How AI and Machine Learning Are Being Used by Nepali Businesses Today

AI gets discussed mostly in hype terms — chatbots and generative tools — but the more durable value for Nepali businesses today is in narrower, applied machine learning.

Fraud and phishing detection

Our own phishing detection system is a good example: a focused classifier trained on real signals (sender patterns, link structure, content features) does one job well, rather than trying to be a general-purpose AI product.

Demand forecasting for retail and inventory

Businesses running a POS system with enough sales history can use simple forecasting models to predict which items are likely to run out, reducing both stockouts and overstock — a direct, measurable win that doesn't need a data science team to maintain.

Document and data extraction

Extracting structured data from scanned forms or invoices using OCR plus a classification layer can meaningfully speed up data processing work that used to be fully manual.

Customer support triage

A basic classifier that routes incoming inquiries by topic or urgency — before a human ever reads them — is a modest, achievable use of ML that saves real staff time without needing a full chatbot deployment.

Where we're cautious about AI

We're deliberately skeptical of bolting a generic AI feature onto a product just because it's trendy. The projects that actually deliver value solve one narrow, well-defined problem with a model trained on your actual data — not a general-purpose model wrapped around your business hoping it fits.

Curious whether a specific workflow in your business is a good candidate for ML? Tell us about it and we'll give you a realistic assessment — including if the answer is "not yet."