology drifts when the model has no retrieved context.
The second wall is the cost of ever leaving the tools that produce the tax. In an enterprise, every dimension multiplies – glossaries accumulated across teams, translation memory built up in TMX and vendor-proprietary formats across projects, connectors wired into each team's CI, translator rosters negotiated through procurement, SSO integrated with the IdP.
The migration reads as a multi-quarter engineering program no localization manager wants to own.
Two architectures fit the multi-team shape.
One replaces project-scoped translation memory with an organization-scoped localization engine that retrieves context at inference time. One glossary, one brand voice, every team's app pulls from the same localization engine.
The other replaces customer-runs-the-migration with a forward-deployed localization engineer from Lingo.dev who does the migration on our clock, not yours.
Both patterns already run every other piece of infrastructure in your stack – now, we want localization to finally catch up.
Architecture #1: the localization engine#
Localization engine
A stateful translation API that teams create on Lingo.dev, configured per organization. Each localization engine persists its own glossary, brand voice, locale-specific instructions, and ranked model chain. Every request retrieves matching glossary terms, injects them into the model's context window before the first token is generated, and is independently scored after completion. The first translation benefits from zero context; the thousandth benefits from everything.
A localization engine holds consistency at the term level, not the segment level. It is scoped to your organization, not to any single team's project. One glossary, one brand voice, every team's surface pulls from the same localization engine.
A glossary entry for "Submit" fires on every Spanish surface – button, email subject, tooltip. Web team or mobile team, it does not matter. Retrieval matches meaning, not strings. One entry for "Deploy" fires on "deploying", "deployment", "Deploy your app" – no separate entry for each form.
A brand voice is attached to the localization engine per locale. Every request uses it.
Instructions are discrete, testable rules scoped to a locale. Abbreviation conventions, non-breaking spaces, quotation marks – each debuggable on its own.
A model chain routes each request to the primary model with ranked fallbacks. Swap providers without touching the glossary.
An AI reviewer runs on an independent model. It scores every request against the glossary and each instruction separately. Pass/fail with reasoning, tracked as a time series.
| Concern | Project-scoped tooling | Organization-scoped localization engine |
|---|---|---|
| Scope of consistency | Per project, per team | Per organization |
| Consistency unit | Whole segment, keyed by hash | Individual term, matched semantically |
| Survives source rewrites | No | Yes |
| Cross-app, cross-team | Discipline; humans keep it aligned | Architectural; the localization engine keeps it aligned |
| Quality measurement | Rule-based checks (tags, numbers) | Per-request LLM scoring |
| Model flexibility | Provider lock | Ranked chain |
| Authority over output | Translator discretion | Glossary overrides model |
Drift becomes a condition you can measure, not a condition you absorb. The glossary fires on every request. The AI reviewer verifies compliance per request.
The named mechanism is retrieval augmented localization (RAL). At inference time, the engine decomposes the input into n-gram phrases, embeds them, and runs cosine similarity search against the glossary's vector index. Matched terms go into the model's context window before the first token is generated. Structurally identical to RAG, applied to translation.
In a controlled evaluation across multiple LLM providers and multiple European languages, RAL reduced terminology errors by 17–45%. 42,000+ paired quality judgments. Holm-Bonferroni corrected p < 0.001 on every provider. Holistic quality scores could not detect the gap at all.
Architecture #2: forward-deployed localization engineering#
The second wall is migration. You have a working stack. It produces the tax, but it works. The cost of replacing it – engineering time, integration rework, translator reonboarding, historical data migration – consistently exceeds the cost of paying the tax.
That calculation is why the tax still gets paid. After watching the same migration bottleneck block serious enterprises from moving, we decided to absorb the migration ourselves.
When Lingo.dev onboards an enterprise, our engineers do the migration. Not as a professional-services contract layered on top of the license. As the default onboarding path.
A forward-deployed localization engineer reads your glossary, your brand-voice documents, your connector configuration, your translator contracts. They import your translation memory from TMX and your glossary from whatever legacy format it lives in. Nothing gets re-derived. They build the localization engine on Lingo.dev with your terminology preloaded. They wire it into your CI. They pipe your translator roster through the async pipeline so the humans you trust stay in the loop.
The multi-team case is where the architecture pays off. In the legacy version, aligning terminology across teams means N synchronized migrations – each team re-deriving keys and TM inside its own project. Here the localization engine is built once. Each team wires its app to it on its own cadence. Cross-app consistency shows up on the first locale that hits the engine, not after every team finishes its own migration.
Our engineers stay with you through your next deployment to production, and the one after that, until your internal team owns the system.
This is how we onboard enterprise customers.
By the time a multi-team org is shipping every week, translation cannot be a procurement ticket between buyer and vendor. It has to run alongside your next deployment to production, not after it. Forward-deployed engineering is how Palantir, Scale AI, Ramp, and other infrastructure vendors have onboarded enterprise customers for over a decade.
Now, we want localization to finally catch up.
Audit
A Lingo.dev engineer reads your source repos, your existing TM (including TMX exports), your glossary, your connectors, and your translator contracts – across every team that owns a surface. They produce a migration plan with order and timeline. You own the plan.
Engine built to match your current quality
We configure the localization engine with your imported glossary, your brand voice per locale, and your translator pipeline. Before any production traffic, we run a side-by-side comparison – your current tool's output versus the engine's, same strings, same week. You decide whether the quality holds.
Wired into each team's CI
No rip-and-replace. The localization engine runs as one step in each team's existing pipeline. Merge flows, review flows, reviewers – all stay the same. The engine replaces the old step.
Cutover at your cadence
One team, one locale pair first. Then three. Then the rest. You choose the order. We run the comparison at each step. Rollback is one commit.
Transfer to your team
Our engineer hands the system off to your platform team – docs, runbooks, and an on-call rotation we cover until they take it over.
Evidence#
Research. The RAL study: 42,000+ paired quality judgments across multiple LLM providers and multiple European languages. Holm-Bonferroni corrected p < 0.001 on every provider. Terminology error reduction ranged 17–45%.
Configuration over model choice. We found that across Mistral, Gemini, Claude, GPT – any model + a good glossary, brand voice, and context setup consistently produces shippable, reference-quality translations at a fraction of the cost. Not because we improved the model. On every request, the localization engine retrieves the matching glossary terms, brand voice, and locale instructions by similarity search, and injects them into the model's context window before the first token is generated.
Production scale. 200M+ words translated on the platform.
Named customers. Mistral, Solana, SoSafe, Cal.com.
Scope#
Lingo.dev serves localization teams of many shapes – single-product companies, open-source projects, mobile-only teams, enterprise platforms. The architecture described here is the one tuned for enterprises with several teams shipping several apps across 20+ locales.
What happens next#
The first step is a two-week pilot. One team, one locale pair.
A forward-deployed localization engineer sits with your localization owner and your engineering lead. We study your workflow. We set up a measurement system so you can see the quality of translations in languages your team does not speak – AI reviewers running on independent models, scoring each translation against your glossary and your rules. The scoring is adapted from MQM, the standard framework for translation quality evaluation.
We build the localization engine against your glossary and your brand-voice documents. We run it on your source content, side-by-side with your current tool. You see the delta and decide.
From there, we schedule the migration for the remaining teams and locales on your clock, not ours.
Talk to our localization engineering experts today.
