UniQ AI

UniQ AI serves large open-weight model families from your own racks, quantized or at full precision to fit the node. Open-weight models differ from API models in one structural way: the weights are files you hold, not a service you call. An API model can change behavior overnight because the provider updated it. An open-weight deployment changes only when you load new weights. That determinism matters for agency work, where a prompt that produced a defensible answer in March should produce the same answer in June.

Retrieval-augmented generation (RAG) connects the models to your own document stores: instead of answering from training data alone, the system retrieves relevant passages from your repositories and grounds its answers in them, citing back to the source documents. The context window is the amount of text a model can consider at once, and it is sized against your real documents so a full case file or technical order fits in a single pass. Model updates are deliberate: the agency chooses when weights change, a property cloud APIs can't offer.

The compliance story is where it runs. UniQ AI operates inside the agency's own network boundary, under the security controls the agency already operates. Those include CJIS Security Policy-aligned handling for criminal-justice information (local processing, agency-managed access control and audit) and NIST SP 800-53 / 800-171-aligned control implementations. It supports air-gapped operation for classified-adjacent or disconnected sites.

That architecture is what simplifies the CJIS and NIST conversation. Cloud AI asks an agency to extend its boundary around a vendor's infrastructure, which brings new data flows, new agreements and new assessment scope. An on-premises platform introduces no external boundary at all: prompts, embeddings and outputs stay on hardware inside the enclave, so the system inherits the customer's existing authorization path rather than creating a new one to accredit. UniQ AI claims no certification of its own. Compliance attaches to your deployed system, and the platform is built so that attachment is practical.

UniQ AI hardware is sized from the agency's real concurrency and model targets rather than a one-size SKU. GPU inference nodes on AMD and Intel platforms across Dell and HPE server lines, Vertiv power and cooling, and Cisco or Juniper switching for the AI enclave all go on one bill of materials.

The sizing math is concrete. Model size determines VRAM: a model's parameters must fit in GPU memory at the chosen precision, so the model target sets the GPU class. Tokens per second determine responsiveness under load, and concurrent users determine how many requests the node must serve at once. Ten simultaneous analysts need materially different hardware than two. We work those numbers before quoting, then deliver through the same staging discipline as every Uniqcli order: configured, labeled and documented.

UniQ AI is built for the missions of federal investigative agencies: the NCIS-style caseload of interviews, reports and evidence summaries; postal inspection and mail-fraud analysis; Air Force and other service units working maintenance logs, tech orders and readiness reporting; state and local law enforcement clearing records requests and report-writing backlogs.

Each of those is a platform workflow, not a promise: retrieval and summarization across case files and interview transcripts; translation and triage of foreign-language documents without a byte leaving the enclave; records, FOIA and report-writing acceleration grounded in the agency's own repositories; and maintenance-log and technical-order search for flightline and depot teams.