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Private AI models and on-premise LLMs trained on your data

Smaller, faster models trained on your data and running where your data lives.

Book a strategy callHow it works

What is an on-premise LLM?

An on-premise LLM is a language model that runs on servers you control, in your data center or your own cloud account, so prompts and documents never go to an outside API. For narrow, high-volume tasks we usually fine-tune a small language model on your examples, benchmark it against large hosted models, then deploy it in your environment.

Benchmark · ticket routing, 14 categories

Illustrative
MetricLarge hosted modelFine-tuned 8B
  • Accuracy on 2,000 held-out tickets94.1%95.3%
  • Median latency1.8 s0.3 s
  • Cost per 1,000 tasks$4.20$0.35
  • Data leaves your environmentYesNo
Decision Deploy fine-tuned model in your VPC

The problem

General-purpose models are expensive at volume, can be slow, and send data outside your environment. For narrow, repetitive tasks they are often more model than you need.

Our approach

We fine-tune small language models on your own examples for specific tasks such as classification, extraction, routing and drafting. We benchmark them against larger models on your data, then deploy them in your cloud or on your own servers.

  • Lower cost per task at high volume
  • Data stays inside your environment
  • Accuracy measured on your own data

How one run works

Each step is either fixed logic, a model doing the reading or drafting, or a person on your team. You decide which steps wait for approval.

  • Collect · data pipeline

    Builds a training set from your history

  • Train · training run

    Fine-tunes a small model for the task

  • Benchmark · eval suite

    Compares against larger models

  • Approve · your team

    You sign off before deployment

Private AI models · one run

Illustrative
  1. CollectBuilds a training set from your historyData pipeline
  2. TrainFine-tunes a small model for the taskTraining run
  3. BenchmarkCompares against larger modelsEval suite
  4. ApproveYou sign off before deploymentHuman in the loop
Lower cost per task at high volume

What we build for you

We fine-tune small language models on your own examples for specific tasks such as classification, extraction, routing and drafting.

  • 1

    Training data pipeline

    Your historical examples cleaned, labeled and turned into a training set.

  • 2

    Fine-tuning and benchmarks

    Models trained for your task and compared with larger models on accuracy, speed and cost.

  • 3

    Private deployment

    Served from your cloud account or on-premises hardware, with no data leaving your environment.

  • 4

    Monitoring and retraining

    Quality tracked in production and the model retrained as your data changes.

Related systems in production

Your project starts from architecture we already run.

All systems

Questions about private AI models and on-premise LLMs

Part of these end-to-end blueprints

SLM vs LLM: when is a small model better than a large one?

For narrow, high-volume tasks such as classifying tickets, extracting fields or routing requests. A tuned small model often matches a large model on that one task at a fraction of the cost and latency. We prove it on your data before you commit.

Can the model run on our own servers?

Yes. Small models can be deployed in your cloud account or on-premises, so data never leaves your environment.

What hardware does an on-premise LLM need?

It depends on model size and traffic. A small model with a few billion parameters can serve a typical business workload from a single modern GPU server, while larger open models need several GPUs. We size the hardware during the audit from your expected volume and response-time needs.

Do you build custom LLMs?

Not from scratch. Training a model from the ground up is rarely worth the cost for one company. Custom LLM development for us usually means retrieval over your documents first, or a small open model such as Llama, Mistral or Qwen tuned to one task. That gets most of the benefit for a small share of the effort.

What does an LLM development company do for a business our size?

It picks the model, prepares your data, tunes or connects the model, tests it on your cases and deploys it where your data can stay. For generative AI for business use, the first question is usually whether a hosted model with the right controls is enough. We move to a private or tuned model when volume, privacy or accuracy on a narrow task calls for it, and we show the benchmark first.

How much data do we need?

It depends on the task. Many classification and extraction tasks work well with a few thousand good examples, which we help you assemble from existing records.

Guides on private AI models and on-premise LLMs

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Other solutions

Tell us about the task you want a private model to run.

A 30-minute call with an engineer. You will leave knowing what a system could do for that workflow and what it would take to build.

Book a strategy callor email hello@scenoxstudios.com

What happens next

  1. 1

    Strategy call

    We learn how the workflow runs today and what a good outcome would be.

  2. 2

    Workflow audit

    We map the process and measure where a system would save the most time.

  3. 3

    Proposal

    A written scope, timeline and fixed price for your first system. No obligation to build.