Private LLM Services on Our Own Hardware
Language models that run where your data already lives
ZingZee runs open-weight language models on its own hardware in Cyprus and on dedicated cloud servers in Germany, so a client's documents, bookings and ledgers never reach a public model API. Each deployment is scoped in a strategic assessment, sized to the workload and handed over with the model, the prompts and the evaluation set under the client's control.
What a private LLM is
ZingZee's private LLM services cover the selection, deployment, and operation of language models on hardware ZingZee or the client controls. A large language model, or LLM, is the software behind an AI assistant: it reads text, answers questions, summarises documents, and classifies messages. A public model runs on a vendor's servers and every document sent to it leaves the company. A private model runs on a GPU server in ZingZee's own facility in Cyprus, on a dedicated cloud server in Germany, or on a machine the client owns, behind the client's own login, with every prompt and answer logged for audit. For a business owner, a private model matters when the documents involved are contracts, policies, ledgers, or customer records that must stay under the company's control.
What we do with Private LLMs on our own hardware
A private LLM deployment is a language model served from a machine ZingZee controls, behind the client's own authentication, with no request leaving for a third-party API. ZingZee runs open-weight models on GPU servers in Cyprus and on dedicated cloud servers in Germany, and installs the same stack on hardware a client owns. The application logs every prompt and answer for audit.
Model selection is part of the assessment. ZingZee's engineers benchmark candidate models against the client's real documents and questions, measure accuracy, speed and memory on the target hardware, and record the result before a model is fixed. Fine-tuning is used only where retrieval and prompting fall short, because a tuned model is harder to update and harder to explain.
Around the model sit a retrieval layer over the client's documents, deterministic extraction for figures and dates, a tool layer that lets the model query a database or raise a task, and an evaluation set re-run on every change. The insurance policy desk in the case studies runs on this pattern, with a private file store, key-only access and no training on client data.
When a private LLM is
the right choice.
When a private LLM is the right choice
A private LLM is the right choice when the documents involved are regulated or commercially sensitive, such as policy wordings, contracts, bank statements, and customer correspondence, and no vendor's contract may govern where they go. It is the right choice once an application reads thousands of documents a month, because a model on owned hardware costs the same on a busy day as on a quiet one. It suits a company whose data must stay in a named country, since the same stack installs on a server in that jurisdiction. It also suits work that must be auditable, where each answer is traced to the prompt, the retrieved passages, and the model version that produced it. ZingZee benchmarks candidate models on the client's own documents before any model is fixed.
When a private LLM is the wrong choice
A private LLM is the wrong choice for a low-volume task on public information, such as drafting marketing copy from published material, where a public model API costs less and needs no hardware. It is the wrong choice where the problem is a missing rule or a missing field in the platform, which is fixed in code without a model. Fine-tuning a private model is the wrong choice where retrieval and prompting reach the target accuracy, because a tuned model is harder to update and harder to explain. ZingZee's assessment measures the volume, the sensitivity of the documents, and the accuracy reached by retrieval alone before a private deployment is proposed, and states the reason in writing.
Why Private LLMs on our own hardware
Data stays on known hardware
Documents, bookings and ledger lines are processed on a server ZingZee or the client controls, so no model vendor's contract governs where the data goes.
Predictable cost at volume
A model on owned hardware costs the same on a busy day as on a quiet one, which matters once an application reads thousands of documents a month.
Models chosen on measured results
Each candidate is scored on the client's own questions before it is fixed, and the score is kept so a later model can be compared on the same set.
Every answer is logged
Prompts, retrieved passages and answers are written to an audit table, so a wrong answer can be traced to the passage that caused it.
Portable by design
The stack runs on ZingZee's hardware, a dedicated server or the client's own machines, and moving between them is a deployment rather than a rewrite.
Use cases
Document reading for regulated work
Policy wordings, contracts and statements read against a checklist, with each finding cited to a page.
Language models that run where your data already lives
Industries where ZingZee applies private models
ZingZee has applied private LLM services in insurance, where a policy-checking tool reads wordings on a private file store with key-only access and no training on client data; in financial services, where an accounting platform's invoice inbox reads photographs and PDFs into coded records; in travel and hospitality, where guest messages are classified and answered from the booking record; and in aviation training, where spoken answers are transcribed with domain vocabulary and graded against a rubric.
Private LLM services ZingZee provides
Model selection and benchmarking
ZingZee's engineers benchmark candidate open-weight models against the client's real documents and questions, measure accuracy, speed, and memory on the target hardware, and record the result so a later model can be compared on the same set.
Deployment on ZingZee, dedicated, or client hardware
The model is served from a GPU server in Cyprus, a dedicated cloud server in Germany, or the client's own machine, behind the client's authentication, with no request leaving for a third-party API and every prompt and answer logged.
Retrieval, extraction, and tools around the model
A retrieval layer over the client's documents, deterministic extraction for figures and dates, and a tool layer that lets the model query a database or raise a task, so the model explains what the code has already found.
Evaluation sets and accuracy monitoring
A set of real questions with known answers built in the assessment and re-run on every change to the model, the prompts, or the retrieval layer, with results kept beside the deployment log.
Assistants, intake, and message handling
Staff assistants over business data, invoice and form intake into structured records, and guest or customer messages classified and answered from the record, with a person in the loop where the rule says so.
Private LLM scope
- Deliverables
- The model deployment, the retrieval index, the extraction and tool layer, and the evaluation set, installed on the agreed hardware and connected to the platform, with audit logging running.
- Included as standard
- The evaluation runner re-run on every change, the security checklist signed off, monitoring of accuracy and cost, an operations runbook for the server, and a recorded handover to the operating team.
- Priced separately
- Hardware on the client's premises, additional workloads after the first, message and voice channels, and the platform the assistant sits inside are scoped and quoted as separate items.
- What the client provides
- The documents and the questions staff would ask, the sensitivity rules, the reference answers for the evaluation set, network access for an on-premises install, and a person who signs off accuracy.
- Outside the engagement
- Hardware purchase, hosting contracts for dedicated servers, and any commercial model licences are the client's. Open-weight models carry no fee, and ZingZee's hardware is priced per engagement.
How a private model project with ZingZee runs
A private model project with ZingZee runs through the five-phase delivery framework. The strategic assessment collects the documents, the questions staff would ask, the volume, the sensitivity rules, and the hardware options, and builds the evaluation set. The AI roadmap fixes which model and which workloads come first. Integration and deployment installs the stack on the chosen servers, connects it to the platform, and re-runs the evaluation set before go-live. Adoption and enablement trains the staff who use the assistant and the team that operates the server. Governance, optimisation and scale re-runs the evaluation set on every change and reviews cost, accuracy, and model versions as usage grows. Each phase opens with a scoping workshop and closes with a hardening workshop, where the deployment is tested against the evaluation set and the security checklist, and a delivery workshop, where the client's staff sign it off.
- Strategic assessment
- AI roadmap
- Integration and deployment
- Adoption and enablement
- Governance, optimisation and scale
Private LLM tooling
ZingZee's private model work uses one set of tools on every deployment, so a client who reads about document reading can expect the same on an assistant. The tooling covers:
- Open-weight models chosen per engagement and served on GPU hardware in Cyprus, Germany, or on the client's premises
- An inference server behind the client's authentication, with no outbound calls to a public model API
- Postgres with pgvector for the retrieval index, held beside the client's other data
- Deterministic extraction and a tool layer in Python around the model
- An evaluation runner that re-runs the question set on every change
- Audit logging of prompts, retrieved passages, answers, and model versions
Private LLM engineering practices
Every private model ZingZee deploys is chosen on a measured score against the client's own documents, and the score is kept so a later model is compared on the same set. Figures, dates, and clause references are extracted by deterministic code before the model reads anything. Every prompt, retrieved passage, answer, and model version is written to an audit table, so a wrong answer is traced to the passage that caused it. Client documents are used for retrieval and evaluation and never written into the model weights unless the client commissions a fine-tune for its own use. The evaluation set is re-run on every change. Access is scoped by role, the server carries the same monitoring and backups as the rest of the platform, and at handover the client receives the model, the prompts, the evaluation set, and the runbook under its own control.
Cost and time for a private model deployment
The cost of a private model deployment depends on the volume of documents and questions, the accuracy target, the hardware chosen, the retrieval and extraction work around the model, and the number of workloads. A single document-reading workload on ZingZee's hardware against an existing platform is a matter of weeks. An assistant over business data with several tools, an intake pipeline, and message handling is a matter of months and goes live workload by workload. Hardware on the client's premises is specified and priced in the assessment. ZingZee provides a written estimate after the strategic assessment and phases the budget to the client's priorities.
What happens next?
You describe the documents, the questions staff would ask, and where the data may sit.
An engineer reads it and replies within two working days with the shape of a strategic assessment.
You sign a non-disclosure agreement if you need one, and you receive a proposal with the phases, the estimate, and the team.
Related case studies.
Real projects, real numbers.
Frequently asked questions
Straight answers on Private LLMs on our own hardware work with ZingZee.