Studio›Services›ai-enablement›private ai deployment
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Service · AI Enablement

Your models. Your servers. Your data.

KreativeHub deploys private AI for medium and enterprise teams. We pick the right open-source model, run it on your hardware or your Vertex, Azure or Bedrock tenant, and fine-tune it on your data, so sensitive and high-volume work stays inside your perimeter and the cost stops climbing with every prompt.

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+78%Y/Y client growth
3.4×Median ROAS · paid
98%Retention · cumulative
−34%CPL · B2B engagements
LIVE

Measured in pipeline, not pageviews. Senior operators only. Five clients per quarter, capped on purpose.

— studio note
Frame · Notes

When the data cannot leave the building, you own the model

For regulated data, confidential work, or inference at high volume, a public application programming interface (API) is the wrong fit. The risk is too high or the bill grows with every successful use. At that point owning the model is the safer and cheaper answer.

Open-source models have caught up. Llama, Qwen, Mistral and DeepSeek are now good enough for production, and after fine-tuning on your domain they hold their own against the frontier on the tasks that matter to you. The hard part is no longer the model. It is choosing the right one, getting it onto your infrastructure, and running it like a system your team can trust.

That is the work we do. We benchmark the candidates against your real use cases, deploy the winner on your servers or your cloud tenant, fine-tune it on your data and tone, and stand up the serving, monitoring and rollback so it behaves like infrastructure, not a science project. Private AI deployment is one part of how we build AI capability, alongside frontier rollout and adoption across the AI Enablement hub. Start with a free AI audit.

Frame · Manifesto · 3 positions
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What private AI actually involves

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Choosing the right model

Open-source models vary widely in quality and cost to run. We benchmark the realistic candidates against your actual workloads, whether that is code, customer support or document analysis, and pick the smallest model that wins. A smaller model that passes your evaluations is cheaper to host and faster to serve than a larger one you do not need.

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Fine-tuning on your data

We prepare and structure your data, fine-tune the model on your domain and tone, then validate it against examples it has never seen. You do not need a huge dataset. For most tasks a few hundred high-quality examples are enough, and we help you generate or curate them. The result answers with your facts and sounds like your business.

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Running it like infrastructure

We deploy on your servers, inside your Vertex, Azure or Bedrock tenant, or fully air-gapped on-premise. We set up the serving stack with vLLM or text generation inference (TGI), the scaling, the monitoring and the rollback path. The system gets handed back to you running and documented, not as a prototype your team has to reverse-engineer.

Frame · Capabilities · 6 pillars
// capabilities

What a private AI deployment covers

Open-source models, benchmarked, fine-tuned and served on your own stack.

See how private AI fits the full AI capability
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    Open-source model benchmarking

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    LoRA and full fine-tuning

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    vLLM and TGI serving

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    Vertex, Azure and Bedrock tenants

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    Air-gapped on-premise hosting

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    Monitoring and evaluation

Frame · Method · 4-phase flow
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How we ship a private deployment

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01. Map the workloads

We separate the work that belongs on a private model from the work better left on a frontier API. Sensitive data, high-volume inference and domain-specific tasks usually justify owning the model. We name the candidates and size the saving before anyone trains anything.

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02. Prepare the data

We clean, label and structure your training data, then build a held-out evaluation set that reflects how the model will actually be used. Good data preparation is most of the result. Skip it and you fine-tune on noise.

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03. Fine-tune and evaluate

We train candidate models, then benchmark them against the held-out examples and against the frontier baseline. You see the numbers: accuracy on your tasks, speed, and cost per inference, so the choice is evidence, not a hunch.

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04. Deploy and monitor

We push the chosen model to production with the serving stack, evaluation dashboards and a rollback plan in place. If a new version regresses, we catch it and roll back before it reaches your users.

Frame · FAQ · 7 questions
// honest answers

Private AI questions, answered

Every question we get asked on first calls. Answered in writing — decide before you book.

Not for everything. A frontier model is broader and stronger on open-ended, general tasks. But for your specific domain, after fine-tuning on your data, a smaller open-source model often matches or beats it on the work that matters, at a fraction of the cost per inference. We benchmark both before recommending either, so the decision rests on numbers from your own use cases.
Less than most teams expect. For many tasks a few hundred high-quality examples are enough to fine-tune with a method like LoRA (low-rank adaptation). Quality matters far more than volume. We help you generate, curate and structure the examples, so a thin dataset is rarely the thing that stops a project.
Wherever your data and security requirements point. We deploy on your own servers, inside your Vertex, Azure or Bedrock cloud tenant, or fully air-gapped on-premise with no external network access. The common thread is that your data stays inside your perimeter and never trains anyone else's model.
No. We bring the engineering, deploy the model and the serving stack, and hand it back to you running and documented. You do not have to hire a machine learning team before you start. You need a partner who has deployed private AI before and will leave you with a system your existing engineers can operate.
Once volume is high enough, a private large language model (LLM) wins on cost. A public API charges per token, so the bill grows with every successful use. A self-hosted model has a fixed cost to run regardless of how many times you call it. For heavy, repeatable workloads the crossover usually arrives quickly, and we model the break-even point in the audit before you commit.
A typical private AI deployment runs 6 to 10 weeks, and the variable is almost always data readiness rather than the model. Workload mapping and data preparation take the first few weeks, fine-tuning and evaluation the next, then deployment and monitoring. We commit to a timeline in the audit, before you commit to us.
We hand over a documented system with monitoring and evaluation dashboards so you can see how the model performs in production. We set up a rollback path for new versions and can stay on for ongoing tuning as your data and use cases change. Either way you own the model, the weights and the infrastructure outright.
Send the studio a brief
Frame · Apply · new file
apply · q2-2603/05 open
Three slots remain · Q2 ’26

Own your AI stack outright.

Tell us which workloads are too sensitive or too costly to run on a public API, and we will show you which open-source models can replace them, where they should run, and what you will save before you commit to anything. Senior team, full transparency, no fixed packages.

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