Govini is a defense software company that transforms outdated manual processes into a software-driven strategic advantage for the federal government. Our software-as-a-service (SaaS) platform, Ark, integrates structured and unstructured data across logistics, acquisition, maintenance records, engineering documentation, and supply chain systems — transforming fragmented information into decision-ready intelligence.

At the core of Ark is Ace, Govini’s agentic AI engine. Ace doesn’t just retrieve information — it reasons across it. Ace can autonomously execute multi-step workflows: drafting technical documents, synthesizing maintenance histories, forecasting parts demand, identifying corrosion or reliability trends from historical data, and surfacing anomalies across product lines — all within a secure FedRAMP High and Impact Level 5 SaaS environment.

For aviation logistics organizations like ALC, Ace functions as an always-on subject matter expert: one that can leverage every technical manual, work order, procurement record, and inspection report in your environment and can act on that knowledge at the pace of operations — not the pace of a manual process.

Govini’s customers include all U.S. military services, and multiple defense acquisition programs, in addition to USCG’s own Surface Forces Logistics Center. Our team includes former acquisition professionals, defense logisticians, and engineers who understand the operational realities of depot-level maintenance and the compliance demands of military aviation sustainment.

We are at ALC to demonstrate how a configured AI knowledge layer can reduce administrative burden, improve reliability outcomes, save on cost, and accelerate mission readiness across every division.

Problem Statement:

ALC’s data holds untapped potential — the right AI layer puts it to work.

The problem:

• Maintainers and engineers spend too much time searching for data vs executing repairs and inspections
• Document drafting is a persistent bottleneck — manual and inconsistent across divisions
• Institutional knowledge is concentrated in experienced personnel and lost when they depart
• Predictive capability is underdeveloped — historical data exists in ALC systems but isn’t synthesized for proactive decisions