Innotone

Build AI That
Thinks and Acts Like Your
Business

Train & Deploy Your Custom Thinking LLMs in Hours

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

Teach it how you think

As training progresses, the model aligns to your policy—and behaves more like you, consistently.

Input

Customer message

Order delivered 14 minutes late.
Rider note: "traffic".
Restaurant confirmed delay.
Customer tier: Gold.
Request: refund.

Your logic

Delay Check if delay exceeds policy threshold (general: > 15 minutes)
Validate Validate rider note + restaurant confirmation
Tier Apply customer tier-based threshold (Gold: > 5 minutes)
Decision Refund if corroborated AND threshold is met

Desired thinking

The order was delivered 14 minutes late. The delay is corroborated by the rider note ("traffic") and the restaurant confirming the delay. Apply the tier-based rule: for general customers, refund only if delay is greater than 15 minutes; for Gold tier, refund if there is any delay greater than 5 minutes. Since the customer is Gold and the delay is greater than 5 minutes, approve the refund.

Expected output

Approve refund

Model thinking (trace) Not aligned

Training progress Start of training

StartTrainingEnd

Model thinking

Trace

Output

Decline refund

Why it changed

How we compare

See how Innotone stacks up against traditional approaches

🧱

Traditional ML

✕ High data requirement with large labeled datasets

✕ No explainability, is a black-box

✓ Low latency during inference

✓ Low cost to train and run

✓ No hallucinations, but can be logically wrong

🧠

GPT like LLMs

✓ Low data requirement via zero-shot prompting

⚠ Partial explainability, dependent on prompting

✕ High latency due to large model size

✕ High cost driven by token-based pricing

✕ Significant hallucination risk

✨

✓ Low data requirement (~1k curated samples)

✓ Full explainability with explicit thinking exposed

✓ Low latency using compact 2–8B models

✓ Low cost from small, efficient models

✓ No hallucination due to deterministic reasoning

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

Eval Accuracy

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.