Each service is a fixed-scope package with a clear deliverable, so an agency can review and approve it without a long procurement.

AI Readiness and Use-Case Assessment

A review of your data, systems, and workflows, with a ranked list of AI use cases, risk and cost estimates, and a recommended first project. Typical timeline: 2 to 3 weeks.

Secure Document Q&A (RAG) Prototype

A working search and question-answering tool over agency documents, with citations to the source, access controls, and an evaluation report. Typical timeline: 4 to 6 weeks.

Agentic Workflow Pilot

An AI agent that carries out a defined multi-step process, such as intake, triage, and routing, with human approval steps, audit logs, and test results. Typical timeline: 6 to 10 weeks.

MCP Integration

Model Context Protocol servers that let approved AI assistants read from and act on agency systems safely, with permission scoping and logging. Typical timeline: 3 to 5 weeks.

Computer Vision or Machine Learning Model Development

A trained and evaluated model for image detection, classification, forecasting, or text classification, with a documented data pipeline and results. Typical timeline: 5 to 8 weeks.

Predictive Maintenance and Edge AI

A failure-prediction model built on equipment sensor data, tuned to balance false alarms against missed failures, with a plan for running it on the device or at the edge. Typical timeline: 5 to 8 weeks.

Model Deployment and MLOps

Containerized deployment of a model or language model on agency or cloud infrastructure, with an API, monitoring, and a maintenance runbook. Typical timeline: 3 to 6 weeks.

Responsible AI and LLM Security Review

Testing of an existing AI system for prompt injection, data leakage, bias, and output quality, with a findings report and recommended fixes. Typical timeline: 2 to 4 weeks.

Related Engineering Services

Nautilus also provides embedded and IoT development, which pairs with our edge AI work. See the Embedded Development and IOT Development pages for details.