Someone spending premium money on their first electric vehicle arrives at handover with a particular set of questions. How do I charge it at home. What does the app actually do. What happens on a long drive. Someone trading in their third EV arrives with almost none of them, and sitting through a charging explainer they already know is not a premium experience, it is forty minutes of their afternoon.
Dealership consultants had no way to tell these two customers apart before delivery day. Handovers ran to a fixed script of roughly two hours, with time budgets set per topic regardless of who was in the passenger seat, and the supporting checklists lived on paper that nobody could verify afterwards. When appointments overran, feature demonstrations were quietly dropped with no record and no follow-up.
Net Solutions designed and engineered a two-sided platform connecting an adaptive pre-delivery customer survey to a dealer workspace. The survey needs no login and no account: a link arrives by email or text, and a few minutes of slides captures prior EV experience, home charging setup, focus areas and how much time the customer actually has. Those answers feed a time-allocation engine that rescales seven delivery sections independently, so the consultant walks in with a plan shaped around that specific buyer.
Around that core we built a pre-delivery briefing sheet, an interactive checklist with embedded explainer media, an item-level follow-up queue for anything not covered on the day, and a two-tier administrative console for managing staff, schedules and shared assets across multiple locations.
A premium handover is supposed to feel personal. In practice it was the same script, on paper, for everyone.
Time budgets were fixed per topic with no mechanism to adjust for a customer’s prior EV experience or interests. The buyer who needed longer on charging got the same allocation as the one who had been charging at home for three years.
There was no structured way to capture a customer’s familiarity with home charging, app connectivity or their availability ahead of delivery day. Consultants met the customer and their preferences at the same moment.
Handover tasks lived on physical forms, which meant management had no way to confirm that pre-delivery preparation and mandatory safety steps had actually been completed, at any location.
When an appointment ran long, features were skipped. There was no record of what was missed and no mechanism to send the customer the relevant explainer material afterwards, so the gap simply stayed a gap.
There was no central layer for managing staff rosters, role permissions or the distribution of brochures and instructional media across a multi-location network. Each site ran its own way.
The platform had to support English and French natively from the first release, work on unauthenticated customer mobile browsers, and perform reliably on showroom tablets at 1024px landscape. These were fixed constraints, not preferences.
The hard part of this build was not the interface. It was turning a set of informal dealership habits about how long things should take into logic a machine could apply consistently.
Six components, built around one engine that decides where the time goes.
The engine starts from an ideal time budget for each delivery section, then adjusts each one independently based on survey responses. A returning EV owner sees a shortened charging walkthrough, while a first-time buyer keeps close to a full infotainment session. If the customer states a hard time limit, the engine rescales every remaining section proportionally so the total fits, rather than truncating sections in a fixed order and running out at the end.
Customers receive a secure link by email or text and complete the survey in a browser with no registration. Simple sliders capture prior EV experience, home charging setup and focus priorities, and a live time budget indicator shows the customer when their answers are pushing the appointment past the time they said they had. The trade-off is visible while they are making it.
Every completed survey generates a structured briefing for the dealership covering the customer’s current vehicle, daily commute pattern, focus areas and self-reported comfort with EVs and connected services. Consultants use it to prepare both the vehicle and the parts of the walkthrough that matter to that particular buyer.
The delivery checklist covers administration, exterior, interior, charging, infotainment, connected services and final sign-off, each carrying its personalised time allocation. Any checklist item can hold an inline image, video or document, so a consultant can play a short explainer clip at the car rather than working from memory.
When time runs short on a section, any remaining item moves to a customer follow-up queue in a single action instead of being dropped. Staff review the queue afterwards and send an email pre-populated with the exact clips, guides and documents attached to the items that were missed, so the customer still receives what the appointment could not fit.
We built a two-tier administrative portal with day, week and month calendar views of scheduled deliveries. Local dealership admins manage their own staff records and daily schedules, while a central super admin layer distributes shared brochures, images and instructional videos across the wider network, so every location works from the same current material.
Rather than every customer receiving the same two-hour walkthrough regardless of experience, delivery time redistributes automatically across sections based on real survey input, which is what a premium handover was supposed to feel like in the first place.
Dropped feature demonstrations are replaced by a reviewable follow-up queue and pre-populated explainer emails, so running out of time no longer means the customer never learns that feature exists.
A digitised, mandatory field workflow replaces paper checklists, giving the client a reliable way to confirm that delivery expectations are met the same way at every location instead of depending on individual staff habits.
Survey and checklist structures were built as reusable templates rather than hardcoded to one vehicle, so additional models or new survey types do not require rebuilding the engine. The architecture was planned for later integration with enterprise systems including the client’s authentication directory, CRM and CMS, and for a rule engine allowing non-technical staff to configure new surveys and checklists without a development cycle.
Every screen, customer-facing and staff-facing, supports English and French from day one, validated on dealership tablets at 1024px landscape so it worked on real showroom hardware rather than in a design file.
The centre of this platform is a deterministic engine that makes a defensible decision about where every minute of an appointment goes. Building that meant modelling how experienced consultants already made those judgements, then writing it down precisely enough to be executed the same way every time.
Showroom tablets, gloved or hurried taps, media playing next to a car. Validating on the actual hardware at the actual viewport is how you find the problems that a desktop preview hides until the pilot.
Template-driven surveys and checklists, plus an architecture planned for CRM, CMS and identity integration, mean the MVP is a foundation rather than a one-off. The second model line costs a fraction of the first.
Paper-heavy customer onboarding does not become digital by being put on a screen. It becomes better when the software takes over the parts people were doing badly by hand: tracking what was covered, standardising what is mandatory, and following up on what was missed.
A customer onboarding journey that treats every buyer identically is the easiest premium experience to improve, because the information needed to personalise it usually already exists somewhere. The engineering question is how to capture it before the appointment and act on it during.
It is dealership software that digitises and personalises the vehicle handover. Instead of a fixed walkthrough script, it captures the customer’s experience level and priorities before delivery day, then applies time-allocation logic to tailor the handover schedule to what that particular buyer needs.
Paper makes it impossible for management to verify that pre-delivery preparation and mandatory steps were completed. A digital workflow standardises handovers across a multi-location network, embeds explainer media for staff to use at the vehicle, and records completed steps in one console.
It begins with an ideal time budget per delivery section. Specific survey answers trigger percentage adjustments to specific sections, so an experienced EV owner might see a shortened charging walkthrough. If the customer sets a hard time limit, every section is rescaled proportionally to fit rather than the last sections being cut.
No. A secure link arrives by email or text and the survey runs in the browser with no registration. Identity is confirmed against the existing delivery record, and it takes a few minutes on any device.
Any item not covered moves to a follow-up queue in one click. Staff then send an email pre-populated with the explainer videos, images and documents attached to those specific items, so the information reaches the customer even though the appointment did not cover it.
Through a two-tier structure. Local admins manage staff profiles, schedules and customer records for their own site, while a central super admin controls shared templates, brochures and instructional video distribution across the network, so updates reach every location at once.
Yes, if surveys and checklists are built as templates rather than hardcoded content. Adding a model line then means configuring new sections, time budgets and media rather than re-engineering the allocation logic underneath.
By treating bilingual support as an architectural requirement from the first release rather than a translation pass at the end. Content structures, media assets and layout all have to accommodate both languages, since text expansion alone will break a tablet layout designed around one.
It depends on how much of the personalisation logic has to be formalised before anything can be built, since that is usually the slowest part rather than the interface. A pilot-first approach shortens the path: prove the engine and the two core workflows with a handful of locations, then extend to the network and to additional models.
Whether you are digitising paper checklists, building personalisation logic, or planning a platform that has to work the same way across every location, there is usually an engineering answer. Tell us what the bottleneck looks like and we will show you how we would approach it.
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