Specialize models for your domain.
When a general-purpose model isn’t enough, Silicore trains, fine-tunes, and specializes models so AI understands your terminology, processes, documents, and operational behavior.
Accuracy on domain-specific tasks. Figures are illustrative — every engagement is measured on a held-out evaluation set from your own data.
Specialization closes the gap between a model that guesses and one that knows.
A model that speaks your language.
When a general-purpose model is not sufficient, existing models are adapted — or dedicated pipelines developed — so the AI understands:
Fourteen ways to make a model yours.
The technique is chosen per engagement — based on the data available, the task at hand, and the accuracy bar the model must clear.
- 01
Supervised fine-tuning & instruction tuning
- 02
LoRA / QLoRA and parameter-efficient fine-tuning
- 03
Distillation and preference tuning
- 04
Reinforcement learning from domain feedback
- 05
Continual learning & synthetic data
- 06
Domain adaptation
- 07
Classifiers and specialized models
- 08
Custom embeddings and rerankers
- 09
Specialized multimodal models
Retraining an entire model is not always necessary.
In many cases, better results come from combining a smaller amount of fine-tuning with the systems around the model — what it is given, what it can retrieve, what it remembers, and what it can do.
Seventeen jobs for a specialized model.
Across documents, images, audio, and operational data — wherever a general model guesses, a specialized model knows.
From raw data to a living model.
Eight stages, managed end to end — and repeated as the business evolves.
One pipeline, run repeatedly.
Silicore manages the complete lifecycle — from the first collected document to a model in production, and back again as the business changes.
- Measured on your tasks
- Deployed where you decide
- Updated as the domain evolves
Gather the documents, records, images, and examples that define the domain.
Clean, label, and structure raw material into datasets a model can learn from.
Fine-tune an existing model, or train a dedicated one, on the prepared datasets.
Score the model on real tasks from your operation — not on generic benchmarks.
Adjust data, parameters, and technique until the evaluation clears the bar.
Ship the model to your cloud, your servers, or a fully isolated environment.
Track accuracy, drift, and failure modes once the model is live in production.
Retrain and refresh as your terminology, processes, and data evolve.
Trained on your data. Deployed on your terms.
Models are trained and specialized on the company’s proprietary data, then deployed where the company decides — creating proprietary, vertical AI models designed around the organization’s data and processes.
Dedicated cloud infrastructure
Reserved cloud capacity running only your models, for only your workloads.
Private cloud environments
Isolated cloud tenants — elastic, but never shared.
Company-owned servers
Hardware the company already owns, operates, and controls.
On-premise infrastructure
Servers physically installed at the client's premises.
Fully isolated environments
Air-tight setups where nothing leaves the perimeter.
Every model is trained on the company’s proprietary data — and belongs to the company.
Make the model fit the work.
Share the domain gap — terminology, documents, or outcomes that miss today.