Margin disappears in handoffs
PM coordination, file movement, reviewer routing, and exception recovery consume the time automation was meant to save.
expertise / 01
For LSPs, AI-data companies, and enterprise localization teams
Turn multilingual operations into an owned software advantage.
Aurix builds the operating layer between content, models, linguists, reviewers, vendors, and customers—when generic tools no longer fit the service you need to deliver.
operating map / 01production path
01Ingestcontent + data
02Orchestratemodels + rules
03Reviewhuman judgment
04Delivertraceable output
AI + data
human controls
workflow logic
production ops
The shift / why now
Language and AI-data operations are becoming software businesses.
Customers expect faster turnaround, more formats, defensible quality, and AI-enabled economics. The constraint is rarely access to another model. It is the operating system that turns models, people, and exceptions into a reliable service.
Quality becomes difficult to defend
When model output and human decisions live in separate tools, the final score has no complete operational history.
New services wait on vendor roadmaps
A differentiated workflow becomes a workaround—or never launches—because the platform was designed for the average operation.
Operational failure map
The signals that software has become the constraint.
What you seeWhat it costsWhat we change
What you see
A new customer workflow becomes a spreadsheet chain.
What it costs
Every exception adds coordination cost and slows delivery.
What we change
Model the workflow as explicit states, permissions, and recoverable handoffs.
What you see
AI output reaches production without a decision trail.
What it costs
Reviewers repeat work and quality teams cannot explain the result.
What we change
Put evaluation, human gates, and evidence capture inside the pipeline.
What you see
Each modality needs another disconnected tool.
What it costs
Text, image, audio, and annotation operations fragment the customer experience.
What we change
Create a shared operating layer with modality-specific work surfaces.
What Aurix builds
The product, AI, and operating layers—together.
We do not force an operational problem into a single capability. The system is composed around the result the business needs to own.
build / 01Localization + AI data
Localization workflow platforms
Customer intake, TMS and CAT integration, engine routing, vendor workspaces, file processing, and delivery in one traceable flow.
TMS APIsfile fidelityvendor portals
build / 02Localization + AI data
Quality and evaluation systems
AI-assisted detection, MQM scoring, reviewer arbitration, model evaluation, and decision-grade reporting.
MQMHITLaudit trail
build / 03Localization + AI data
AI-data operating systems
Collection, annotation, preference and summary evaluation, quality control, and feedback loops built for the methodology.
annotationevaluationdrift loops
build / 04Localization + AI data
Multimodal production pipelines
Image, audio, voice, and structured-content systems that combine automation with role-specific human judgment.
OCRASRmultimodal
Two ways teams arrive
Different starting points. The same production standard.
Agencies + LSPs
Extend your technology capability without building a second company.
Launch a differentiated service, replace an operational bottleneck, or give an enterprise customer the workflow their standard stack cannot support.
- New AI-enabled service lines
- Customer-specific platforms
- Internal workflow modernization
Enterprise teams
Own the system behind multilingual and AI-data delivery.
Connect internal content, models, reviewers, and governance without forcing sensitive operations through a generic external workflow.
- Specialized internal systems
- Region-controlled processing
- Cross-team operating layers
Anonymous client work / 03
The problem, the system, and what changed.
Client identities and sensitive details remain protected. The operating problem and engineering response are shown plainly.
Language quality/01
A defensible quality operating system for high-volume review.
Problem
Quality decisions were split across AI output, reviewer files, and manual escalation, making review slow and difficult to audit.
Built
A multi-stage quality platform with model-assisted detection, reviewer routing, arbitration, resumable processing, and segment-level evidence.
Outcome
Review cycles became easier to operate, quality decisions stayed inspectable, and manual coordination moved into the system.
Outcome focus
Review time + cost of quality
fewer handoffsfaster reviewtraceable decisions
Read the technical case AI data + human feedback/02
A shared data factory for multiple evaluation methodologies.
Problem
Separate annotation initiatives were creating inconsistent tooling, duplicated infrastructure, and no durable route from production feedback back to data.
Built
Methodology-specific workspaces on a shared versioned backend, with asynchronous processing, quality controls, and human-feedback loops.
Outcome
New evaluation workflows could reuse one production foundation while keeping each methodology’s decisions explicit.
Outcome focus
Setup time + operating cost
shared infrastructurefaster onboardingreusable workflows
Read the technical case Multimodal localization/03
An image-localization pipeline that removed rebuild-by-hand work.
Problem
Image text extraction, translation, design reconstruction, and vendor review were separate manual steps with limited traceability.
Built
A connected OCR, inpainting, translation, rendering, and canvas-review workflow with glossary support and structured export.
Outcome
Teams could move assets through one auditable pipeline, reducing repetitive production effort and unnecessary handoffs.
Outcome focus
Turnaround time + unit effort
higher throughputless reworkone review surface
Read the technical case 01
Cycle time
From intake to approved delivery
02
Unit cost
Effort and infrastructure per processed asset
03
Quality cost
Review, rework, and escalation effort
04
Throughput
Work completed without adding coordination load
Delivery and data controls
Global delivery with explicit boundaries.
Trust is handled as part of delivery design: who can access what, where systems run, and how collaboration works across regions.
See how we partnerWorking-hour overlap
Structured European and US working-hour overlap for direct collaboration with product and operations teams.
Commercial confidentiality
NDA and data-processing agreements can be established before sensitive discovery or access begins.
Controlled deployment
Region-controlled hosting and client-owned infrastructure are supported where the operating model requires them.
Restricted production access
Production-data access is intentionally limited and aligned to the work being delivered.
Documented practices
Security and delivery practices are documented so controls can be reviewed instead of assumed.
Explore another operating environment
Specific first step / no generic sales call
Book a workflow architecture session.
Bring one workflow, bottleneck, or service line. We will map the handoffs, technical constraints, and strongest path to a production system.
Choose a time