ForthClover — Boutique AI Engineering on AWS, Azure & GCP

Build with AI That Understands Your Business

Are your AI models meeting your requirements?

For specialized applications, generic AI models may not cut it. In these cases, specialized AI applications may benefit from fine-tuning. You might need fine-tuning if you're experiencing:

Poor accuracy with domain-specific terminology (e.g. medical, legal, technical, or proprietary language that generic models don't understand)
Inconsistent or unreliable outputs
High third-party API costs that don't scale
Latency issues with third-party models
Data privacy concerns

When your business depends on speed, accuracy, and unique context, the right model with the proper tuning makes all the difference. We begin our process with an assessment to make sure that fine-tuning is the right fit and determine if our Risk-Free Path applies to your engagement. When fine-tuning is needed, we are typically able to deliver higher accuracy output with ongoing cost savings.

Book your discovery call

Limited availability

Model Tuning Package

LLM Fine-tuning Proof-of-Concept

Included

Custom training dataset, tailored to your specific use case
Fine-tuned model, deployed in your cloud account
Complete codebase (full fine-tuning process you can replicate)
Performance benchmarks and cost analysis
Validation testing to confirm outputs meet your requirements

Up to

$50,000 value

See if you qualify today

Available to qualified ForthClover engagements

Backed by our Risk-Free Path

Risk-free strategy session
100% satisfaction guarantee
No production commitment

Your Guide to Model Performance

Proven process for fine-tuning success

We start by determining if fine-tuning is the right solution. If it is, we'll deliver a working proof-of-concept you can test with real data and then deploy for enterprise-scale use.

Phase 01

Discovery

Your requirements drive everything we do. We'll confirm:

Performance goals and cost constraints
Base LLMs in use or being considered
Whether fine-tuning, RAG, or another approach is optimal
Expected ROI and cost savings
Risk-Free Path eligibility

Deliverable: Fine-tuning assessment with go/no-go recommendation and cloud cost projections

Phase 02

Proof-of-Concept

Model fine-tuning can deliver exceptional results for the right applications. In this phase we'll deliver:

Fine-tuned model in your cloud account
Custom training dataset based on synthetic or real data tailored to your use case
Complete codebase for the fine-tuning process
Performance benchmarks with comparison to base models
Validation testing and cost projections
Risk-Free Path eligibility for production phases

Deliverable: Working fine-tuned model deployed in your cloud account

Phase 03

Deployment

Our engineers deploy fine-tuned models for enterprise-level use in your cloud environment. We'll provide:

Total control over costs and latency in model performance
Full Infrastructure as Code (IaC) implementation that auto-scales to meet usage demand
Advanced performance and cost monitoring
Robust API endpoints with built-in auto-scaling for production application connections

Deliverable: Production-ready system with full documentation

Let's deploy LLMs that work for you

Here's the truth: fine-tuning isn't always the answer. We've recommended RAG, document processing, and other solutions when they were better fits for our clients' needs. Our goal is to solve your problem, not sell you a specific solution.

Book your discovery call

Cut AI costs while
boosting accuracy