Intelligent AI Starts With LingIQ
Intelligent AI Starts With
LingIQ
We optimize Generative AI—LLMs and agentic systems—through research, fine-tuning, and intelligent design for diverse real-world use cases.
About Us
LingIQ is a team of linguists, ML experts, and UX researchers improving LLMs via high-quality data, precise fine-tuning, and rigorous E2E evaluation.
With experience at Apple, Google, Meta, and AWS, we understand how the intersection of language, culture, and data helps build more accurate, inclusive, and user-aligned AI.
Contact us for collaboration and consultation, or explore how LingIQ can enhance your models.
LingIQ is a team of linguists, ML experts, and UX researchers improving LLMs via high-quality data, precise fine-tuning, and rigorous E2E evaluation.
With experience at Apple, Google, Meta, and AWS, we understand how the intersection of language, culture, and data helps build more accurate, inclusive, and user-aligned AI.
Contact us for collaboration and consultation, or explore how LingIQ can enhance your models.
Benefits
Why Choose LingIQ?
The Benefits of Culturally-Aware, Human-Centered AI:
Proven Expertise
Our team brings data and AI experience from Apple, Google, Meta, and AWS
Proven Expertise
Our team brings data and AI experience from Apple, Google, Meta, and AWS
Proven Expertise
Our team brings data and AI experience from Apple, Google, Meta, and AWS
Sociolinguistic Intelligence
We build AI that's accurate across languages, dialects, and cultural contexts.
Sociolinguistic Intelligence
We build AI that's accurate across languages, dialects, and cultural contexts.
Sociolinguistic Intelligence
We build AI that's accurate across languages, dialects, and cultural contexts.
Multilingual Excellence
Fine-tune models in 10+ languages with deep linguistic and cultural accuracy.
Multilingual Excellence
Fine-tune models in 10+ languages with deep linguistic and cultural accuracy.
Multilingual Excellence
Fine-tune models in 10+ languages with deep linguistic and cultural accuracy.
High-Quality Data
From curation to annotation, we ensure training data is clean, consistent, and representative.
High-Quality Data
From curation to annotation, we ensure training data is clean, consistent, and representative.
High-Quality Data
From curation to annotation, we ensure training data is clean, consistent, and representative.
Ethical Data Practices
We source, curate, and generate data responsibly to reflect diverse perspectives and reduce harmful bias.
Ethical Data Practices
We source, curate, and generate data responsibly to reflect diverse perspectives and reduce harmful bias.
Ethical Data Practices
We source, curate, and generate data responsibly to reflect diverse perspectives and reduce harmful bias.
Rigorous Model Optimization
We fine-tune LLMs and agentic systems for specific domains, languages, and real-world use cases.
Rigorous Model Optimization
We fine-tune LLMs and agentic systems for specific domains, languages, and real-world use cases.
Rigorous Model Optimization
We fine-tune LLMs and agentic systems for specific domains, languages, and real-world use cases.
Versatile Use Cases
We optimize AI for diverse use cases and domains like literature, film and more.
Versatile Use Cases
We optimize AI for diverse use cases and domains like literature, film and more.
Versatile Use Cases
We optimize AI for diverse use cases and domains like literature, film and more.
User-Centric Design
We ensure AI interactions are accessible, inclusive, and intuitive.
User-Centric Design
We ensure AI interactions are accessible, inclusive, and intuitive.
User-Centric Design
We ensure AI interactions are accessible, inclusive, and intuitive.
Real-World Impact
We train AI to be technically robust, socially responsible, and widely usable.
Real-World Impact
We train AI to be technically robust, socially responsible, and widely usable.
Real-World Impact
We train AI to be technically robust, socially responsible, and widely usable.
Our Services
AI That Works for Everyone
At LingIQ, we help organizations build smarter, more inclusive AI. Our team blends machine learning expertise with deep linguistic insight to deliver scalable, real-world solutions for global users.
UX Research & Consulting
LLM Fine-Tuning
Synthetic Data Creation
LLM Audits & Data QA
Chatbot Creation
Agentic AI Development
Understanding users
We conduct end-to-end user research to optimize AI interactions—ensuring accessibility, inclusivity, and user-centered design.
UX Research & Consulting
LLM Fine-Tuning
Synthetic Data Creation
LLM Audits & Data QA
Chatbot Creation
Agentic AI Development
Understanding users
We conduct end-to-end user research to optimize AI interactions—ensuring accessibility, inclusivity, and user-centered design.
UX Research & Consulting
LLM Fine-Tuning
Synthetic Data Creation
LLM Audits & Data QA
Chatbot Creation
Agentic AI Development
Understanding Users
We conduct end-to-end user research to optimize AI interactions, ensuring accessibility, inclusivity, and user-centered design.
UX Research & Consulting
LLM Fine-Tuning
Synthetic Data Creation
LLM Audits & Data QA
Chatbot Creation
Agentic AI Development
Understanding Users
We conduct end-to-end user research to optimize AI interactions, ensuring accessibility, inclusivity, and user-centered design.
Our Process
End-to-End Workflow
Step 1
Discovery & Strategy
We begin with a strategy session to understand your goals, users, and data environment.
Analyzing current workflow..
System check
Process check
Speed check
Manual work
Repetative task
Analyzing current workflow..
System check
Process check
Speed check
Manual work
Repetative task
Step 2
Research & Audit
We analyze your current systems, datasets, or user journeys to surface opportunities.
- class AutomationTrigger:def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"
def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."def get_status(self):
return f"Status: {self.status}" - class AutomationTrigger:def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"
def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."def get_status(self):
return f"Status: {self.status}"
- class AutomationTrigger:def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"
def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."def get_status(self):
return f"Status: {self.status}" - class AutomationTrigger:def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"
def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."def get_status(self):
return f"Status: {self.status}"
Step 3
Design & Plan
We map out the right approach — whether that’s fine-tuning a model, generating synthetic data, or designing a chatbot.
Our solution
Your stack
Our solution
Your stack
Step 4
Build & Optimize
We create or adapt your AI solution — from prompt engineering to model training — and optimize based on test results.
Chatbot system
Efficiency will increase by 20%
Workflow system
Update available..
Sales system
Up to date
Chatbot system
Efficiency will increase by 20%
Workflow system
Update available..
Sales system
Up to date
FAQs
We’ve Got the Answers You’re Looking For
How does LingIQ reduce bias in large language models (LLMs)?
Is AI automation difficult to integrate?
What types of organizations do you work with?
How is your synthetic data different from off-the-shelf datasets?
Why does representation in training data matter?
How much do your services cost?
How long does a typical project take?
Can you handle domain-specific or low-resource languages?
What if I don’t have labeled data yet?
How does LingIQ reduce bias in large language models (LLMs)?
Is AI automation difficult to integrate?
What types of organizations do you work with?
How is your synthetic data different from off-the-shelf datasets?
Why does representation in training data matter?
How much do your services cost?
How long does a typical project take?
Can you handle domain-specific or low-resource languages?
What if I don’t have labeled data yet?
Let's Build Your AI Solution Together
Tell us a bit about your goals and we'll take it from there.