Risk reports arrived in every format a client could produce, and risk engineers read them one at a time. The view of overall exposure was never complete and never current. 

A custom NLP platform that extracts, classifies, and categorizes every report by type as it arrives. The pipeline handles native digital documents and scanned ones alike, which is what lets a single classification pass work across sources that share no structure. Engineers work from one complete view of exposure instead of assembling it by hand, and the hours that went into sorting go back into assessment. 

Every county in America can send a mass notification. Almost none can tell a parent where their child is. Reunification is the primary goal in the hours after a school shooting or a hurricane landfall, and it runs on clipboards and a phone bank. 

KitelyTech is the client's embedded product and engineering organization. The multi-tenant platform it built is live across three agencies in one metropolitan region: county emergency management, a sheriff's office, and a neighboring city. It layers onto the systems agencies already run. Each holds its own tenant, and the hard part is that agencies work one another's incidents without those tenants merging: shared incident, isolated records, separate authority. The reunification workflow includes AI facial recognition and photo matching. When an agency activates it for an incident, the system matches submitted family photos against intake photos, turning a manual scan of hundreds of faces into a ranked shortlist in seconds. It is agency-triggered and scoped to the incident. It never auto-confirms; a credentialed person makes every decision. The platform minimizes what it holds about children and vulnerable people, and logs every action for the audit that follows. Capacity is reserved ahead of the worst realistic incident rather than scaled into it, because a mass-casualty spike is the wrong moment to discover a ceiling. Nothing is metered during a response. 

Dealer and customer email arrived faster than support could sort it. Triage was manual, the context needed to answer lived in systems someone had to open one at a time, and the quality of a reply depended on who picked up the message. 

An AI agent that classifies intent and urgency on arrival, retrieves context from internal systems, and drafts a brand-compliant response. High-risk cases route to a person. High-confidence replies send automatically, and the confidence threshold governing auto-send is set by the business, so the line between drafted and sent moves as trust in the system grows. Every action is logged for audit.