Private and Sovereign AI.
Keep control of your own data.

Search your documents, draft from approved sources and run workflows with human approval. Models, data and search run on premises or in your private cloud.

Talk to an Engineer See how we build

Trusted on systems that cannot fail

SiemensYunex TrafficAutobahn
Finastra
InfraSignal
Abidat
PTV Group
Körber
Westfalia
FrontFundr
GFA Group
Seneca ESG
Cygon
Dasan
Aimsun
eGo Digital
Maoneng
OnOffice
Reinstil
200+
Engineers
Across Hanoi, Aachen and Tokyo
93%
Client retention
Across all projects
12+
Years
Engineering for enterprise clients
15
Countries
Where we've delivered
The problem

What keeps AI from reaching production?

An AI pilot needs more than a working model. To go live, it must meet your data rules, show its sources and respect existing access permissions.

  1. 01

    Sensitive data must stay private

    Contracts, designs and customer records cannot go to a public AI service. Pilots stall when they cannot meet those restrictions.

  2. 02

    AI answers need sources you can check

    Engineers, lawyers and auditors need to trace figures and claims to the original documents before acting on them.

  3. 03

    Document permissions must carry through

    Knowledge spans file shares, wikis and business systems. Search results and AI answers must respect each source's access rules.

What it does

One private AI platform.
Find, draft and act.

Find answers, draft documents and run approved workflows. Your data, models and logs stay on your infrastructure.

AI knowledge search with citations

Find

Ask questions across your documents in plain language. Get answers with source citations, drawn from content you have permission to access.

AI drafting from approved sources

Draft

Turn approved documents into reports, summaries and tables. Follow source links to check the figures before you share the draft.

AI agents with human approval

Act

Let an AI agent prepare the work. A person reviews and approves it before the agent changes your systems.

Explore AI workflow automation
What we build

Private AI deployment, under your control

Run the platform on premises or in your private cloud, with an air-gapped option for isolated environments. You control the data, models and infrastructure.

Reference private AI site: primary, standby and test environments with backups inside your boundary; only sign-in reaches the corporate directory, there is no internet link, and backups copy nightly to a secondary site. Your site Primary, active Standby, passive Test Data and search AI models on local GPUs Application services Sign-in and access Backup storage Backup appliance Production network Internet No link Corporate directory Sign-in only Secondary site Nightly backup copy
Enterprise-ready

Private, secure and yours to run

On-premises, private cloud or air-gapped

Choose where the platform runs. Models, data and search stay within the environment you control.

Your access rules, applied to AI answers

Each query checks the user's permissions. Answers draw from and cite only the sources that person is allowed to access.

AI activity logs for audit review

Record questions, sources, answers and approvals in your own logs so reviewers can trace how the platform was used.

Tested and signed before deployment

Each release is tested, scanned for vulnerabilities and signed before deployment to your environment.

The workspace

What your teams work with

AI answers with sources you can check

Ask questions across documents you can access. Follow citations to the source and see when the search cannot find enough information.

Extract deadlines and obligations with AI

Select documents to summarize, compare or review for deadlines, obligations and risks. Check extracted items against the linked clauses.

Search across SharePoint, file shares and wikis

Connect existing sources to AI search without moving the original documents. Search respects each source's access rules.

Track AI usage.
Set limits by team.

Track requests, model tokens and budgets for each team. Set usage limits that take effect on the next request.

A private AI workspace answering a question about bolt torque, with two cited documents and a note on what is not in the sourcesKey dates and obligations extracted from three selected documents, each row linked to its clauseKnowledge base listing six sources with document counts, who can see each one and sync statusUsage and limits per team: questions against limits, model tokens and budget used
Models

Open-weight AI models.
Hosted on your GPUs.

We select models for your tasks, languages and hardware, check the applicable licences and serve them through your own gateway.

  1. 01

    Llama by Meta

    General-purpose models for private assistants and document search. We select the model size for your workload and hardware.

  2. 02

    Mistral by Mistral AI

    Open-weight options for multilingual work and coding. We assess the specific model's capabilities and serving requirements.

  3. 03

    Qwen by Alibaba Cloud

    Models for multilingual and long-document tasks. We select a version for your language and context requirements.

  4. 04

    Gemma by Google

    Open-weight models for tasks such as extraction and classification. We match the model size to your available GPU capacity.

  5. 05

    DeepSeek

    Open-weight reasoning and coding options hosted on your GPUs. Model inference runs within your environment.

  6. 06

    Phi by Microsoft

    Small language models for focused tasks. We assess speed, hardware requirements and answer quality against your use case.

How we build it

Private AI implementation.
Tested before it reaches your site.

AI agents accelerate development. Senior engineers define acceptance criteria, review changes and approve releases before deployment.

  1. Assess your environment

    Review your infrastructure, data and operating rules. Define the deployment architecture and capacity requirements.

  2. Define what success requires

    Turn each use case into specifications and acceptance tests, including which sources the AI may use and cite.

  3. Build and review

    AI agents build against the specifications in a separate environment that mirrors your setup. Senior engineers review each change.

  4. Test and sign

    Test each release, scan it for vulnerabilities and sign it before it leaves the build environment.

  5. Deploy and operate

    Transfer signed releases through your approved process. Deploy the platform with the access controls agreed for your environment.

What the framework delivers
Faster time to market
30-50%
Less rework and rip-out
40-60%
Delivery throughput per engineer
2-3x
Requirements covered by tests
90%+
Explore the Agentic SDLC
Tech stack

An AI stack your team can run and inspect

Run the stack within your environment. We select tools around licence terms, hardware requirements and the skills your team already has.

  • PyTorch
  • TensorFlow
  • scikit-learn
  • NumPy
  • pandas
  • Rasa

AI knowledge search
with source citations

AmatriumGPT answers questions from a materials company's technical library, with citations engineers can check.

AmatriumGPT sign-in screen: a secure AI assistant that reads, summarizes and translates documents and shows its sources
5.5 months
From kickoff to production
30-40%
Less time spent on document queries
Read the case study
Case study · Materials engineering

AI knowledge search for a technical library

Engineers needed answers from thousands of specifications, datasheets and research reports. Filename searches missed the technical meaning they needed, and answers required sources they could verify.

We built AmatriumGPT on Azure, with one search index across the library, cited answers and translation between English and Japanese that preserves document formatting.

Results

From kickoff to production
5.5 months
Seats and document uploads
Unlimited
Less time spent on document queries
30-40%
Sector
Manufacturing, United States
Engagement
Project delivery, 5.5 months
View all case studies
Testimonials

What clients say.

“Very professional technical work, and high quality of the results. Professional and systematic project management. We are very satisfied, and in any case continue our work with them.”
Paul Mathias
Paul Mathias
Owner, MAT.TRAFFIC
Clutch GoodFirms TechReviewer
How we work together

Plan your private AI deployment.
Build, then scale.

  1. 01

    Architecture and sizing

    Get a deployment design, GPU and storage sizing, a security review and a costed implementation plan.

    Fixed price

  2. 02

    Private AI implementation

    We build, test and deploy the platform in your environment, with agreed deliverables at each milestone.

    Fixed price per milestone

  3. 03

    Managed AI operations

    We monitor, patch and update the platform, or hand it over to your team with a runbook and on-call support.

    Monthly retainer

Prefer to pay for outcomes?

A fixed fee covers delivery. An outcome bonus is payable only for measurement periods when the agreed KPI meets its target.

We agree on the baseline, target, measurement data and scope-change terms before work starts. Depending on the project, the KPI may measure processing time, cost per transaction, downtime or automation rate.

Discuss outcome-based pricing

Example: time to answer a document query

Agreed target: 12 minutes or less

Minutes per query Fees payable
Fixed fee Outcome bonus

Q3: 11.5 minutes. Target met. Fixed fee plus outcome bonus payable.

Certified, accredited and independently reviewed

ISO 27001:2022 Information Security Management System certified ISO 9001:2015 Quality Management System certified Claude Partner Network member
AWS Partner
Clutch Top App Modernization Service company, Vietnam 2026Clutch Top Cloud Consulting Company, Vietnam 2026Clutch Top Machine Learning Company, Vietnam 2026

Explore other services

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FAQ

Frequently asked questions.

Something not covered here? Ask an engineer directly, no SDRs, no funnel.

Can it run fully air-gapped?

Yes. Models, search and applications run on hardware inside your site. In an air-gapped deployment nothing connects to the internet, and updates arrive as signed, tested releases through your own transfer process.

Which models can we use?

Models you are licensed to run on your own hardware, including open-weight models, sized to your GPUs. Where your policy allows it, a private cloud tenant can also host commercial models. We confirm the choice during architecture and sizing.

How are permissions kept in search?

Each source keeps its own access rules. The index records who may see each document, and every query is filtered by the person's roles from your directory, so an answer only cites what that person can open.

Who owns the platform and the data?

You do. You own all IP, including the code, the configuration and the specifications, and the platform runs in your environment. You also get direct access to our founders and board from kickoff.

How does this relate to Mekong OS?

Mekong OS is Eastgate's platform for agentic process transformation. When you move from answering questions to running governed workflows with human approval, it can run inside the same private boundary.

Contact

Let's Work Together

German engineering discipline meets private AI delivery. Send a short note and we'll reply within one business day, straight to an engineer, no SDRs, no funnel.