Own the intelligence layer, not just the apps.
Deploy your fine-tuned model as private AI on your own infrastructure, alongside open-weight and commercially licensed models, with frontier models reachable over an API. Every call is evaluated and guardrailed.
Any model type. Fine-tuned, evaluated, and governed.
Frontier models converge. Any can be switched off.
Providers deprecate on their own schedule. Private AI keeps your models on your infrastructure, never taken away.
Open-weight, commercial, fine-tuned, or frontier.
Three run inside your environment. Frontier models you reach over an API. Swap any of them without rebuilding.
Smaller, fine-tuned, and yours.
Fine-tune an SLM on your data. It outperforms the frontier on your tasks, at a fraction of the cost.
Every call, evals and guardrails.
PII redaction, policy checks, and prompt-injection defense run on every call, logged and reproducible.
Most agent stacks forget everything and lock you into someone else’s runtime.
Yours won't. Agents keep working memory for the task and long-term recall across sessions, isolated per tenant so nothing leaks between customers. The harness that orchestrates, evaluates, and serves them runs on your infrastructure, not a vendor's hosted black box.
Owned models, in production today.
Teams run their own fine-tuned models in production, on their own hardware. The accuracy and economics prove it.
Pressure test your
AI strategy with us.
Sit with our architects and design an AI strategy you own: private models, fine-tuned to your use cases, on your infrastructure.
Questions, answered.
On your specific, well-defined tasks, yes: a small model fine-tuned on your data routinely outperforms a general frontier model, at a fraction of the cost and latency. On open-ended reasoning or tasks you have little data for, a frontier model is still the better tool, which is why you can reach one over an API whenever you want.
Inside your environment. Weights, fine-tuning data, prompts and inference logs stay on your hardware and never leave. The only exception is a frontier API call you explicitly choose to make.
Open-weight, commercially licensed, and your own fine-tuned models all run inside your environment. Frontier models are reachable over an API when you want them. You can swap any of them without rebuilding your applications.
Fine-tuning happens inside your environment on your data, and that data is never used to train anyone else's model. The resulting weights are yours, and nothing is sent to a third party.
Every model is scored against task-specific eval sets, including the frontier model it replaces, before it ships, and evals keep running on production traffic. A release is gated behind evals and guardrails, so quality is measured, not assumed.
Every request passes input and output guardrails: prompt-injection and PII checks on the way in, policy and format validation on the way out. In regulated industries, guardrails are a control, not a feature, and it's the same evidence a model validation or risk team will ask for.
Usually both. Fine-tuning teaches the model your tasks, format and domain behavior; retrieval keeps it current on facts. kis.ai gives you hybrid search, memory and agents alongside fine-tuning, so you combine them rather than choose.
You own your fine-tuned weights and decide when to retrain or upgrade the base model. No provider can deprecate a model out from under you, and moving to a newer open-weight base does not mean re-architecting your applications.