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Cloudus Infotech
AI · Machine Learning Development

Classical models where they beat an LLM on cost and accuracy

Forecasting, classification and scoring built on your own data, deployed with retraining and monitoring in place.

Scope a two-week POCAll AI capabilities

  • 2 wks

    To a working POC

  • 15

    AI capabilities we build

  • 9

    Years in business

Analytics dashboard with charts on a laptop screen
OverviewProcessStackMore AIFAQ

What you get

What every machine-learning project includes

A model is only useful if it beats what you do today. We set that baseline first.

Talk through the use case

Honest comparison against an LLM baseline

Retraining pipeline defined at build time

Drift monitoring in production

Our process

Four steps from data to deployed model

Each step is measured against a baseline.

  1. 01

    Frame the problem & baseline

    The prediction you need, and how well it is done today.

  2. 02

    Prepare the data

    Cleaning, labelling and splitting so the results can be trusted.

  3. 03

    Train & evaluate

    Models compared on held-out data, with the errors that matter reviewed.

  4. 04

    Deploy & monitor

    Served behind an API, with drift and accuracy tracked over time.

Tech stack

What we build it with

Four layers, each chosen per project. Open one to see what sits in it and why.

Models & frameworks

The layer that generates. Chosen per task against quality, latency and cost.

scikit-learnPyTorchTensorFlowXGBoost

More AI capabilities

Fourteen more ways we apply AI

AI Software Development

End-to-end AI product builds.

AI Chatbot Development

Smart, conversational AI for business.

AI Agents Development

Linked agents for smarter tasks.

AI Voice Agents

Voice intake and call handling.

Generative AI Solutions

AI that creates and innovates.

LLM Chatbot Development

Grounded in your own content.

LLMs for SMEs & Enterprises

AI built to scale with you.

RAG Model Development

Smarter AI with search + generation.

GPT Integration

AI seamlessly in your workflow.

Prompt Engineering

Better prompts, better results.

NLP Development

Classification, extraction, sentiment.

AI Workflow Automation

Process automation that holds up.

AI MVPs & POCs

Proof before you commit to a build.

AI Consulting

Where AI pays off, and where it doesn't.

Frequently asked questions

The questions we get asked about AI

Data handling, output quality, cost and ownership.

It depends on the problem. We check what you have in the first step and say plainly if it is not enough yet.

We track accuracy and input drift over time and set alerts, so you hear about a drop before your users do.

A POC on one workflow runs two weeks. A production build on top of it is typically 6–12 weeks, delivered in increments you can use.

Fixed price against a written scope for the POC, then fixed scope or a monthly dedicated-team rate for the build. Cost per request is established before we scale.

You do, from day one. Repositories, prompt history, evaluation sets and infrastructure accounts are all in your name.

Send us the workflow you want to automate. We come back within one business day with questions, then a written scope and a cost per request.

Start with one prediction

Two weeks, your data, a baseline and a first model to compare against it.

Start the POC