Your Data Stays Yours. Your AI Stays Ahead.
Private large language models fine-tuned on your data, running in your infrastructure, answerable only to you.
No third-party exposure. No per-token cost at scale. Complete data sovereignty from day one.
The Hidden Cost of Public AI APIs
Enterprise teams are building on borrowed infrastructure. Every prompt sent to a commercial API is a data transfer.
Public Commercial APIs
- ✕Sensitive data processed on infrastructure you don't control
- ✕No guarantee of model behaviour between version releases
- ✕Token costs that compound indefinitely as adoption grows
- ✕Vendor lock-in with zero portability of capability
- ✕Regulatory exposure under Australian Privacy Act and sector-specific frameworks
- ✕Your proprietary data trains their next model
Private Fine-Tuned LLMs
- ✓Model trained on your data, owned entirely by you
- ✓Deployment inside your VPC, on-prem, or sovereign cloud
- ✓Predictable infrastructure costs that don't scale with usage
- ✓Auditable outputs with full explainability and traceability
- ✓Zero dependency on any external API or third-party service
- ✓Built on open-source foundations — Llama 3, Mistral, Mixtral
Enterprise-Grade Capabilities
Every engagement is built on a production-proven technical foundation designed for enterprise compliance and scale.
Data Curation & Preparation
We conduct structured discovery across your document repositories, databases, and internal knowledge bases — extracting, cleaning, and formatting training datasets that accurately represent your domain. PII redaction, deduplication, and quality filtering are applied before a single training run begins.
PEFT / LoRA Fine-Tuning
Using Parameter-Efficient Fine-Tuning (PEFT) techniques — specifically LoRA and QLoRA — we adapt foundation models like Llama 3 and Mistral to your domain at a fraction of full pre-training cost. The result is a specialist model that understands your business context with measurable benchmark improvements.
Guardrails & Anti-Hallucination
Multi-layer guardrail systems prevent uncontrolled generation: constitutional prompting constraints, RAG grounding against verified knowledge bases, output validation pipelines, and confidence-scoring that flags low-certainty responses before they reach end users.
On-Prem / VPC Secure Deployment
The trained model and inference stack are deployed entirely within your security perimeter — on-premises, a private GCP or AWS VPC, or an Australian sovereign cloud environment. No data ever leaves your infrastructure in production.
How It Works
A structured four-phase engagement from first conversation to production deployment.
Discovery & Scoping
A structured technical engagement to understand your use case, data landscape, compliance requirements, and infrastructure constraints. We define success metrics, identify the right foundation model, and produce a detailed project scope with timeline and investment.
Data Preparation
Our data engineering team works with your stakeholders to extract, clean, and format your training data. Automated preprocessing pipelines, privacy controls, and a curated dataset ready for fine-tuning — reviewed and approved by you before training begins.
Secure Model Training
Fine-tuning is conducted in an isolated compute environment under your account or within your own infrastructure. Iterative training cycles, benchmark evaluation, and a fully documented model with complete training methodology and performance results.
Deployment & API Integration
The model is deployed as a private inference API within your chosen environment, optimised for latency and throughput. OpenAI-compatible endpoint schemas for drop-in integration, developer documentation, and 90-day post-deployment support.
Ready to Own Your AI Infrastructure?
The organisations that will lead the next decade aren't renting intelligence from a third party. They're building it — privately, securely, and on their terms.
A 45-minute technical discovery call with our AI engineering team. We'll assess your use case, outline an approach, and give you a clear picture of scope, timeline, and investment — no obligation.
Book an Enterprise Consultation →Engagements typically begin from $35,000 AUD. Custom scoping available for complex multi-model or multi-tenant deployments.
Enterprise LLM — Common Questions
Which foundation models do you fine-tune?
We work with leading open-source models including Llama 3, Mistral, and Mixtral. Model selection depends on your use case, compute constraints, and performance requirements — we recommend the right architecture during the Discovery phase.
Does any of our data leave our infrastructure during training?
No. Training is conducted either within a dedicated cloud instance under your own account or on your on-premises infrastructure. Your data never transits to our systems. We provide the expertise, tooling, and methodology — the compute and data remain under your control.
What compliance frameworks do you design for?
We design deployments with Australian Privacy Act, GDPR, SOC 2, and sector-specific frameworks (healthcare, finance, government) in mind. Specific compliance scoping is conducted during Discovery to ensure the architecture meets your obligations.
What is the typical engagement timeline and investment?
Engagements typically run 8–16 weeks depending on data complexity and deployment scope. Investment starts from $35,000 AUD for focused domain fine-tuning projects. Complex multi-model or multi-tenant deployments are scoped individually.
Can the model be integrated with our existing enterprise systems?
Yes. We deploy with OpenAI-compatible API schemas for drop-in integration with existing tooling. We also provide custom integration support for enterprise platforms including internal portals, document management systems, and bespoke workflows.