Private Inference API
Fine-tuning

Expert tuning & deployment

For scenarios where RAG alone can’t deliver. Finetuning embeds your knowledge, style, and processes directly into the model, with full privacy and enterprise-grade control.

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Why private tuning matters

01

Limited domain understanding

Out-of-the-box models often lack familiarity with your specific terminology, style, and workflows — leading to shallow or inconsistent results.

02

Difficulty with specialized tasks

Generic LLMs struggle with technical or domain-specific challenges that require expertise beyond general training data.

03

Generic output on
edge cases

When handling nuanced scenarios, off-the-shelf models tend to produce vague or imprecise answers that don’t meet enterprise standards.

By fine-tuning a foundation model on your proprietary data you transform a generic model into a domain-expert assistant tailored to your business.

What we offer

01

Full tuning & LoRA options

We support both full fine-tuning and LoRA-based adapter tuning, depending on your needs, data, and resource constraints. Full tuning is ideal for deep specialization across all aspects of a task, while LoRA offers a lightweight and efficient alternative that often requires far less data and compute yet still delivers strong improvements on domain-specific tasks. This flexibility ensures you get the right level of customization, whether you need broad domain mastery or narrow behavioral tuning.

02

Expert support & service

Our team guides you through data preparation, hyperparameter selection, quality validation, and deployment — from start to finish. We help design training sets, select base models, monitor fine-tuning progress, and ensure performance meets your standards. For organizations without in-house ML expertise, we act as your dedicated AI tuning partner, delivering reliable, production-ready results without the guesswork. Our step-by-step approach covers feasibility, tuning, and running your model in production.

03

Private, dedicated infrastructure

Your model runs on private endpoints with dedicated GPUs, ensuring all data remains within your secure environment — and your model stays fully yours. With no shared infrastructure or multi-tenant risks, you benefit from a high-performance setup tailored to your workloads. This foundation provides the performance, scalability, and isolation required for enterprise-grade AI solutions.

When fine-tuning is better than RAG

Many companies rely on RetrievalAugmented Generation (RAG) — combining an LLM with an external knowledge base. RAG excels when you need dynamic access to large or frequently-updated information repositories. But sometimes, RAG alone isn’t enough. Fine-tuning shines when:

Stable domains

When your domain is well-defined and rarely changes, tuning gives the model deeper, more reliable expertise than RAG alone.

Embedded expertise

When you need the model to internalize your logic, vocabulary, style, or workflows — not just retrieve information.

Performance first

When low latency and high throughput matter, a tuned model delivers faster, more efficient responses without retrieval overhead.

Unlock the full potential of your model

When your use case demands deeper expertise than RAG or generic models can provide, fine-tuning delivers the precision and reliability your business needs.

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Where private tuning makes the difference

01

Industry-specific compliance & regulation

E.g., legal, medical, financial domains where terminology, rules and reasoning are complex and stable.

02

Code generation & internal tooling

Customizing a model to work with your unique codebase, libraries, naming conventions, or internal APIs.

03

Customer support & documentation

Building chatbots or assistants that understand company-specific product details, style guidelines, and brand voice.

04

Brand-aligned content generation

Marketing copy, reports, or documentation that needs consistent tone, style, and domain knowledge.

05

Data-sensitive workflows

When data privacy and security matter (e.g., internal documents, trade secrets), running everything on private infrastructure ensures control and compliance.

Deploy AI and get results without the risks

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