OpenAI's models are among the most capable general-purpose language models available. For an agency, the model is the easy part. The work is getting it into a real system safely: on data the agency owns, inside the boundary its authorization covers, with results someone can check.
How we use it
- Through an authorized deployment. Where the work needs a government cloud, we use OpenAI models through the deployment the agency's authorization allows, such as Azure OpenAI in Azure Government. Where it does not, we use the OpenAI API under the agency's own account and data terms.
- Behind a defined interface. The model sits behind an interface we write down: what it receives, what it returns, how it signals low confidence, what happens when it is unavailable. That keeps it testable and replaceable, the same modular discipline we apply to every other part of a system.
- On your data, grounded. Answers are grounded in the agency's own records through retrieval, with sources cited back to the record, rather than left to the model's general knowledge.
- Evaluated before it ships. We build an evaluation set from the agency's real cases and measure the model against it before deployment and after every change.
- Logged end to end. Every prompt, retrieved source and output is logged, so a reviewer can see why the system said what it said.
Where it fits
- Drafting and summarizing case records, reports and correspondence for staff review.
- Classifying and routing incoming requests and documents.
- Search across policy, procedure and program records in plain language.
- Assistants for staff that answer from approved sources, with a person in the loop for decisions.
Getting started
Most engagements start with a readiness review: which use case has a real payoff, what data it needs, what the authorization allows, and how you will know it works. Then a scoped pilot, then production.
OpenAI is a trademark of OpenAI. Vonar AI is not affiliated with or endorsed by OpenAI.