Fine-tuning AI Models

Overview

What we deliver

We fine-tune open and closed AI models on your proprietary data so outputs match your domain, tone, and task requirements.

Generic foundation models often miss the mark on specialized vocabulary, formatting, or judgment calls that matter to your business. We fine-tune AI models on your curated datasets so they reason in your domain, write in your voice, and follow your task patterns reliably. Our team handles dataset preparation, instruction formatting, supervised fine-tuning, LoRA and QLoRA adapters, evaluation harnesses, and deployment to production endpoints. We work with open weights like Llama, Mistral, and Qwen, plus managed tuning on OpenAI and Anthropic where appropriate. Every project includes baseline benchmarks, side-by-side comparisons, and a rollback plan so you ship with confidence. We also document hyperparameters, training data lineage, and evaluation criteria so your team can iterate after handoff. The goal is a model that performs measurably better on the tasks you care about, not a science experiment.

Fit Check

Built for teams like yours

Who it's for

AI product teams
Enterprise data teams
SaaS companies
Healthcare and legal firms
Financial services

Pain points we solve

Generic models miss domain nuance
Inconsistent tone and formatting
High prompt engineering overhead
Token costs from verbose prompts
Compliance and accuracy gaps

What's included

Capabilities

Everything we cover in this engagement.

Dataset curation and labeling
Instruction and chat formatting
Supervised fine-tuning (SFT)
LoRA and QLoRA adapter training
DPO and preference tuning
Evaluation harness setup
Model quantization and packaging
Deployment to inference endpoints

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Discovery

We map use cases, success criteria, and data sources.

02

Dataset prep

We curate, clean, and format training and eval splits.

03

Training

We run SFT or adapter training with hyperparameter sweeps.

04

Evaluation

We benchmark against baselines and your acceptance tests.

05

Deployment

We package, deploy, and monitor the tuned model in production.

What you get

Deliverables & outcomes

What you get

Tuned model weights or adapters
Training dataset and eval splits
Benchmark report with baselines
Inference endpoint or container
Hyperparameter documentation
Monitoring and rollback playbook

Outcomes you can expect

Higher task accuracy on domain inputs
Shorter prompts and lower token costs
Consistent tone and formatting
Faster response latency
Reduced reliance on prompt hacks

Timeline

4 to 8 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

Hugging Face, Axolotl, Unsloth, OpenAI fine-tuning, Weights and Biases

KPIs we track

Task accuracy, eval score delta, token cost per call, latency, hallucination rate

Client stories

What clients say

"

We were paying three agencies and a lifecycle freelancer to argue over attribution. RevoraOps absorbed all of it in 30 days, killed our worst-performing Meta ad sets, and rebuilt the welcome flow from scratch. CAC dropped 31 percent in the first full month. Honestly the relief of having one weekly call instead of four was worth it alone.

Megan W.

"

We were drowning in tier-one tickets about password resets and appointment changes. They built a deflection layer on top of our help desk and kept their agents in the loop for anything sensitive. Volume to humans dropped 58 percent in two months and our patient NPS held steady. The hybrid handoff is the part most vendors get wrong. They did not.

P.M.

Proof