Background

AI Model Development

ClickMasters builds and fine-tunes AI models for B2B companies across the USA, Europe, Canada, and Australia. LLM fine-tuning (GPT-4o, Llama 3, Mistral) for domain-specific accuracy on your proprietary terminology and output formats. Custom classification and extraction models (BERT, RoBERTa, DistilBERT) for production efficiency. MLOps pipelines for training, evaluation, versioning, and deployment. Self-hosted models on your infrastructure when data cannot leave your environment.

LLM Fine-Tuning (GPT-4o / Llama 3)
Custom Classification Models
RLHF & Alignment
Self-Hosted Deployment
MLOps Pipelines
Model Evaluation Frameworks
0+

Years Experience

0+

Projects Delivered

0%

Client Satisfaction

0/7

Support Available

Business client portrait
Business client portrait
Business client portrait
Business client portrait
150+ clients worldwide
4.9/5 rating
AI Model Development

Custom AI Models When Off-the-Shelf APIs Are Not Accurate, Private, or Fast Enough

Fine-Tuning vs RAG Choose Correctly Before Investing

Fine-tuning is NOT the solution for giving an LLM access to your data. That is RAG. Fine-tuning is the solution for changing how a model behaves its writing style, response format, domain-specific vocabulary, or reasoning patterns by training on examples of the behaviour you want.

  • Fine-tune when: you need the model to respond in a specific format that prompt engineering cannot reliably produce, you need domain-specific vocabulary and reasoning not in the base model's training, or you need to reduce tokens per response
  • Use RAG when: you need the model to know current or proprietary facts, you need source attribution, or the information changes frequently
  • Most organisations that ask for fine-tuning actually need RAG ClickMasters will identify the correct solution in the scoping engagement

What we deliver

AI Model Development Services We Deliver

05 capabilities

ClickMasters operates as a full-stack ai model development partner — product strategy, UI/UX, engineering, cloud infrastructure, QA, and ongoing support in one delivery model.

01

LLM Fine-Tuning

Custom LLM fine-tuning on proprietary datasets: dataset preparation (prompt-completion pairs or chat format), base model selection (GPT-4o via OpenAI API; Llama 3.1/Mistral via HuggingFace), LoRA/QLoRA training, evaluation (ROUGE, F1, human evaluation), and deployment.

02

Custom Classification & Extraction Models

Lightweight models for specific tasks: text classification (BERT/RoBERTa fine-tuned), named entity recognition (NER for custom domain entities), binary/multi-class classification. 100-1000x cheaper than LLMs, runs on CPU.

03

RLHF & Preference Alignment

Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimisation (DPO): preference data collection, reward model training, PPO or DPO fine-tuning, and alignment evaluation.

04

Self-Hosted Model Deployment

Open-source model deployment (Llama 3 70B, Mistral 7B) on your infrastructure. vLLM for 20-40x throughput improvement, GGUF quantisation for CPU inference, OpenAI-compatible REST API.

05

MLOps Pipeline Development

End-to-end MLOps infrastructure: data pipeline (DVC, Label Studio), training pipeline (SageMaker, W&B), model registry (MLflow), CI/CD for models, blue/green deployment, drift detection.

Why choose us

Why Companies Choose ClickMasters

05 advantages

We combine architecture discipline, transparent delivery, and long-term partnership — so your investment translates into measurable business results, not just shipped code.

01

Fine-Tuning vs RAG Clarity

Amber "choose correctly" callout | Basic: Misleading "fine-tuning for knowledge" advice

02

Parameter Efficiency

LoRA/QLoRA with VRAM reduction specifics (75-90%) | Basic: Full fine-tuning (unnecessarily expensive)

03

Inference Optimisation

vLLM, GGUF quantisation, OpenAI-compatible API | Basic: Naive HuggingFace inference (slow, expensive)

04

MLOps Rigor

DVC, W&B, MLflow, CI/CD with regression blocking, drift detection | Basic: Manual training + deployment

05

Dataset Quality Guidance

100 excellent examples > 10,000 mediocre ones | Basic: "More data is better" (often wrong)

500+

Companies served

4.9/5

Client rating

15+

Years in delivery

Our Process

Our AI Model Development Process

Scroll to walk through each phase — lines connect as you move down.

Phase 1
Week 1-2

AI Model Scoping

Use case analysis, fine-tuning vs RAG decision, model selection, dataset requirements definition, success metrics.

Phase 2
Week 2-6

Dataset Preparation

Data curation, labelling workflow (Label Studio), dataset formatting, quality review, train/eval/test split. Quality > quantity.

Phase 3
Week 3-8

Model Training

LoRA/QLoRA fine-tuning (Llama/Mistral) or OpenAI fine-tuning API. Hyperparameter tuning, evaluation against hold-out test set.

Phase 4
Week 6-9

Model Evaluation & Alignment

Hold-out test set evaluation, human evaluation for generation quality, RLHF/DPO alignment if required, regression testing.

Phase 5
Week 7-10

Self-Hosted Deployment

vLLM inference optimisation (20-40x throughput), OpenAI-compatible REST API, GPU infrastructure, monitoring dashboards.

Phase 6
Week 8-12

MLOps Pipeline (Optional)

DVC for dataset versioning, Weights & Biases experiment tracking, MLflow model registry, CI/CD for models, blue/green deployment, drift detection.

Phase 7
Ongoing

Ongoing Model Retainer

Retraining on new data, evaluation monitoring, model iteration, drift response.

Technology Stack

Modern tools we use to build scalable, secure applications.

Languages & Frameworks

Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch
Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch
Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch
Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch
Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch
Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch
Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch
Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch
Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch
Python
Python
Node.js
Node.js
TensorFlow
TensorFlow
PyTorch
PyTorch

Data Processing

NumPy
NumPy
Pandas
Pandas
Jupyter
Jupyter
NumPy
NumPy
Pandas
Pandas
Jupyter
Jupyter
NumPy
NumPy
Pandas
Pandas
Jupyter
Jupyter
NumPy
NumPy
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Pandas
Jupyter
Jupyter
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NumPy
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Pandas
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Jupyter
NumPy
NumPy
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Pandas
Jupyter
Jupyter
NumPy
NumPy
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Pandas
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Jupyter
NumPy
NumPy
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Pandas
Jupyter
Jupyter
NumPy
NumPy
Pandas
Pandas
Jupyter
Jupyter
NumPy
NumPy
Pandas
Pandas
Jupyter
Jupyter
NumPy
NumPy
Pandas
Pandas
Jupyter
Jupyter
NumPy
NumPy
Pandas
Pandas
Jupyter
Jupyter
NumPy
NumPy
Pandas
Pandas
Jupyter
Jupyter
NumPy
NumPy
Pandas
Pandas
Jupyter
Jupyter

Infrastructure

AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes
AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes
AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes
AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes
AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes
AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes
AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes
AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes
AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes
AWS
AWS
Google Cloud
Google Cloud
Docker
Docker
Kubernetes
Kubernetes

Industry-Specific Expertise

Deep expertise across various sectors with tailored solutions

Domain-Specific LLM Fine-Tuning

Custom Classification & NER

Self-Hosted Private Models

Continuous Training MLOps

Pricing

AI Model Development Development Pricing

Transparent pricing tailored to your business needs

AI Model Scoping

Perfect for businesses that need ai model scoping solutions

$4,000 – $8,000

AUD · one-time investment range

Package Includes

  • Timeline: 1 - 2 weeks
  • Best For: Use case analysis, fine-tuning vs RAG decision, model selection, dataset requirements
  • Budget Range: 4,000 - 8,000 AUD
  • Dedicated Project Manager
  • Quality Assurance Testing
  • Documentation & Training

Dataset Preparation

Perfect for businesses that need dataset preparation solutions

$5,000 – $20,000

AUD · one-time investment range

Package Includes

  • Timeline: 2 - 4 weeks
  • Best For: Data curation, labelling, formatting, quality review, train/eval split
  • Budget Range: 5,000 - 20,000 AUD
  • Dedicated Project Manager
  • Quality Assurance Testing
  • Documentation & Training

LLM Fine-Tuning (GPT-4o)

Perfect for businesses that need llm fine-tuning (gpt-4o) solutions

$8,000 – $25,000

AUD · one-time investment range

Package Includes

  • Timeline: 2 - 4 weeks
  • Best For: OpenAI fine-tuning API, eval framework, endpoint deployment
  • Budget Range: 8,000 - 25,000 AUD
  • Dedicated Project Manager
  • Quality Assurance Testing
  • Documentation & Training

LLM Fine-Tuning (Open-Source)

Perfect for businesses that need llm fine-tuning (open-source) solutions

$15,000 – $45,000

AUD · one-time investment range

Package Includes

  • Timeline: 4 - 8 weeks
  • Best For: Llama/Mistral, LoRA/QLoRA, self-hosted deployment, inference optimisation
  • Budget Range: 15,000 - 45,000 AUD
  • Dedicated Project Manager
  • Quality Assurance Testing
  • Documentation & Training

Custom Classification Model

Perfect for businesses that need custom classification model solutions

$10,000 – $30,000

AUD · one-time investment range

Package Includes

  • Timeline: 3 - 6 weeks
  • Best For: BERT fine-tune, labelled dataset, eval metrics, production deployment
  • Budget Range: 10,000 - 30,000 AUD
  • Dedicated Project Manager
  • Quality Assurance Testing
  • Documentation & Training

Self-Hosted Model Deployment

Perfect for businesses that need self-hosted model deployment solutions

$12,000 – $35,000

AUD · one-time investment range

Package Includes

  • Timeline: 3 - 6 weeks
  • Best For: vLLM, OpenAI-compatible API, GPU infrastructure, monitoring
  • Budget Range: 12,000 - 35,000 AUD
  • Dedicated Project Manager
  • Quality Assurance Testing
  • Documentation & Training
Transparent Pricing
No Hidden Costs
Flexible Engagement
30-Day Support

* All prices are estimates and may vary based on requirements.

CEO Vision

To build scalable, intelligent custom software development solutions that empower businesses to grow, automate, and transform in a digital-first world.

CEO Vision
We are not building software. We are architecting the infrastructure of tomorrow—systems that think, adapt, and grow alongside the businesses they power.
AK

Amjad Khan

Chief Executive Officer

12+

Years Exp

300+

Success

98%

Retention

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Common Inquiries

Frequently Asked Questions

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