AI That Understands How Your Customers Actually Write
"Bhai delivery kab tak aayegi?" is not English, and it is not Hindi. It is how millions of customers across India, Pakistan and the Gulf write every day — and it is what most AI systems quietly fail on, scoring it as neutral or misreading it entirely. We build AI that reads it correctly.
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Our Approach to Multilingual & Code-Mixed AI
Code-mixing — switching between languages inside a single sentence, often written in Roman script — is the default register for hundreds of millions of people, yet almost every commercial AI tool is benchmarked on clean monolingual English. This is an active research area for us, not a feature we bolted on: our team includes doctoral research in code-mixed language identification and sentiment analysis for Roman Urdu–English text. In practice this means we do not simply route your data to a general-purpose model and hope — we evaluate performance on text that looks like your customers' text, fine-tune where accuracy demands it, and show you the measured difference between the generic approach and ours on your own data.
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What We Deliver
Comprehensive multilingual & code-mixed ai solutions designed for your requirements
Code-Mixed Sentiment Analysis
Customer reviews, support tickets and social comments scored accurately whether written in English, Hindi, Urdu, Arabic or any mixture of them in Roman script.
Multilingual Chatbots
Assistants that respond in the language and register the customer used — including mixed-script messages that break most off-the-shelf bots.
Language Identification
Word-level detection of which language each token belongs to. The foundation layer that everything downstream depends on, and the part most pipelines skip.
Text Normalisation
Handling the spelling variation that defines Roman-script writing — "kya", "kiya", "kia" — so search, matching and analytics stop fragmenting across variants.
Voice-of-Customer Analytics
Themes, complaints and product signals extracted across an entire multilingual feedback corpus, with volume and sentiment trends over time.
Regional Content Generation
Marketing copy, product descriptions and campaign assets generated in regional languages that read naturally rather than translated.
Languages & Scripts
The languages and scripts our models are evaluated on
Our Process
A structured approach to delivering excellence
Step 1
Language Profiling
We sample your real customer text to establish which languages, scripts and mixing patterns actually appear — the answer is rarely what clients expect.
Step 2
Baseline Evaluation
We measure how a standard off-the-shelf model performs on your data. This number is what everything afterwards is judged against.
Step 3
Annotation & Dataset
We build a labelled evaluation set from your own text, because published benchmarks rarely reflect a specific customer base.
Step 4
Model Selection & Tuning
We select and, where accuracy requires it, fine-tune models for your language mix rather than defaulting to the largest available.
Step 5
Measured Comparison
Baseline versus tuned, reported honestly. If the generic model is good enough for your use case, we will tell you and save you the spend.
Step 6
Deploy & Monitor
Production deployment with ongoing accuracy monitoring, because language use drifts and models need revisiting.
Where This Matters Most
Consumer brands with regional reach
If your reviews and social comments arrive in mixed script, your current sentiment dashboard is probably understating both your problems and your advocates.
Support teams at scale
Routing and prioritisation degrade badly on code-mixed text. Angry messages get classified as neutral and sit in the queue.
Employers with distributed workforces
Engagement and exit survey free-text is where the real signal lives — and where non-English responses are most often quietly dropped.
Gulf and South Asian market entry
Customer-facing AI that handles only formal English will feel foreign to the customers you are trying to win.
Frequently Asked Questions
Don't large language models already handle Hinglish?
General-purpose models are benchmarked on clean monolingual English, so code-mixed text is where they quietly fail — the system still returns an answer, it's just often wrong, and nobody notices because there's no error message.
How much of our data do you need?
Enough real customer text to build a representative evaluation set — typically a sample of recent support tickets, reviews, or survey responses, sized during the language profiling step.
Is our data used to train models you sell to others?
No. Data and fine-tuned models built for your evaluation and deployment are yours, not folded into a shared product.
What accuracy can we expect?
We report the measured difference between a standard off-the-shelf model and our tuned approach on your own data, rather than quoting a generic number — the baseline evaluation step exists specifically to give you that comparison.
Can this work with our existing chatbot or analytics tool?
In most cases yes, since language identification and sentiment scoring can sit in front of or alongside your existing tool rather than replacing it — we confirm this during scoping.
Ready to Get Started with Multilingual & Code-Mixed AI?
Let's discuss how we can help you achieve your goals.
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