When general AI isn't good enough, we train one that is.
General-purpose models are a great starting point, until you need consistent output, domain-specific accuracy, or lower costs at scale. We help you determine when a custom or fine-tuned model makes sense, then train and validate one built around your data, domain, and budget.
Signs an off-the-shelf model isn't enough.
Inconsistent output
You need the same structured result every time.
Domain language
The model doesn't understand your industry's terms.
Accuracy ceiling
Prompting only gets you so far on your specific task.
Cost at scale
Per-call costs add up as volume grows.
Privacy needs
You want a model you can run in your own environment.
We recommend the right approach — not the most expensive one.
Prompting
Best when the task is simple, stakes are low, and you need to move fast. No training data required.
RAG (retrieval)
Best when answers depend on changing knowledge and you need citations from your own sources.
See AI Agents & Copilots →Fine-tuning / custom models
Best when you need consistent output format, domain-specific accuracy, high precision, or lower cost at scale.
What we deliver.
A clear build-vs-buy recommendation: whether you even need a custom model
Benchmarks against accuracy, cost, and speed, with honest before/after numbers
Model training on your domain and data (fine-tuning or custom, including smaller/self-hosted models)
A deployment-ready package: the model, wrapped and ready to serve
Evaluation and documentation so you can trust and maintain it
How we build a custom model.
Assess
Define the task, metrics, and whether it's justified.
Prepare data
Gather, clean, and structure training and eval data.
Train
Fine-tune or train, iterating against benchmarks.
Evaluate
Measure accuracy, cost, and speed on a held-out set.
Package & deploy
Deliver a deployment-ready model with docs.
Not everything needs a giant model
Often the best answer isn't the biggest model — it's a smaller one, trained well, running cheaply and privately. Where it fits, we use compact and open models that can run in your own environment at a fraction of the cost, with your data staying in your control. We measure the trade-offs — quality, speed, and cost per request — so the choice is based on numbers, not hype.
Why trust us with your model.
Benchmark-driven
We prove the model works before you rely on it.
Senior-led
Experienced ML engineers on the work.
Cost-honest
We recommend the cheapest approach that meets the bar, including "you don't need a custom model."
Secure by default
Handled under ISO 27001 practices, never used to train third-party models without consent.
From discovery to production.
Discovery & Assessment
Task, data readiness, success metrics.
Build & Validate
Prepare data, train, and benchmark.
Deploy & Harden
Package, deploy, document.
Operate & Optimise
Retraining and improvement as data grows.
Custom Models & Fine-tuning
FAQ's.
Anything else? Write to us directly.
Often not — prompting or retrieval (RAG) solves many problems more cheaply. We assess your task first and only recommend a custom or fine-tuned model when it clearly earns its place on accuracy, consistency, or cost.
Consistent output format, domain-specific accuracy, precision on your specific task, and cost efficiency at scale.
We agree metrics upfront — accuracy, latency, and cost — and benchmark the model against a held-out test set with honest before/after numbers.
Yes, where they fit. Smaller and open models can run privately in your environment at much lower cost — we measure quality, speed, and cost to pick the right one.
Yes. Your data is handled under ISO 27001 practices and is never used to train third-party models without your explicit consent.
Yes. We deliver a deployment-ready package and can deploy to your cloud or on-premise setup.
Not getting the accuracy you need?
Tell us the task. We'll tell you honestly whether a custom model is worth it — and build it if it is.
support@zyqo.ai · Bengaluru, India