Two practices.
One engineering team.
For sixteen years Linguanational has handled language for Chicago institutions that cannot afford a mistake. Then we built the AI platform that does the work — and now we build systems like it for other organizations too.
- 55.8M
- Words processed
- 153
- Language pairs
- 2,005h
- Audio transcribed
- 98.0%
- Automated QA pass rate
- 10.3 min
- Median turnaround
in production, to date
104 target languages
multi-speaker included
12 checks on every job
per job, end to end
Live from the platform we operaterefreshed every 60s
What we do
Buy the outcome, or buy the system that produces it.
Practice one
Translation as a Service
Software stopped being something you buy in a box. Translation is going the same way. Same linguists, same review, same sixteen years of institutional work — delivered as a service you plug into instead of a project you re-quote every time.
- Translation & certified translation
- Website & software localization
- Interpreting
- Audiovisual & media
- Education & eLearning
- Human review & linguistic QA
Practice two
AI solutions
Document automation, OCR, speech, multilingual systems and the evaluation harnesses that prove they work — built for your data and deployed where your data is allowed to live.
- Document & workflow automation
- Document intelligence & OCR
- Multilingual AI systems
- Speech & audio AI
- Evaluation & quality harnesses
- Private & on-premise deployment
- Integration, APIs & webhooks
Translation as a Service
If you buy translation from us today, nothing you rely on is going away.
You already know this model
Software stopped being something you bought in a box and became something you subscribe to. Translation is going the same way — and we have already built the version of it that works.
The service does not change. The delivery does.
The same linguists, the same terminology, the same review. What changes is that it arrives through an API, a portal or your own systems instead of a quote and a purchase order every time.
Capacity you do not have to schedule
No re-quoting for each batch, no waiting for a vendor to have availability. Volume moves up and down and the service absorbs it.
Priced like a service, not a project
Per word, per hour or committed volume — whichever your procurement office needs. Predictable enough to budget a year out.
Why believe the AI claim
We are not integrating someone else's AI. We run our own.
The numbers on this page come out of the platform we operate with Flix Translations Group LLC. They update on their own. If a number looks bad one week, it stays on the page.
- Our own pipelines, our own infrastructure, our own quality gates
- 12 automated QA checks on every single job, not on a sample
- Human review scored on a formal error typology with severity weighting
- Deployable in your environment when the data cannot leave it
Production volume by file type
- audio5,921 jobs · 19,187,455 words
- pdf602 jobs · 17,208,161 words
- rtf475 jobs · 2,893,107 words
- docx461 jobs · 1,884,443 words
- batch197 jobs · 917,147 words
- txlf488 jobs · 473,453 words
- sdlxliff206 jobs · 306,976 words
- mqxliff66 jobs · 101,647 words
- xml67 jobs · 15,624 words
- pptx5 jobs · 6,597 words
Live production counters from the platform Linguanational operates with Flix Translations Group LLC. Refreshed every 60 seconds.
Who we work with
Four sectors where language and automation are the same problem.
Client work
Chicago Public Schools
Family communications for a district of 400,000 students
One of the largest school districts in the United States — over 600 schools — where families arrive from dozens of language backgrounds and every notice has to reach all of them.
Read the case study- 600+
- schools served by the district
- ~400K
- students in the district
Working with an institution this size means insurance, records handling and audit expectations are already routine for us — which is usually the part that stalls an AI vendor in procurement.
Procurement packageWhy Linguanational
What a Chicago AI shop usually can't say.
We operate an AI platform, we don't resell one
Most firms pitching AI in Chicago are integrating somebody else's API. We run our own production system: our own pipelines, our own infrastructure, our own quality gates. The numbers on this page come out of it.
We measure quality instead of asserting it
Every job runs through a 12-step automated QA chain, and human review is scored on a formal error typology. When we say a system works, we can show you the evaluation set it works on.
We already clear institutional procurement
Working with a district the size of Chicago Public Schools means insurance, records handling, background requirements and audit expectations are not new to us.
Language is the hard version of the problem
A team that can keep a Trados tag pair intact across 101 target languages can handle your invoice extraction. The reverse is rarely true.
Chicago, since 2009
Same city, same time zone, sixteen years of institutional clients here. You can meet the people who will do the work.
Your data can stay where it is
Open-weight models deployed in your environment when the material cannot leave it — which is where most public-sector and healthcare AI projects actually get stuck.
How an engagement runs
Two weeks to know whether this is worth building.
Discovery sprint
2 weeks · fixed fee
We work on your real data, not a demo set. You get a written technical plan, a working prototype of the riskiest part, and a build estimate you can take to procurement. If the answer is that AI is the wrong tool here, we write that down too.
- Technical plan
- Prototype on your data
- Fixed build estimate
Build
6–12 weeks typical
Fixed scope, milestone billing, a demo every two weeks. We build against the evaluation harness from day one, so 'done' is a measurement and not a feeling. Deployed to your infrastructure or ours.
- Working system in your environment
- Evaluation harness + baseline
- Documentation and handover
Run
Ongoing or handover
Either we operate it under an SLA with monitoring and on-call, or your team takes it and we stay available for the first two quarters. Both are priced up front. No surprise managed-service lock-in.
- SLA-backed operation
- Monitoring & alerting
- Or clean handover
Where we are
A Chicago company, working on Chicago's problems.
Chicago is a city where more than a hundred languages are spoken in the public schools alone. That is not a side constraint on the technology work here — it is the technology work.
“The hardest problems in applied AI are the ones where being almost right is worse than being obviously wrong.”
A benefits letter, a court filing, a discharge instruction, a safety procedure. That is the category we have worked in since 2009, and it is why we build evaluation before we build features.
Next step
Tell us what is taking too long.
A translation program that cannot keep up, an archive nobody can search, a back office retyping PDFs. Start with the bottleneck and we will tell you honestly whether AI is the right tool for it.