On-site linguist deployment: 150 specialists in Tokyo.
An AI company needed language specialists on-site in Tokyo, working alongside its engineering team, where direct collaboration moved model refinement faster than a remote workflow could.
Tokyo, on-site - 150 deployed, steady production team after ramp - Model refinement with direct engineering collaboration
110,000+
native linguists and AI data contributors
· Founder-reported combined network · 4 Oct 2026
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare, indigenous and low-resource languages
1,000+
organizations served since 2015
Measured outcomes
On-site linguist deployment
The project
On-site linguist deployment
- Client
- confidential AI company (on-site, Tokyo)
- Service
- On-site language specialists for model refinement
- Location
- Tokyo, on-site
- Scale
- 150 specialists deployed
An AI company needed Japanese-language specialists working on-site in Tokyo, embedded with its engineering team rather than delivering from a remote pool.
The goal was speed of iteration: model refinement moves faster when linguists and engineers can resolve questions in the same room.
The problem to solve
Remote workflows add a lag to every clarification, and model refinement depends on tight, repeated feedback between linguists and engineers.
Standing up 150 qualified Japanese-language specialists on-site, then narrowing to a steady production team, needed both reach and on-the-ground coordination.
What MoniSa changed
MoniSa sourced and deployed 150 specialists on-site in Tokyo, then settled into a focused production team working directly with the engineering group.
On-site deployment
Specialists worked in Tokyo alongside the engineering team, not from a remote queue.
Scale then focus
A 150-person deployment narrowed to a steady production team as the work found its rhythm.
Direct collaboration
Linguists and engineers resolved questions in person, shortening the refinement loop.
Results
Measured outcomes from this engagement.
150 specialists were deployed on-site in Tokyo, settling into a steady production team that worked directly with the engineering group on model refinement.
| Location | Tokyo, on-site |
|---|---|
| Specialists | 150 deployed, steady production team after ramp |
| Focus | Model refinement with direct engineering collaboration |
| Outcome | Faster iteration than a remote workflow |
What supported the result
Why the fit was real
The engagement needed on-the-ground reach to source 150 specialists in one city, plus the coordination to run them on-site.
What decided the result
On-site placement is what shortened the feedback loop between linguists and engineers.
What buyers can reuse
- Some model-refinement work moves faster on-site than through any remote pipeline.
- Deploying at scale and then narrowing to a steady team kept the on-site program focused.
- The evidence keeps the client details confidential and attributes the metrics only to this engagement.
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.
Services and guides
Related services
Languages named
Examples referenced in the engagement.
- Japanese
- On-site language specialists
- Model refinement support
More proof
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Operating response
The change that was actually made.
Not the result — the decision taken mid-programme that the result depended on.
In the room with the engineers
Specialists worked in Tokyo alongside the engineering group rather than from a remote queue, so a question took a minute rather than a cycle.
case evidence
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Buyer questions
Common questions.
What was delivered on this engagement?
Location: Tokyo, on-site. Specialists: 150 deployed, steady production team after ramp. Focus: Model refinement with direct engineering collaboration
What control kept the work stable?
On-site placement is what shortened the feedback loop between linguists and engineers.
Where should similar work go next?
Use AI data services for the delivery model, AI data annotation vendor guide for buyer-side evaluation, and the contact page for a scoped brief.
What happens if you cannot staff one of my language pairs?
For the proposed project, ask for pair-by-pair availability or a recruitment window in writing before agreeing a date. Define qualification and pilot approval for any new contributor before live work. A coverage claim should be checkable before the scope is signed.
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