Artificial Intelligence Services

AI Model Development

Custom AI models tailored to your proprietary data and specialized use cases.

Start a project Book a call

Delivery focus

Built to move from idea to production.

Custom model training, fine-tuning, and evaluation for teams that need AI tailored to proprietary data and specialized use cases. The work sits inside Artificial Intelligence Services and is scoped, built, reviewed, and handed over as one engagement.

Custom model training

Fine-tuning

Model evaluation

Specialized use cases

Scope

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.

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 this engagement delivers

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.

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.

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.

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.

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.

Process

  1. Phase 1 · Week 1-2

    AI Model Scoping

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

  2. Phase 2 · Week 2-6

    Dataset Preparation

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

  3. 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.

  4. 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.

  5. Phase 5 · Week 7-10

    Self-Hosted Deployment

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

  6. 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.

  7. Phase 7 · Ongoing

    Ongoing Model Retainer

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

Tech stack

Fine-Tuning Frameworks
LoRA/QLoRA (75-90% VRAM reduction), HuggingFace transformers, PEFT library, Axolotl, OpenAI fine-tuning API
Base Models
Llama 3.1 8B/70B, Mistral 7B/Mixtral 8x7B, GPT-4o, BERT/RoBERTa/DistilBERT, DeBERTa
Inference Optimisation
vLLM (PagedAttention 20-40x throughput), GGUF quantisation (CPU inference), TensorRT-LLM, Triton Inference Server
MLOps & Experiment Tracking
Weights & Biases, MLflow, DVC, SageMaker Model Registry, Label Studio
RLHF & Alignment
DPO (Direct Preference Optimisation), TRL, Reward model training, Human preference data collection
Deployment Infrastructure
AWS SageMaker, EC2 G-instances (G4dn, G5, P4d), ECS Fargate, Lambda, CloudWatch

How we work

Fine-Tuning vs RAG Clarity

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

Parameter Efficiency

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

Inference Optimisation

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

MLOps Rigor

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

Dataset Quality Guidance

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

Where this is used

Domain-Specific LLM Fine-Tuning

Teach Llama or GPT-4o your industry's terminology, writing style, and reasoning patterns. 500-2,000 examples for domain adaptation.

Custom Classification & NER

BERT/RoBERTa fine-tuned for intent classification, sentiment, document type classification, or custom entity extraction. 100-1000x cheaper than LLM API calls.

Self-Hosted Private Models

Healthcare, legal, and financial services with PHI/PII data that cannot leave your environment. Llama/Mistral deployed on your infrastructure.

Continuous Training MLOps

Teams that will retrain models regularly on new data: automated pipelines, versioning, CI/CD, drift detection, blue/green deployment.

Pricing guidance

These ranges are planning anchors. The quote names scope, timeline, and what is excluded. Talk through a quote.

AI Model Scoping

$4,000 - $8,000

1 - 2 weeks

Use case analysis, fine-tuning vs RAG decision, model selection, dataset requirements

Dataset Preparation

$5,000 - $20,000

2 - 4 weeks

Data curation, labelling, formatting, quality review, train/eval split

LLM Fine-Tuning (GPT-4o)

$8,000 - $25,000

2 - 4 weeks

OpenAI fine-tuning API, eval framework, endpoint deployment

LLM Fine-Tuning (Open-Source)

$15,000 - $45,000

4 - 8 weeks

Llama/Mistral, LoRA/QLoRA, self-hosted deployment, inference optimisation

Custom Classification Model

$10,000 - $30,000

3 - 6 weeks

BERT fine-tune, labelled dataset, eval metrics, production deployment

Self-Hosted Model Deployment

$12,000 - $35,000

3 - 6 weeks

vLLM, OpenAI-compatible API, GPU infrastructure, monitoring

MLOps Pipeline

$15,000 - $50,000

4 - 8 weeks

DVC, W&B, model registry, CI/CD, drift detection, model monitoring

AI Model Retainer

$4,000 - $10,000/mo

Ongoing

Retraining on new data, eval monitoring, model iteration, drift response

FAQs

When should I fine-tune an LLM vs. use RAG?

Fine-tuning and RAG solve different problems and are often used together. Use RAG when: the model needs access to specific, current, or proprietary facts, the information changes frequently, or you need source citations. Use fine-tuning when: the model needs to behave differently adopt specific response format, use domain vocabulary consistently, follow particular reasoning patterns, or match brand tone in ways prompt engineering cannot reliably achieve. Fine-tuning teaches style and behaviour; RAG provides knowledge. Most organisations asking for fine-tuning actually need RAG ClickMasters identifies the correct solution before any training begins.

What is LoRA and why is it used for LLM fine-tuning?

LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that trains small adapter matrices alongside frozen base model weights. A 7B parameter Llama model has 7 billion weights requiring massive GPU memory. LoRA trains adapters representing 0.1-1% of original parameters reducing VRAM requirements by 75-90% and training cost proportionally. QLoRA combines 4-bit quantisation with LoRA enabling 70B model fine-tuning on a single consumer GPU that would otherwise require 8 high-end server GPUs. ClickMasters uses LoRA/QLoRA for open-source model fine-tuning as default.

How much training data do I need for fine-tuning?

For format and style fine-tuning: 50-200 high-quality examples. For domain adaptation: 500-2,000 examples covering production input range. For complex reasoning tasks: 1,000-5,000+ examples. Quality matters far more than quantity 100 carefully curated examples consistently outperform 10,000 examples with label noise or format inconsistency. ClickMasters includes dataset quality review as part of every fine-tuning engagement.

What is MLOps and when do I need it?

MLOps is the engineering discipline of deploying, monitoring, and maintaining ML models in production. You need MLOps when: training and retraining models regularly on new data, need to track experiment results (Weights & Biases/MLflow), need model versioning traceable to training data, need to detect model degradation (data drift), or are serving multiple model versions simultaneously (A/B testing). For teams training models once and infrequently, a full MLOps pipeline is unnecessary overhead.

Related reading

AI Model Development is one line inside Artificial Intelligence Services. Teams with an adjacent problem often compare it with Generative AI Solutions and AI Consulting Services. For a written delivery narrative, read the SaaS booking platform QA case study and the rest of the case studies.

Back to Artificial Intelligence Services