AI Assistants & Knowledge Systems

Private AI

Your data does not leave. The artificial intelligence does.

Many companies hold back on AI for a concrete reason: sensitive data cannot run on third-party cloud infrastructure. Clinical data, confidential documentation, financial information, intellectual property — entrusting them to external services is a legal, competitive and reputational risk.

The solution is not to give up on AI. It is to bring AI inside your perimeter. We design and deploy AI systems entirely on-premise or on dedicated private infrastructure: models running on your servers, on your data, under your control.

Problems we solve

Blocked knowledge, critical dependencies and inefficiencies holding back growth

Sensitive data that cannot leave the corporate perimeter

In some sectors sending data to an external API is simply not an option. Period.

Dependence on cloud providers

Relying on external services means accepting their costs, limitations, conditions and unilateral changes.

Generic models that do not know your domain

A general-purpose LLM responds well on everything and excellently on nothing specific.

Latency and reliability

Depending on a cloud service in production means depending on its availability. On-premise eliminates this variable.

Use Cases

Real projects, measurable results

For a client with stringent data privacy requirements, we designed and deployed an entirely on-prem…

Private deployment on dedicated hardware — NVIDIA A100

For a client with stringent data privacy requirements, we designed and deployed an entirely on-premise AI system on NVIDIA A100 GPU with an LLM fine-tuned on the specific domain and a RAG pipeline on internal documentation.

No data leaves the client's infrastructure. Enterprise-level performance, total control.

Private deployment on dedicated hardware — NVIDIA A100

We developed fine-tuning pipelines for LLM and VLLM calibrated on technical documentation, company …

Fine-tuning of LLM and VLLM on proprietary data

We developed fine-tuning pipelines for LLM and VLLM calibrated on technical documentation, company data and specialist domains.

Performance superior to any out-of-the-box solution, on your specific domain.

Fine-tuning of LLM and VLLM on proprietary data

Ingestion, preprocessing and indexing pipelines entirely on-premise: from document collection to ve…

Private data management for AI systems

Ingestion, preprocessing and indexing pipelines entirely on-premise: from document collection to vector index construction, everything remains in the client's infrastructure.

Zero external exposure, zero performance compromises.

Technology Corner

The architecture and technologies that make the difference

Our projects don't rely on off-the-shelf solutions. Every system is designed on the right architecture for the specific problem — and the technology choice makes the difference between a tool that works in demo and one that holds up in production.

On-premise deployment and dedicated infrastructure

✓ Deployment on dedicated GPU hardware
NVIDIA A100 and equivalent architectures
✓ Infrastructure optimization
for AI workloads in production
✓ Hybrid on-premise / private cloud architectures
to balance control and scalability

Model fine-tuning and adaptation

✓ LLM fine-tuning
on proprietary data and documentation
✓ VLLM fine-tuning
for domains with combined visual and textual content
✓ Parameter-efficient fine-tuning
LoRA, QLoRA to reduce computational requirements

Private data management

✓ On-premise ingestion and preprocessing pipeline
✓ Private vector indices
pgvector, Weaviate, Qdrant for retrieval on internal documentation
✓ Access control and traceability
of queries

Interested in this service?

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