enSmaller is model-agnostic. It connects to any language model through standard APIs - hosted, private cloud, or on-premise. Your data stays where you need it to stay. The governance layer behaves consistently, regardless of which model you use.
Connect to models provided by OpenAI, Anthropic, Google, or others via standard APIs.
Run models in your own cloud account - Azure, AWS, or other providers. Full control over where your data is processed.
Run models on your own infrastructure, inside your own network, for maximum control and data isolation.
The model can change. The results don't. enSmaller defines what a good answer needs to include, filters the evidence, checks for sufficiency, and verifies the output - using the same approach regardless of the underlying model.
Outputs are checked against the same requirements - whether they come from GPT, Claude, Gemini, Llama, or another model.
Each query produces a detailed record of what was required, what evidence was used, and how the answer was evaluated - consistent across models.
Token usage is governed at the evidence level, not the model level. Less irrelevant content goes in - so usage stays lower regardless of provider.
When evidence is insufficient, the system surfaces the gap instead of letting the model guess - with consistent behaviour across models.
enSmaller connects to language models through standard APIs - not web chat interfaces. It sends and receives data programmatically, with control over what reaches the model and what comes back.
Data is only shared with external models where explicitly configured. All communications are encrypted in transit, and model interactions are logged with traceable records.
Role-based access controls, audit logging, and encrypted communication are standard parts of the platform.
enSmaller does not replace the language model or attempt to reason in its place. It constrains what the model sees and checks what it produces - but generation still depends on the underlying model.
enSmaller only allows answers when there is sufficient evidence to support them. If that evidence isn’t there, it surfaces the gap instead of letting the model guess.
enSmaller does not require a fully structured knowledge graph or perfectly curated dataset. It works with existing documents and data, applying governance at runtime.
enSmaller does not summarise output quality with a single score. It exposes where evidence supports the answer - and where it does not - so users can make their own judgement.
We'll connect to your existing models and data during a short paid pilot. Same AI you already use - with enSmaller governing the evidence. You'll see what changes in output quality, cost, and traceability.
Get in touch