Solution

Fine-Tuning & Custom Models

SFT, LoRA, and DPO pipelines on Bedrock, Azure, and Vertex — with the data work and eval harness to make it worth the spend.

All solutions

Models that know your domain

Fine-tuning only pays off when prompting and retrieval have run out of room. When it does pay off, we build the data pipeline, training runs, and evaluation that turn a base model into one that speaks your domain — smaller, faster, cheaper, or more accurate than a prompt-only baseline.

Outcomes

2-10x

cheaper per request vs. frontier baseline

Higher

accuracy on domain-specific tasks

Private

model weights under your control

How we build it

Our approach.

Prove you need it

First we exhaust prompting, retrieval, and model selection. If the eval still fails, we move to tuning — never the other way around.

Build the dataset

Curate, label, dedupe, and version. Most of the win comes from data quality, not training tricks.

Train, eval, iterate

SFT first, then LoRA for cost, DPO where ranking quality matters. Every run scored against the same battery as the baseline.

Deploy as private endpoint

Bedrock, Azure, or Vertex custom endpoint with autoscaling, quota, and cost controls. Rollback is a pointer change.

Capabilities

What you get.

Dataset construction and versioning

SFT, LoRA/QLoRA, and DPO/RLAIF training

On-cloud training (Bedrock, Azure ML, Vertex, SageMaker)

Baseline-vs-tuned eval on real tasks

Deployment as private endpoint with autoscaling

Continuous fine-tuning loops with feedback data

What it looks like

Production-shaped, from day one.

tune.yaml

# LoRA SFT run config
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
method: lora
lora:
  r: 16
  alpha: 32
  dropout: 0.05
train:
  dataset: s3://codelucent/datasets/intake_v4.jsonl
  epochs: 3
  lr: 2e-4
  per_device_batch: 8
eval:
  suite: intake_gold_v3
  compare_to: base
  gates:
    accuracy: ">= 0.92"
    regression_on_base_skills: "== 0"
deploy:
  target: bedrock_custom_model
  region: us-east-1

Architecture

A proven shape for this solution.

We adapt it to your cloud, data, and compliance requirements. Nothing here is boilerplate — every layer is justified by the numbers.

Data prep pipeline (dedupe, filter, format)

Labeling + review workflow

Training jobs on managed cloud compute

Eval harness comparing base vs. tuned on gold tasks

Private model endpoint with quota + cost controls

Use cases

Where this shows up.

Healthcare terminology and chart summarization
Domain-specific classification at inference cost
Tone and persona alignment for customer-facing bots
Structured output compliance for strict schemas

Stack

What we use.

We’re not religious about tools. We pick what fits your constraints and team.

AWS Bedrock Custom Models

Azure ML

GCP Vertex AI

AWS SageMaker

OpenAI Fine-Tuning

Hugging Face

LoRA / QLoRA

MLflow

Common questions

What teams usually ask.

When is fine-tuning worth it?

When prompting and retrieval plateau below your quality bar, or when a smaller tuned model is materially cheaper than a prompted frontier model at equivalent quality. Otherwise, skip it.

Can we keep model weights private?

Yes — we fine-tune inside Bedrock, Azure ML, Vertex, or SageMaker with customer-managed keys. Weights stay in your account.

How do we avoid breaking what already works?

Eval suites include regression checks on base-model skills, not just the target task. A tuned model that helps one thing and breaks five doesn't ship.

Keep exploring

Ready to accelerate your tech growth?

Schedule your free consultation today and let's discuss how we can help your business scale efficiently.