Hydra Group Let’s talk

Private AI engineering

Private AI.
On infrastructure
you control.

We design, deploy, and operate AI systems on your hardware or within your cloud account. Run models privately, or connect your cloud applications to frontier models with clear data controls and usage budgets.

Discuss your AI deployment Explore deployment options
Private AI inside your environment For a private deployment, your data connects to self-hosted models and your team's workflows inside a defined boundary, on your hardware or within your cloud account. YOUR ENVIRONMENT YOUR DATA PRIVATE MODELS YOUR WORKFLOWS
Customer-owned hardwareCustomer-controlled cloud

From the first workload to ongoing operation.

Engineering Integration Deployment

01 / What we build

Start with work
that needs to get done.

Private infrastructure becomes useful when it solves a real problem. We start with one workload and evaluate the model, data, and integration it needs.

Internal knowledge

Find answers across approved documents and systems, with source references and access controls built around your organization.

Document workflows

Extract, classify, and summarize business documents within the agreed environment, with human review where the work requires it.

AI in your applications

Connect private or provider-hosted models to the tools your team already uses, with the testing, monitoring, and maintenance to support them.

02 / Private deployment

Two places to run.
One principle: your control.

On premises

On your hardware.

Run models on physical infrastructure your organization owns. We assess the workload, size the compute, deploy the system, and plan how it will be supported.

Customer cloud

Within your cloud account.

Deploy models inside infrastructure your organization controls, with an agreed network design, access policy, and operating budget.

Optional / Frontier model access

Cloud solutions
with frontier models.

Build applications in your cloud account that connect to frontier models through managed cloud services or model-provider APIs. We handle integration, access controls, usage monitoring, and budgeting.

Requests are processed by the selected inference provider. We agree what data can be sent and review its retention and regional settings.

Know the ongoing cost

Token pay-per-use remains.

Provider charges for input and output tokens still apply. These usage charges are separate from cloud infrastructure and Hydra’s implementation and support fees.

Implementation, infrastructure, model usage, and ongoing support are scoped separately.

Private, by design

Define the boundary.
Then build inside it.

“Private” needs a practical definition. Before deployment, we agree how data moves, what can connect, and who can operate the system.

Offline and air-gapped environments are scoped explicitly.

Data & systems
Approved sources, access permissions, storage, and backups.
Network & dependencies
Permitted connections, model sources, and any data sent to external inference services.
Updates & telemetry
How changes are reviewed and what operational data is collected.
Operational access
Who can administer the system and how that access is controlled.

03 / Our approach

Engineering from
first question to production.

  1. Establish fit

    Review the use case, data, workload, infrastructure, and budget. Decide whether private deployment makes sense.

  2. Design & validate

    Agree success criteria. Evaluate models and integrations against representative tasks in an approved environment.

  3. Deploy & integrate

    Configure the infrastructure, connect your systems, test the workflow, and document how it operates.

  4. Operate & improve

    Define support, monitoring, maintenance, model updates, and capacity planning for the system’s ongoing use.

The engineering behind Hydra

Built on experience.
Focused on your workload.

Hydra Group is led by a principal-level engineer with 12 years of experience building software from scratch at startups.

We bring that experience to the infrastructure, integrations, and operations that make a private AI system useful.

A clear operating budget

Plan capacity.
Understand the costs.

Self-hosted inference can remove dependence on a metered third-party model API. Hardware, cloud infrastructure, storage, and engineering operations still have costs.

Frontier model connections retain provider token charges. We plan for expected usage and capacity, with implementation and ongoing support scoped separately.

Let’s define the first workload

What should AI
do for your team?

Tell us what your team repeatedly searches for, prepares, or checks. We’ll explore the workflow, the right data controls, and a practical first implementation.

office@hydragroupllc.com

A useful starting point

  • The workflow you want to improve
  • Your hardware or cloud environment
  • Your data and access requirements

Start with a high-level description. We’ll agree how to review any confidential material separately.