Klaren AI does not ship a different product for every industry. It aims the same products at your problem — with the limits your sector actually has, including where a model is allowed to run. Every solution on this page is coming soon and none is on sale yet; today Klarcode is sold to individual developers.
Your hardwareDeployed on-premise, in your private cloud, or fully air-gapped.
Your modelKlaren AI’s models, your own API key, or a model that never leaves your network.
Your recordEvery agent action recorded and attributed, in a history your compliance team can read.
Seven solutions. Each one starts with a conversation.
These are not self-serve plans. Tell us the problem and the constraint, and we will say plainly whether Klaren AI fits it today.
01
Small Language Model
A compact model trained on your own domain, running on hardware you control.
Most work inside an organisation does not need the largest model available. It needs one that knows your documents, your terminology and your formats, and that answers without the data leaving the building. A small language model is tuned to one organisation's domain and deployed on its own servers or devices, so the answer stays inside your perimeter and the cost of a request is set by your hardware rather than a metered bill.
Tuned on your own documents, code and terminology.
Deployed on-premise, in your private cloud, or fully air-gapped.
Fast and inexpensive to run, because it is sized to the task rather than to everything.
Usable in Klarcode anywhere a local model is accepted.
Agents that do the work, with an audit trail your compliance team can read.
Most agent deployments fail on the same thing: nobody can tell what the agent actually did. Klaren AI runs agents against an append-only record of every action, every tool call, and every credit spent — so delegation is a decision you can review rather than a leap of faith.
Delegate a task to one agent or fan it out across a fleet.
Every action recorded, attributed, and reversible.
Entitlements checked per request, so scope changes take effect immediately.
Spend capped by the same shared balance the rest of your work draws on.
Research, modelling, and client materials — without the model leaving your control.
Analysts spend most of a week proving a number rather than finding it. Klarcode holds the research library and the execution simulator together, so a change to an assumption surfaces every published result that depended on it before it reaches a committee.
Run entirely on a local model when the data cannot leave the building.
Reproducible research: every figure traceable to the code and inputs that made it.
Lookahead and survivorship checks run as tests, not as folklore.
Client-ready output generated from the same source as the analysis.
Reproducibility as a property of the tooling, not of the researcher's discipline.
A result that cannot be re-run is not a result. Klaren AI treats the pipeline, the parameters, and the environment as one versioned object — so a figure in a paper points back to the exact state that produced it, years later.
Pipelines versioned with their data and environment together.
Parameter sweeps fanned out across agents, with only divergences returned.
Figures and tables regenerated from source on demand.
Local and air-gapped models for work that cannot touch a third party.
Move a legacy system forward without losing the behaviour nobody documented.
The hard part of a migration is not the new language — it is the twenty years of undocumented behaviour the old system encodes. Klarcode reads the whole repository, derives what the code actually does, and changes it against tests that prove the behaviour survived.
Whole-repository context, including the parts no one has opened in years.
Characterisation tests generated from current behaviour before anything moves.
Hunk-level review, so a migration is approved in pieces rather than trusted wholesale.
An agent that reads every pull request and only speaks when it matters.
Automated review usually fails by commenting on everything, which teaches everyone to ignore it. Review Agent ranks a change by consequence — auth paths, migrations, money, and anything with a test that failed recently — and stays silent on the rest.
Reviews ordered by blast radius rather than by file path.
Formatting and import noise collapsed into a single approvable row.
Findings link to the reasoning and the failing check, not just the line.
From control code to the line, with the simulation in between.
Changing what a machine does means changing code that moves physical mass. Klaren AI keeps the controller, the simulation harness, and the telemetry in one context, so a change is replayed against real recorded runs before it reaches hardware.
Control logic and its simulation harness held in one context.
Changes replayed against recorded telemetry across many seeds.
Only the runs that disagreed with the simulation come back for a human.
Operator-facing material generated in every language the floor speaks.
Klarcode
OkBoss
Soundklar