Automotive

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. 

Sales and inventory data lived in more than seventy places: internal systems, dealer feeds, supplier reports, logistics providers. Any question that crossed two of them took days, and by the time you had an answer, it was already wrong. 

A data engineering engagement. KitelyTech engineered a new data architecture across the manufacturer's existing systems, unifying more than seventy internal and external sources. Seventy sources meant seventy definitions of the same field, so the work was as much schema mapping and conflict resolution as pipeline construction. Sales, inventory, wholesale analysis, supply chain logistics, and dealer transit time now read from one live view. 

For a premium brand, delivery day is the emotional peak of the purchase, and the manufacturer's legacy system had nothing for the customer living it. It trained staff on preparing vehicles and interacting with buyers, but the buyer's own experience of the wait was silence.

A custom delivery platform that runs from point of purchase through delivery and beyond. Customers get exclusive access to track their vehicle's milestones through production and transit, a delivery day built as an experience, feature education for the car they chose, maintenance expectations, and their documentation in digital form. The wait becomes part of the ownership.

An enterprise automotive imaging application had aged past its architecture, and nobody wanted to replace what could be recovered. 

Reverse engineering and a full audit of the existing code to understand its structure before touching it, then refurbishment for performance and new features to meet modern standards, delivered as a Windows desktop application and hybrid mobile apps for iOS and Android. Inheriting someone else's codebase and improving it is its own discipline, and this is what it looks like.

Dealers needed answers that lived across multiple systems, and getting them meant knowing which system to search and how. 

An AI natural language agent embedded directly in the dealer portal. Dealers ask questions in plain language and get direct answers with the supporting artifacts attached: downloadable PDFs and deep links into the source systems, so the answer arrives with its evidence. 

A vehicle sits at a dealership past its promised date because parts are late or a technician hit something unexpected. Nobody tells the customer, because telling the customer requires somebody to notice first. Across a franchise network, that silence becomes downtime, negative reviews, and eroded trust. 

An AI agent that monitors repair orders and service events in real time, detects promise-time breaches and parts delays, sends templated status notifications to dealers and customers automatically, logs everything to the case, and escalates high-risk situations. It integrates with dealer systems where they exist and provides a fallback portal for smaller shops.