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Services

Fine-Tuning Services

When prompting isn't enough.

Track record

Creators of RustyRAG

Realtime RAG, built in Rust · Sub-200ms end-to-end

10+ years delivering AI solutionsIgnas Vaitukaitis · Founder & CEO

Read RustyRAG’s source before you sign.

Shipped for

VersarWashington, DC
GynisusNew York
CampusReelNew York
LuniqGermany
HospitalityFlowSingapore

Overview

Your Model, Your Data

General-purpose models are impressive, but they weren't trained on your domain. Fine-tuning closes the gap — teaching models your terminology, your formats, and your edge cases so they perform like specialists, not generalists.

Data Preparation & Curation

We clean, format, and structure your training data into high-quality examples the model can learn from.

Model Selection & Training

We pick the right base model for your task and run fine-tuning with proper hyperparameter optimization.

Evaluation & Benchmarking

Side-by-side comparisons against the base model on your real-world test cases to prove the improvement.

Deployment & Serving

Optimized inference setup — hosted or self-hosted — with latency and cost targets that make sense for production.

Process

How It Works

Fine-tuning is only worth it when the data and the task are right. We make sure both are before you spend a dollar on training.

Feasibility & Data Review

We assess whether fine-tuning is the right approach and audit your data for quality, volume, and coverage gaps.

Training & Evaluation

Iterative training runs with systematic evals. We compare against baseline until the fine-tuned model clearly wins.

Ship & Monitor

Deploy the model with monitoring for drift and degradation. We set up retraining pipelines for when your data evolves.

Updated July 15, 2026