Innotone - Democratizing Intelligence

Train & Deploy
Your Own Thinking LLMs
Weeks

Model's thinking completely under your control

Data

Grounded in your data and logic

By your context

Thinking

Thinking follows your view point

Of your thinking

LLM

Acts to fulfill your expectations precisely

For your outcomes

What You Can Build ? | Ability

How It Works? | Process

Prepare data & upload

Pick Model & Train

View Training Results

Try Model

Deploy Model & Get API

Create high-quality training datasets with your thinking/logic

Image

Thinking

Label

Cricket Field

Gucci

Drop your dataset here

Data uploaded successfully

Choose a LLM, GPU config and # epochs to train the model

Model

GPUs 4 GPUs

Epochs 6

Estimate

Cost —

Duration (est.) —

✅ Training started on — with — GPU(s) × — epochs.

Check out the training run metrics

Model: — GPU(s): — Epochs: —

Train Accuracy

0%

Eval Accuracy

0%

Loss

0.00

Loss by epoch

Train

Val

Play with your trained model to get a feel of its performance

Type a message…

Deploy on your favourite cloud in one click and get production ready API endpoints

Model

Provider

Requirements

Latency 100 ms

Throughput 1000 req/min

Provisioning GPU Cluster...

API Endpoint


                                        curl -X POST https://api.innoland.ai/v1/completions \

                                          -H "Authorization: Bearer sk-..." \

                                          -d '{ "model": "Satellite_Qwen_V1", "prompt": "..." }'

Why It Matters? | Benefits

Build powerful applications without writing a single line of code. Intuitive visual tools do the heavy lifting.

Why It Works? | Under the Hood

Adaptive Thinking

We fine-tune the thinking process itself, not just the outputs. By adapting how models think about your domain through reinforcement learning with reward shaping, we create AI that naturally understands and thinks according to your specific context and rules.

Small Models, Big Performance

Structured thinking dramatically improves smaller model capabilities. You get high accuracy at a fraction of the computational cost and latency of general-purpose LLMs.

Mathematically Enforced Determinism

We don't rely on temperature settings alone. Through fundamental changes to the model architecture and the training algorithm, we ensure our models are deterministic.