PE Management

The brief was integration, not another siloed tool. Leads came in from several sources, sales were tracked elsewhere, and policy administration happened separately again. The cost of that fragmentation was invisible until you tried to answer a simple question — how many of this week's leads have actually been worked, and how many turned into sales — and found the data lived in three different places.

We modelled the whole journey as one pipeline. A lead enters, gets qualified, is scheduled for a callback, becomes a sale, and moves into processing — and every one of those stages lives in the same system, scoped per product. Because the products (Benefactor.Life and Karri Cover) have different data and volumes, the platform keeps separate lead and sales pipelines for each while presenting a unified view on top.

PE Leads is one branded instance of a larger platform we built to run this way for more than one business. It's multi-tenant and white-labelled underneath, so the same core can carry a different brand and product set per tenant, with a full audit trail behind every action — the three pillars that make it safe to grow beyond a single client.

Outbound sales runs on timing. The platform includes a callback scheduler built around a 06:00–20:00 working window, laid out as a rolling week so an agent can see at a glance what's due today and what's coming up. Callbacks are tied to the lead record, so scheduling one doesn't mean re-keying details into a separate calendar.

The scheduler is deliberately low-friction. The point is to keep agents moving through their list rather than administering it, so the day view surfaces what needs a call without burying it under configuration.

The dashboard leads with the numbers that drive a sales day: leads created today, month-to-date volume, how many are still unprocessed, and how many have been processed as a percentage. Underneath that, the analytics break leads down by product, by status, and by qualified versus unqualified, with a daily time series and a compare-periods toggle for spotting trends.

We built the charts with ApexCharts and made every view exportable to CSV and PNG, because the people running the team need to pull numbers into their own reporting without asking a developer. The date-range and view controls let a user look at their own data or a wider team picture from the same screen.

The dashboard updates live rather than on a manual refresh, so the KPIs reflect what's happening on the floor as it happens. We used Laravel Echo with Pusher to push changes to the client, which keeps the numbers honest during a busy trading period.

Access is scoped by role. A 'My Data' view keeps an agent focused on their own pipeline, while supervisors and support can impersonate an agent's account to see exactly what that person sees — invaluable for troubleshooting and coaching without asking someone to screenshare mid-shift.

Distribution in financial services rarely stays with a single brand, so we built the platform multi-tenant from day one. One codebase and one deployment serve isolated tenants, each with its own data, users, products, and settings. Onboarding a new brand or partner isn't a fork of the application — it's a new tenant provisioned against the same core, which is what keeps the system economical to run as it grows.

White-labelling is dynamic rather than compiled in. Each tenant supplies its own identity — name, logo, colours, domain — and the interface resolves that at runtime, so every tenant sees their own brand instead of ours or anyone else's. 'PE Leads' is one branded surface over the underlying platform; the same system can present an entirely different face to another tenant without a separate build or redeploy.

Tenant isolation is enforced at the data layer, not just hidden in the UI. One tenant can never read another's leads, sales, or agents, and that hard boundary is exactly what makes it safe to run competing brands side by side on shared infrastructure.

In a regulated space like life and funeral cover, 'the system says so' isn't enough; you have to be able to show who changed what and when. Meaningful actions across the pipeline — lead status changes, sale updates, callbacks, and administrative changes — are recorded with the user and a timestamp, so the history of a record can be reconstructed rather than guessed at.

Impersonation is part of this story rather than a gap in it. When a supervisor or support user acts as an agent, the audit trail captures both identities, so an action taken on someone's behalf stays attributable to the person who actually performed it. The record stays honest even when someone is troubleshooting from inside another user's account.

Because every entry is tenant-scoped and time-stamped, the same trail serves both the everyday — coaching an agent, settling a dispute over what was said or done — and the occasional after-the-fact reconstruction that compliance and reporting demand.

The backend is a Laravel application serving a Vue 3 single-page frontend built with Vite. That split lets the interface stay fast and app-like — navigating between leads, sales, and dashboards without full page reloads — while Laravel handles the data, auth, and the heavier pipeline logic behind a clean API.

This is a working internal system carrying real volume: over fourteen thousand leads and thousands of sales across the two product lines. The stack was chosen for maintainability as much as performance — it's a system PE Management's team relies on every trading day, so it had to be something that stays dependable and is straightforward to extend as their process evolves.

The real measure of this platform isn't the feature list — it's that a question which used to take a morning of cross-referencing three tools now takes a glance at one screen. Where a lead sat, whether it had been worked, and whether it turned into a sale became a single live answer instead of a reconciliation exercise. That collapse of three systems into one is what the team feels day to day: agents work their list instead of hunting for it, and supervisors see the floor in real time rather than waiting on an end-of-day export.

The gains compound where the old setup used to leak. Leads stop falling through the gap between capture and callback, because the same record carries a lead from intake to a scheduled call; sales are attributed to the agent and product that earned them, because the pipeline is scoped that way from the start; and nothing is re-keyed between stages, which is where both time and accuracy used to be lost. The significant impact on internal processes and productivity that Philip Kotze describes is exactly that — reclaimed time and restored visibility across the whole pipeline, not a number that lived in a spreadsheet somewhere.