What base models can you fine-tune?

We work with all major foundation models including GPT-4, Claude 3, Llama 3, Mistral, and others. The choice depends on your use case, budget, and deployment requirements.

How much training data do I need?

Minimum 500-1,000 high-quality examples, but more is better. We'll assess your data quality and help you augment or collect additional data if needed.

Where does the trained model live?

You own it completely. We can deploy it to your infrastructure (AWS, GCP, Azure) or help you set up managed hosting. The model and weights are yours.

How long does fine-tuning take?

Typically 6-12 weeks including data preparation, training iterations, evaluation, and deployment. Rush timelines are possible for additional fees.

Can the model be updated later?

Yes! We build versioning and retraining procedures so you can continuously improve the model as you gather more data.

Back to AI Model Training & Fine-Tuning

SingaporeEngineering

AI Model Training & Fine-Tuning in Singapore

Turn base AI models into domain experts that know your business.

Singapore is positioning itself as Southeast Asia's AI hub with strong government support, advanced digital infrastructure, and a highly skilled workforce. Pertama Partners helps Singapore businesses harness AI for measurable growth.

Duration6-12 weeks

Investment$50,000 - $200,000

LocationSingapore

Get Started in Singapore

$4.5 billion AI market by 2030

AI Market Size

25% annual growth in AI adoption

Annual Growth

40% of workforce needs AI upskilling by 2025

Workforce Upskilling Need

LOCAL CONTEXT

AI landscape in Singapore

Singapore leads Southeast Asia in AI readiness, with a well-established Smart Nation initiative and mature digital infrastructure. Government-backed programmes like SkillsFuture and the Enterprise Development Grant make it one of the most accessible markets for AI adoption in the region.

Market Size

$4.5 billion AI market by 2030

AI Maturity

advanced

Key Drivers

Smart Nation initiative
SkillsFuture funding ecosystem
World-class digital infrastructure
Strong regulatory frameworks (IMDA, MAS)

THE CHALLENGE

Sound familiar?

“Generic AI models don't understand our industry terminology or context”

“Off-the-shelf solutions produce inconsistent or inaccurate results”

“We have valuable proprietary data but no way to leverage it in AI systems”

“Our use case requires specialized knowledge that base models don't have”

“We need AI outputs that align with our brand voice and standards”

Our team has trained executives at globally-recognized brands

OUTCOMES

What you'll achieve

Problems you'll solve

Domain-specific model that understands your industry context
Improved accuracy and relevance for your specific use cases
Custom outputs that match your style, tone, and requirements
Proprietary AI advantage built on your unique data
Reduced hallucinations and errors in production systems

Value you'll gain

10-50% improvement in model accuracy for domain-specific tasks
Faster time-to-value with pre-trained models
Competitive advantage through proprietary AI capabilities
Better user experience with contextually relevant outputs
Reduced reliance on expensive API calls through efficient fine-tuned models

OUR PROCESS

How we deliver results

Step 1

Problem & Baseline Definition

We formalise evaluation metrics, assess existing approaches, and establish quantitative baselines that subsequent training must surpass.

Step 2

Data Curation & Augmentation

Training corpora are labelled, cleaned, and enriched using active learning and synthetic generation to maximise model quality.

Step 3

Training & Experimentation

Models are trained on GPU clusters with hyperparameter sweeps, experiment tracking, and distributed parallelism for efficiency.

Step 4

Evaluation & Deployment

Rigorous testing, bias audits, and model cards precede production deployment alongside drift monitoring and a retraining playbook.

IS THIS RIGHT FOR YOU?

Finding the right fit

This is ideal for you if...

You have domain-specific data (>1,000 quality examples)

Generic models consistently underperform for your use case

You need outputs that match specific style or compliance requirements

Your industry has specialized terminology or knowledge

You want a proprietary AI capability as a competitive advantage

Consider another option if...

You're just getting started with AI (try training programs first)

You don't have quality training data available

Generic models already meet your accuracy requirements

Your use case changes frequently (making training inefficient)

Budget constraints prevent investment in custom engineering

See yourself above? Let's talk about AI Model Training & Fine-Tuning in Singapore.

Let's Talk

COMMON QUESTIONS

Frequently asked

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