Models tuned to your domain and your language
Gezora fine tunes language models on your own domain, so terminology, tone, and the judgement calls a model makes match how your business actually works.
From dataset to deployed model
- Dataset assembly
- Data cleaning
- Holdout split
- Labeling review
- Instruction formatting
- Format conformance checks
What a tuning engagement delivers
- Training data built from your work
- A model that matches your domain
- Evaluation against the base model
- Deployment, monitoring, and retraining
What tuning changes about the model
Speaks your terminology
The model uses the words your business uses instead of generic language that needs editing afterwards.
Measured against a baseline
Every gain is compared to the base model on an evaluation set agreed before tuning starts.
Quality that holds
Monitoring watches for drift as your domain moves, and retraining runs on a cycle.
Honest advice first
If retrieval or better prompting solves your problem, we say so rather than tune a model.
From dataset to deployed model
- Dataset assembly
- Data cleaning
- Holdout split
- Labeling review
- Instruction formatting
- Format conformance checks
- Evaluation set
- Baseline comparison
- Regression checks
- Model deployment
- Drift monitoring
- Retraining cycle
What a tuning engagement delivers
Tuning is only worth doing when a general model keeps getting your work wrong even with careful prompting in place. When it is, this is what the work covers.
Training data built from your work
Examples are assembled from the work your team already does, then cleaned of the noise and duplication that quietly degrades a model. A person reviews the set before anything is used, because a labeling error becomes a model behavior.
A model that matches your domain
Tuning targets the terminology, tone, and judgement calls a general model keeps missing on your work. Output follows your own wording and format, instead of arriving in a shape someone has to correct first.
Evaluation against the base model
An evaluation set is agreed before tuning begins, so the target is fixed in advance. The tuned model is compared against the base model on that set, with regression checks so a gain in one area cannot hide a loss in another.
Deployment, monitoring, and retraining
The tuned model is deployed into your environment and watched for drift as your domain and your data move. Retraining runs on a cycle, rather than waiting for someone to notice that quality has slipped.
How a tuning engagement runs
Every step is agreed with you before it happens, and the result is measured rather than described.
- 01
Step 1, Assessment
We check whether prompting or retrieval already solves the problem before recommending any tuning at all.
- 02
Step 2, Data preparation
Training examples are assembled from your real work, cleaned, and reviewed by a person before use.
- 03
Step 3, Baseline
An evaluation set is agreed and the base model is scored on it first.
- 04
Step 4, Tuning
The model is tuned on your data and compared against that baseline, with regression checks.
- 05
Step 5, Deploy and monitor
The tuned model goes into your environment with drift monitoring and a scheduled retraining cycle.
Where tuning earns its cost
Tuning earns its place in a narrow set of situations, and these are what they look like from the inside.
Teams patching prompts around the same failure
Today someone rewrites the prompt every time the model misses the same judgement call, and the prompt grows without the output getting steadier. Tuning moves that correction into the model itself, once the assessment confirms prompting has genuinely run out.
Companies with heavy internal terminology
Today output arrives in general language and someone edits your own words back into it before it can be used. A tuned model writes in the terminology your business already uses, so that editing pass stops being part of the job.
Teams who need output in a fixed format
Today the content is usable but the shape is wrong, so a person reformats every response before it can go anywhere. Tuning targets format alongside wording, so output arrives in the structure your process already expects.
Companies sitting on records of completed work
Today that archive is storage, written once and never read again. It is also the raw material for a training set, assembled from your real work, cleaned of noise and duplication, and reviewed by a person before anything is used.
What you own at the end
A tuning engagement hands over the model and everything needed to judge it, question it, and rebuild it.
- The tuned model deployed in your environment
- The cleaned and reviewed training set
- The evaluation set agreed before tuning
- Baseline and tuned scores side by side
- The regression checks and their results
- A retraining schedule and configured drift monitoring
We stay on through one retraining cycle so your team can run the ones after it.
FAQ about LLM Fine Tuning
Straight answers on scope, timelines, and what running LLM Fine Tuning asks of your team.
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Tell us what you want to automate. We will map the workflow, deploy the right agents, and train your team to run them.
- Every agent is trained on your own workflows, never a generic template
- Most deployments are live within two to four weeks
- SOC 2 compliant, with a complete audit trail on every deployment