Custom AI Models

We help teams decide when a fine-tune is enough, when retrieval will do, and when a custom model is the right investment. If custom training matters, we build it so you own the model and the artifacts.

Custom AI models, made for you, owned by you

Honest model strategy

We do not force every problem into custom training. We look at task structure, available labels, retrieval fit, latency budget, context limits, and operating cost before recommending prompting, RAG, fine-tuning, or full custom training.

From fine-tunes to from-scratch training

When a base model plus retrieval is enough, we say so. When your product needs domain-specific behavior, lower inference cost, tighter control, or weights you can own, we fine-tune or train a model around your actual use case.

Artifacts you keep

Weights, tokenizer choices, training pipeline, eval setup, inference stack, and deployment handoff belong to you. Goal is not long-term dependency on our team. Goal is durable capability in yours.

What We Make

AI Model Discovery • From $9,500
Model Fine-Tuning • From $29,500
Custom Model Training • Project-based
Dataset Audit & Feasibility
Training, Fine-Tuning & Evals
Inference, Optimization & Handoff

Start With Discovery

Two-week paid engagement. Written recommendation, fixed-price next scope, and a clear answer on whether custom training is worth doing.

Book Discovery Session

When This Service Fits

Teams Replacing API Dependence

You want lower long-term inference cost, tighter operational control, and fewer product constraints than rented black-box APIs allow

Businesses With Proprietary Data

You have domain data, task structure, workflows, or labels that generic assistants do not understand well enough to become core product behavior

Products Needing Smaller Purpose-Built Models

You need something sized for your task, latency target, memory footprint, or infrastructure instead of paying for a general model that does too much

Founders Wanting Real IP

You do not want your product moat to be prompt wrappers around someone else's intelligence layer

Operators Who Need Straight Answers

You want an honest recommendation, even if result is better prompting, retrieval, or a fine-tune rather than a bigger project

How Engagement Works

Same design as rest of site. Different substance. We start with evidence, not AI theater.

1

Discovery

Two weeks to review your task definition, data quality, labeling strategy, evaluation criteria, context requirements, and infrastructure constraints. You get a written recommendation, technical tradeoffs, and fixed-price next scope.

2

Choose Right Build Path

We decide whether your problem needs prompting, retrieval, supervised fine-tuning, preference tuning, or a custom-trained model. This is where most teams save money by not building the wrong thing.

3

Train & Package

We prepare datasets, run training or fine-tuning, define evaluation loops, compare checkpoints, build inference pipeline, document decisions, and prepare artifacts for handoff or deployment.

4

Deploy Where You Want

Run on your infrastructure, chosen cloud, or another target environment that fits your budget, compliance, throughput, and latency needs. We account for quantization, batching, and serving tradeoffs where they matter.

What Delivery Covers

This is model work, not only interface work. We focus on dataset quality, training method, evaluation design, inference economics, and deployment constraints.

Data Preparation

Dataset review, curation, deduplication, formatting, labeling strategy, and training-readiness checks

Model Adaptation

Architecture choice, parameter-efficient fine-tuning, full fine-tuning, and from-scratch training when task demands it

Evaluation & Cost Fit

Task-specific evals, failure analysis, checkpoint comparison, and tradeoffs in latency, cost, and maintainability

Inference & Handoff

Serving stack, quantization considerations, deployment pipeline, and operating notes so ownership stays with your team

Engagements & Pricing

Fixed prices where scope allows. Project pricing where custom training demands it.

AI Model Discovery

From $9,500A two-week paid engagement where we review your data shape, labeling reality, target task, failure tolerance, model options, and serving constraints. You leave with a written recommendation, technical rationale, and a fixed-price scope for whatever comes next, even if it is not us.

Model Fine-Tuning

From $29,500Four to eight weeks. We choose a base model, prepare datasets, fine-tune against your task, define evals, build inference pipeline, and hand you the artifacts. Finished package, fixed price, yours to keep.

Custom Model Training

Project-based pricingTwo to six months. A model trained from scratch on your domain, sized for your problem, evaluated against your task, and deployed where you choose. This is for teams that need actual model ownership, not another thin wrapper.

Why Lunover Does This Work

Lunover brings model-level technical judgment into client delivery, without pretending every problem needs research spend.

Built by Lunover

Lunover approaches model work from first principles. Not generic API assembly. Not AI theater. We work from the training problem outward, with attention to data quality, task framing, evaluation design, inference cost, and long-term ownership.Most AI shops assemble pre-made parts. We can do that when it is enough. When it is not, we work through dataset constraints, model size tradeoffs, training approach, serving requirements, and hand over something you can actually operate.Sometimes that is slower and costs more upfront. Often it leads to lower per-request cost, tighter latency control, and a model that fits your product better than a generic endpoint ever will.

  • custom AI models
  • fine-tuning services
  • train model from scratch
  • domain-specific AI models
  • own your AI model
  • custom LLM development
  • model training consulting
  • AI model feasibility
  • inference pipeline development
  • private model deployment
  • AI model ownership
  • custom machine learning models
  • custom AI models
  • fine-tuning services
  • train model from scratch
  • domain-specific AI models
  • own your AI model
  • custom LLM development
  • model training consulting
  • AI model feasibility
  • inference pipeline development
  • private model deployment
  • AI model ownership
  • custom machine learning models

Curious?

Start with AI Model Discovery. Two weeks, fixed fee, written recommendation at end. If answer is that you do not need us, we will say that.