Kathmandu, Nepal · Est. 2022
AntByte Labs is an AI development company in Kathmandu, Nepal that builds production AI — demand forecasting, document intelligence, computer vision, and LLM integrations. We work with businesses across Nepal — AI projects start from $5,000, with full ownership of code and data. We build against the Bikram Sambat calendar, so Dashain and Tihar are modelled as the demand events they are rather than smoothed away as noise, which is where tools trained on Gregorian seasonality quietly fail in Nepal.
Reviewed by the AntByte Labs engineering team · Last updated June 2026
Production AI that pays for itself — built, deployed, monitored, and owned by you.
Festival- and season-aware forecasting. Dashain and Tihar move Nepali demand in ways a generic model does not see, so the model is fitted to the local calendar and backtested against your own sales history before it goes anywhere near a purchase order.
OCR + NLP to extract, classify, and route documents — turn paperwork into structured data.
Detection, recognition, and surveillance analytics for retail, security, and operations.
RAG chatbots and document Q&A built on LLMs, with guardrails and cost control.
Most AI projects die at the demo. We ship the boring-but-critical parts — data pipelines, evaluation, monitoring, and cost control — so your model runs reliably in production and keeps earning its keep. And we understand the Nepal context: BS-calendar seasonality, local languages, and the network realities your users actually face.
Explore our web development and mobile app development in Nepal, or the full IT company overview.
This is the conversation most AI projects should start with and usually do not. A forecasting model learns from history, so the question is not whether the technique works, it is whether you have enough past to learn from. For anything seasonal, and in Nepal almost everything retail is seasonal, you want at least two full years. One year cannot separate a festival from a trend, because the model has seen each Dashain exactly once.
It also has to be data a machine can read. Sales history living in a stack of Excel files with merged cells, a column that changed meaning in the middle of last year, and item names typed slightly differently by four people is normal, and it is genuinely fixable, but cleaning it is a real part of the project rather than a rounding error. We would rather scope that honestly at the start than discover it in week three.
Plenty of problems that arrive described as AI are better solved without it, and a model makes them worse rather than better. If the logic can be written as rules a person can read, write the rules: they are cheaper, they explain themselves when a customer complains, and they do not drift. If the decision has legal or financial consequences and someone must be able to say exactly why it went that way, a model that cannot explain itself is a liability. And if you have a few hundred records, you have a spreadsheet question rather than a machine learning one.
The useful version of this work is narrow and measurable: forecast this product group, extract these fields from this document type, flag these transactions for a human to review. Ask anyone quoting you what the model will be measured against and what happens when it is wrong. If there is no answer to the second question, the project is not ready.
For seasonal forecasting, at least two full years of history. With one year a model has seen each Dashain and Tihar exactly once, so it cannot tell a festival apart from a trend. It also has to be machine-readable: sales history spread across Excel files with merged cells, a column that changed meaning mid-year, and item names typed four different ways is normal and fixable, but cleaning it is a real line in the project rather than a rounding error. Document extraction is different and can work from a few hundred examples if they are consistent.
More often than the marketing suggests. If the logic can be written as rules a person can read, write the rules, because they are cheaper, they explain themselves when a customer complains, and they do not drift. If a decision has legal or financial consequences and somebody has to justify it afterwards, a model that cannot explain itself is a liability rather than an asset. And with a few hundred records you have a spreadsheet problem, not a machine learning one. We would rather say this at the scoping call than bill you to find out.
There is no single best, and any company claiming the title is selling. Judge on four things instead: whether they will show you a model running on your own data before you commit, who owns the trained model and the training data afterwards, what monitoring ships with it, and what happens when accuracy drifts six months in. AntByte Labs builds AI in Kathmandu with full ownership of code and data handed over, and we will tell you when a problem does not need machine learning at all.
AI projects in Nepal typically start from NPR 200,000 for a focused model or automation, with larger ML platforms ranging higher based on data, integrations, and MLOps needs. International clients are quoted in USD (from roughly $5,000). We scope and quote after a free discovery call.
Demand forecasting, document intelligence (OCR + NLP), computer vision, recommendation engines, and LLM/ChatGPT integrations for your product. We focus on production systems that save real hours — not proofs of concept that never ship.
Yes, and it is the part off-the-shelf tools get wrong here. A model trained on Gregorian seasonality cannot see Dashain or Tihar, which move against the Gregorian calendar every year and dominate retail demand in Nepal. We build forecasting against the Bikram Sambat calendar so festival weeks are a feature rather than an anomaly the model smooths away. Accuracy depends entirely on the history you can supply, so we scope that before quoting.
Yes. We build retrieval-augmented (RAG) assistants, document Q&A, and workflow automation on top of LLMs, with guardrails, evaluation, and cost controls so the feature is reliable in production — not just a demo.
You do. All code, trained models, and documentation transfer to you on final payment, and your data stays yours — we follow strict data-handling practices. No vendor lock-in.
Book a free 30-minute call. We'll assess feasibility, the data you need, and the ROI, then send a clear plan within three business days.
