Your employees are already using AI.
The question is whether your organisation controls where its information goes, how it is used, and who can access the results.
MECI designs, deploys, and supports private AI environments for organisations whose confidential information, intellectual property, and internal knowledge cannot be treated as ordinary cloud data.
We provide the infrastructure, models, security controls, integrations, and operational support required to put AI into daily use—inside the control boundary your organisation requires.
Put AI to work without giving away control
Public AI services are convenient. They are also built for general use.
Your organisation may need something different:
- confidential legal or financial documents;
- proprietary engineering and manufacturing knowledge;
- internal code and technical documentation;
- regulated information;
- client records;
- research and development material;
- security-sensitive operational data;
- business knowledge accumulated over decades.
MECI helps you use AI with that information while retaining control over the environment in which it is processed.
The result is not another chatbot subscription. It is an AI capability designed around your organisation’s data, people, systems, and obligations.
What MECI delivers
We design the complete environment rather than handing you a server and leaving you to assemble the rest.
Depending on your requirements, this can include:
- dedicated AI inference infrastructure;
- private model hosting;
- secure user access and identity integration;
- internal document and knowledge workflows;
- retrieval over approved company information;
- code and repository assistance;
- ticketing and support automation;
- secure integrations with existing systems;
- monitoring, maintenance, and upgrades;
- operational support after deployment.
We can work with your existing technology team or provide the expertise required to take the system from design to production.
The value is in the working system
Buying hardware is not the same as deploying private AI.
A functioning enterprise environment must answer practical questions:
- Where does data enter the system?
- Where is it stored?
- Which users can access it?
- Which documents can each user retrieve?
- How are models selected and updated?
- How are outputs monitored?
- What happens when hardware fails?
- How is the system backed up?
- How does it connect to existing business software?
- Who is responsible for keeping it operational?
MECI addresses the complete chain—from infrastructure and model hosting to access, workflows, support, and continuity.
Use cases
Internal knowledge
Give authorised employees a fast way to search, summarise, and work with internal information without sending confidential material to a general-purpose public AI service.
Documents and research
Help teams review, compare, classify, and draft from large collections of approved documents.
Code and technical work
Provide private assistance for development, documentation, code search, issue analysis, and internal technical knowledge.
Support and ticketing
Connect AI to approved support information and workflows so teams can find answers faster and deal with repetitive work more efficiently.
Operations and engineering
Make manuals, procedures, maintenance records, reports, and other operational knowledge easier to use.
Regulated and sensitive work
Design the environment around the access, audit, data-handling, and deployment requirements of your organisation.
The exact use cases depend on your information, systems, security model, and objectives. We begin with those—not with a generic AI package.
Why organisations choose private AI
A private AI environment can provide:
Control: Your organisation determines how the system is deployed, accessed, integrated, and operated.
Confidentiality: Sensitive information can remain within the environment and policies you specify.
Ownership: Your AI capability becomes an organisational asset rather than a workflow entirely dependent on a public provider.
Adaptability: The system can be designed around your models, documents, users, applications, and operational processes.
Continuity: Your teams are not forced to redesign their entire AI workflow every time a public service changes its terms, limits, pricing, or capabilities.
Practical support: MECI can remain involved after deployment, helping maintain the environment as your requirements develop.
Built around your organisation
There is no single private-AI configuration that suits every company.
A deployment may need to account for:
- the number of users;
- concurrent demand;
- model size;
- response-time requirements;
- document volume;
- data location;
- identity and access policies;
- existing applications;
- network architecture;
- backup and recovery;
- power and cooling;
- future expansion.
We design the system around those requirements and explain the trade-offs before equipment is selected.
For technical teams
For teams that want the engineering detail, MECI can design around Kimi K3-class inference infrastructure, including:
- 8 × NVIDIA H100 80GB SXM5 GPUs;
- HGX- or DGX-class chassis;
- dual server CPUs;
- 512GB–1TB system memory;
- 2–8TB NVMe storage;
- 100/200GbE or InfiniBand networking;
- the power, cooling, and physical infrastructure required to support the system properly.
This is an example of the class of infrastructure that may be appropriate for demanding private inference workloads. The final design depends on model requirements, concurrency, latency, data volume, deployment location, and expected growth.
We can provide a deeper technical design covering:
- model serving;
- GPU allocation;
- networking;
- storage;
- identity and access;
- monitoring;
- backup;
- high availability;
- security boundaries;
- capacity planning;
- expansion options.
From assessment to operation
1. Understand the requirement
We examine your data, users, workflows, existing systems, security requirements, and intended outcomes.
2. Define the use cases
We identify where private AI can create practical value and where it should not be used.
3. Design the environment
We specify the infrastructure, models, integrations, controls, and operating model.
4. Deploy the system
We install and configure the environment, connect approved data sources, and prepare the initial workflows.
5. Test with real work
We test performance, access, retrieval quality, reliability, and user workflows against representative requirements.
6. Put it into operation
We support adoption, monitor the system, maintain the infrastructure, and help develop additional use cases.
Private AI should be useful on Monday morning
The purpose is not to own an impressive machine.
The purpose is to help your people do valuable work:
- find information;
- understand documents;
- draft and review;
- solve technical problems;
- answer internal questions;
- support customers;
- reduce repetitive work;
- preserve institutional knowledge.
A private AI deployment succeeds when people use it in the course of their work and the organisation can operate it with confidence.
Is private AI right for you?
Private AI may be worth considering if:
- your employees are already using public AI tools;
- your data cannot be sent freely to external services;
- your organisation operates in a regulated or sensitive environment;
- you need AI connected to internal knowledge;
- you require more control than a standard SaaS subscription provides;
- you want to build an organisational capability rather than rent an isolated tool;
- you need a partner to design and operate the complete environment.
It may not be necessary if your needs are limited to casual personal productivity or non-sensitive public information. We will tell you that if it is the right answer.
Start with the problem, not the hardware
Tell us what your teams need to do, what information must remain under your control, and what systems the AI must work with.
We will help you determine:
- whether private AI is appropriate;
- which use cases should come first;
- what kind of deployment is required;
- what infrastructure those use cases demand;
- what the implementation would involve;
- how the system could develop over time.
Discuss your private AI requirements with MECI.
Private AI for organisations that need capability without surrendering control.
