# Lingo.dev gives localization teams quality-scored, stateful translation infrastructure

Canonical: https://mudpie.ai/companies/lingodotdev/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [Lingo.dev gives localization teams quality-scored, stateful translation infrastructure](https://mudpie.ai/companies/lingodotdev/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

Lingo.dev is for product and engineering teams that need translations to behave like production infrastructure: consistent terminology, measurable quality, repeatable locale rules and a human review path when the score is not good enough.

## What it does

Lingo.dev lets teams build stateful localization engines around LLMs. An engine can hold glossaries, brand voice, locale-specific rules and model preferences, then run through an API, CLI, CI/CD pipeline, code repository or CMS. Translations receive quality scores, and the team can route low-scoring content to native speakers before it goes live. [Lingo.dev homepage](https://lingo.dev/en) [Lingo.dev docs](https://lingo.dev/en/docs/platform)

The advantage is visibility in languages the product team cannot read. Instead of choosing one translation model and hoping, a team can define its own criteria, trace a low score to a model, rule or missing term, and use corrections to tune the engine. That is a different product from a one-off “translate this string” API.

The public pricing page lists Sandbox at $0 per month, Production at $99 per month plus usage, and Enterprise as custom. LLM provider cost passes through without markup; Lingo.dev infrastructure is listed at $2 per million tokens and AI review runs at $0.01 each. Production includes five million tokens per day, 30-day retention and repository integrations; Enterprise adds human review, governance, SSO, RBAC and data-residency options. [Lingo.dev pricing](https://lingo.dev/en/pricing)

The tradeoff is operational discipline. A glossary and scorecard can make quality measurable, but they still need good locale criteria and native review for consequential copy. The company says it is SOC 2 Type II compliant and has processed more than 300 million words; those are company-published claims, not an independent quality audit.

The [YC profile](https://www.ycombinator.com/companies/lingodotdev) identifies Max Prilutskiy as an engineering leader with Typeform experience and Veronica Prilutskaya as a data-science and AI operator. Their own localization experience explains the product wedge.

## My editorial take

I would shortlist Lingo.dev when translation drift is slowing releases or damaging a product’s voice across markets. Start with one product surface and a small locale set, define the scorecard and decide which strings require human approval. If the team only needs occasional translation, the production infrastructure will be unnecessary.

## Quick facts

| Field | Sourced detail |
| --- | --- |
| Buyer | Product, engineering and localization teams |
| Product | Stateful LLM translation engines, scoring and human review |
| Pricing | Sandbox $0; Production $99/month + usage; Enterprise custom |
| Integrations | API, CLI, CI/CD, repositories, CMS and webhooks |
| Main fit question | Is localization quality and release consistency the real bottleneck? |

## Sources checked

Lingo.dev’s YC profile, homepage, pricing page and platform docs were checked on 2026-09-19.

## Cohort context

Lingo.dev is listed in Fall 2024. In our 2026-09-18 directory snapshot, 57 of 94 listed companies in that cohort have YC’s primary industry label B2B (60.6%). This is a current-directory comparison, not an original intake count or a performance ranking. [Nine-cohort dataset](https://mudpie.ai/research/yc-cohorts-2026-09-19.json).

## Public website snapshot

Observed 2026-09-19T16:14:40.575Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

| Signal | Homepage observation |
| --- | --- |
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Not observed in this response |
| Docs/developer link | Observed |
| Pricing link | Observed |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |

[Public observations](https://mudpie.ai/research/yc-homepage-links-2026-09-19.json) · [Collection method](https://mudpie.ai/research/yc-homepage-methods/README.md). Missing links here do not establish that a capability or file is absent elsewhere.


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.