When generic models plateau on your problem, custom modeling breaks through. Our AI model development services fine-tune, train, and deploy models specialized to your domain, data, and constraints.
Fine-tuned LLMs that master your terminology, classical models trained on your data, and self-hosted deployments where privacy or economics demand - with honest advice on when off-the-shelf is genuinely enough.
Open-source models specialized to your domain language and tasks.
Classification, prediction, and extraction models trained on your data.
Models running on your infrastructure - private, per-token-free at scale.
Quantized, distilled models fitting real hardware and latency budgets.
Quality measured systematically - before, during, and after training.
Versioning, monitoring, and retraining as data and needs evolve.
We identify where AI genuinely saves money or creates revenue in your business - and where it does not.
Your data, systems, and constraints are assessed to pick the right approach and models.
A working proof-of-concept measured against real business metrics before full investment.
Production-grade AI integrated into your existing tools, workflows, and applications.
Models and automations are monitored, retrained, and refined as your data evolves.
We start from ROI, not hype. If a simple script beats a model, we will tell you.
GPT-class LLMs, open-source models, and classic ML - chosen per problem, not per fashion.
AI that plugs into the tools you already use - websites, CRMs, ERPs, and messaging apps.
Architectures that respect your data ownership, compliance needs, and customer privacy.
Start with a focused pilot from Kathmandu at a fraction of Western agency rates.
AI is not fire-and-forget. We monitor, retrain, and tune so quality never silently degrades.
AI Model Development services are available across Nepal - with on-the-ground support in these cities - and remotely for clients worldwide. Explore more AI Services services or talk to our team about your project.
Later than most assume: strong prompting plus retrieval (RAG) solves the majority of cases cheaper and faster. Fine-tuning earns its cost when you need consistent specialized behavior at volume, domain language mastery, smaller/cheaper models for scale, or offline deployment. We prototype the cheap path first - spending your training budget only when evaluation proves the need.
Less than the old days: meaningful LLM fine-tuning (LoRA-style) starts around hundreds to a few thousand quality examples; classical model training varies by task. Data quality dominates quantity - our data preparation phase typically matters more than the training itself.
Three reasons that compound: data that cannot leave your infrastructure (privacy/compliance), per-token costs that break unit economics at scale, and latency/offline requirements. Modern open-source models are genuinely capable - self-hosting is a real strategy now, and we run these deployments in production ourselves.
Less than feared: fine-tuning mid-size models runs on rented cloud GPUs for project-budget costs (no hardware purchase), and quantized inference serves well on modest GPU servers we can supply and manage - or your cloud. We size against measured load, not vendor fantasy.
Evaluation-first development: before training, we build a test set from your real cases and measure the baseline (API model, current process). The custom model must beat it on those numbers to ship - spend justified by measurement, not enthusiasm.
Get a free consultation and a no-obligation quote from our team in Kathmandu.
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