Multilingual AI applications — MVP in 4 to 6 weeks
Multilingual AI applications built by developers and linguists together: MVP within 4 to 6 weeks, LLM-agnostic (DeepL Pro, OpenAI, Anthropic and Google plus open-source models) and production-ready from day one.
Language technology that makes your product smarter
Multilingual AI applications built from linguistic and technical insight: chatbots, translation APIs, NLP-driven search and document processing — MVP in 4 to 6 weeks, LLM-agnostic and production-ready from day one.
Multilingual reach as core functionality
DeepL · OpenAI · Anthropic · Google — the right model per use case
What sets our approach apart is the combination of technical engineering talent and native
linguistic expertise. Where purely technical teams treat multilingual reach as a configuration
question, we also understand the linguistic, cultural and quality dimensions that decide whether
an AI language application genuinely works for end users — across 20+ markets.
Language reach
AI applications in 225+ languages
From core EU languages to low-resource markets with custom fine-tuning on your domain data.
We analyse your business case, define functional and non-functional requirements and decide which language-AI technology fits best: LLM choice, translation API, NLP framework, ASR or TTS.
02
Architecture design
Technical design of your multilingual AI application: LLM choice, API integrations, data flow, caching, scalability, privacy and GDPR compliance per market.
03
Agile development
In two-week sprints we build your application iteratively. After every sprint you receive a working product for feedback — no waiting until the end of a multi-month build.
04
Linguistic integration and testing
Native language specialists test the application in every required language for accuracy, cultural fit and user experience. Developer testing alone is not enough for multilingual AI.
05
MVP delivery and continued development
Working MVP within 4 to 6 weeks. From there we guide further development based on real user feedback — including hosting, monitoring and ongoing maintenance.
Agile × linguistics
Building AI apps without linguistic expertise is a technology stunt.
A chatbot running the same scripts across 8 markets without native reviewers fails on nuance and trust. An AI translation API without linguistic QA produces hallucinations at scale. We work as one team — developers, LLM specialists and native language experts — so your AI product not only runs, but reads right.
Where technical teams treat multilingual reach as configuration, we build it from linguistics — with developers, native experts and LLM specialists in one team.
Unlimited multilingual reach
Our applications are designed to be multilingual from the architecture up — not as an afterthought, but as core functionality. Adding a 20th language does not require a refactor.
MVP in 4 to 6 weeks
With Agile development and state-of-the-art AI components we deliver a working MVP in 4 to 6 weeks. Real user feedback gathered quickly, instead of months of stealth build.
LLM, NLP and API expertise
We work with DeepL Pro, OpenAI, Anthropic and Google plus open-source models, NLP frameworks and translation APIs. Model choice driven by use case, not by hype.
From concept to production
Guidance from idea through prototype to production-grade application — including hosting in a customer-configured cloud environment, monitoring, logging and ongoing maintenance.
Quality assurance
From POC to production-ready AI
From model choice to live monitoring — the foundation of AI applications that real users rely on.
Multilingual by designNot an afterthought, but a core function
DeepL · OpenAI · Anthropic · GoogleModel choice tuned to your use case
Two-week Agile sprintsWorking product every sprint
MVP in 4 to 6 weeksReal users and feedback fast
POC to productionHosting · monitoring · maintenance
GDPR-alignedDatacenter configurable on request
From practice
Concrete AI app projects
From chatbot deployments to document extraction and multilingual search — AI applications running in production.
01Finance · Chatbot
Case Study
Multilingual chatbot — bank in 8 markets
A bank launched a customer-service chatbot across 8 markets. LLM with custom domain knowledge, GDPR-aligned hosting (datacenter configurable on request) and fall-back to human agents. MVP in 5 weeks; high self-service rate achieved.
8markets
5 wkMVP
highself-service
02Pharma · NLP
Case Study
Medical document extraction — 14 languages
A pharmaceutical company automated patient-record extraction from scanned documents in 14 languages. Custom NLP pipeline with OCR and a native human QA layer. Throughput considerably higher than manual.
14languages
considerablethroughput
nativeQA
03E-commerce · NLP
Case Study
Multilingual search — e-commerce in 20 markets
A marketplace built an NLP-driven search engine for 20 markets. Semantic search, query-intent detection per language and regional catalogue matching. Measurable improvement in search-to-buy.
20markets
measurablesearch-to-buy
6 wkMVP
Applications
For which AI applications?
8application types
From customer-service chatbots to NLP search and multilingual e-commerce — wherever language intelligence matters.
Multilingual chatbots and virtual assistants
Automated translation APIs
NLP-driven search engines
Document processing and extraction
Speech-to-text applications
Multilingual e-commerce platforms
Language analytics and reporting tools
Medical NLP applications
Trusted by government, legal institutions & global enterprises
HPMinistry of JusticeDSMSiemensASMLAmazonINGCalvin KleinRocheShellEuropean Court of JusticeBoschBMWPhilipsAudi
HPMinistry of JusticeDSMSiemensASMLAmazonINGCalvin KleinRocheShellEuropean Court of JusticeBoschBMWPhilipsAudi
We choose the most suitable model per use case: commercial LLMs from DeepL Pro, OpenAI, Anthropic and Google for chatbots and content generation, open-source models for privacy-sensitive applications, and specialised translation engines for translation use cases. We also work with TMS-tooling such as Phrase TMS, memoQ and Trados Studio. Model choice driven by use case, not by hype. We stay LLM-agnostic so you are not locked into a single provider.
What is an MVP and why is it useful?
A Minimum Viable Product is the smallest workable version of your application with the core functionality. An MVP lets you bring your product to market quickly, gather real user feedback and iterate — without waiting for a fully built-out version. Our MVPs are production-grade, not just prototypes.
Can you extend existing applications with AI?
Yes — we integrate multilingual AI through APIs and microservices into existing applications. Typically faster and more cost-effective than building an entirely new platform. We assess your architecture and recommend the most suitable integration approach, including feature-flag rollouts and A/B testing.
How do you safeguard the quality of language output?
Linguistic quality is core to our practice. We test AI output structurally with native language experts, measure accuracy and fluency per language, and build human review loops in wherever the quality risk is high. The result: applications that are linguistically reliable too — no hallucinations leaking through to production.
Do you handle hosting and maintenance after delivery?
Yes — we offer managed hosting on scalable cloud infrastructure (datacenter location is configurable on customer request, typically EU) including monitoring, logging, updates and technical support. This is optional — you can also host yourself with our handover support. Uptime targets are agreed per project, from best-effort to 99.9%.
How do you handle GDPR and privacy?
GDPR-aligned process. Datacenter location is configurable on customer request for supported tools, typically EU. With commercial vendor subscriptions (DeepL Pro, OpenAI, Anthropic, Google), customer data is not used for model training. GDPR processor agreements available on request. For privacy-critical applications (pharma, finance) we work with on-premise deployable models or private-cloud LLM inference.
How does your pricing model for AI app development work?
Fixed-price projects for the MVP (4 to 6 weeks) or time and materials for continued development. MVP budget depends on complexity, number of languages, LLM cost projections and integrations. For ongoing applications: monthly subscription covering hosting, maintenance and feature development. Full transparency upfront, no surprises.
01Which AI models do you use?
We choose the most suitable model per use case: commercial LLMs from DeepL Pro, OpenAI, Anthropic and Google for chatbots and content generation, open-source models for privacy-sensitive applications, and specialised translation engines for translation use cases. We also work with TMS-tooling such as Phrase TMS, memoQ and Trados Studio. Model choice driven by use case, not by hype. We stay LLM-agnostic so you are not locked into a single provider.
02What is an MVP and why is it useful?
A Minimum Viable Product is the smallest workable version of your application with the core functionality. An MVP lets you bring your product to market quickly, gather real user feedback and iterate — without waiting for a fully built-out version. Our MVPs are production-grade, not just prototypes.
03Can you extend existing applications with AI?
Yes — we integrate multilingual AI through APIs and microservices into existing applications. Typically faster and more cost-effective than building an entirely new platform. We assess your architecture and recommend the most suitable integration approach, including feature-flag rollouts and A/B testing.
04How do you safeguard the quality of language output?
Linguistic quality is core to our practice. We test AI output structurally with native language experts, measure accuracy and fluency per language, and build human review loops in wherever the quality risk is high. The result: applications that are linguistically reliable too — no hallucinations leaking through to production.
05Do you handle hosting and maintenance after delivery?
Yes — we offer managed hosting on scalable cloud infrastructure (datacenter location is configurable on customer request, typically EU) including monitoring, logging, updates and technical support. This is optional — you can also host yourself with our handover support. Uptime targets are agreed per project, from best-effort to 99.9%.
06How do you handle GDPR and privacy?
GDPR-aligned process. Datacenter location is configurable on customer request for supported tools, typically EU. With commercial vendor subscriptions (DeepL Pro, OpenAI, Anthropic, Google), customer data is not used for model training. GDPR processor agreements available on request. For privacy-critical applications (pharma, finance) we work with on-premise deployable models or private-cloud LLM inference.
07How does your pricing model for AI app development work?
Fixed-price projects for the MVP (4 to 6 weeks) or time and materials for continued development. MVP budget depends on complexity, number of languages, LLM cost projections and integrations. For ongoing applications: monthly subscription covering hosting, maintenance and feature development. Full transparency upfront, no surprises.
Social proof
Client testimonials
What clients say about working with Ecrivus — from banking chatbots to medical NLP.
“
★★★★★
Certified translations for our international cases are delivered quickly and carefully. Our project manager knows our account inside out.
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