PanTech combines marketing expertise, AI leverage, and delivery execution to keep clients in front of the right audience and move that attention toward revenue.
From market-facing platforms to automation-heavy ops work, these services are designed to create visibility, improve follow-up, and support commercial growth.
Custom software, landing experiences, and workflow layers built to help clients get discovered and convert more attention into revenue.
// Real-time data sync across 50+ microservices
const syncPipeline = async (events) => {
return Promise.all(
events.map(async (event) => {
await kafka.produce('user-events', event);
await elastic.index(event);
return event.id;
})
);
};Mobile experiences that keep brands accessible, memorable, and easier to engage with wherever buyers spend time.
// Cross-platform push notifications
PushNotification.localNotification({
title: 'Order Shipped!',
message: 'Your package is on its way.',
bigText: trackingData.summary,
playSound: true,
});Infrastructure and deployment systems that keep growth-facing experiences fast, stable, and ready to scale under pressure.
resource "aws_ecs_cluster" "PanTech" {
name = "PanTech-prod-cluster"
setting {
name = "containerInsights"
value = "enabled"
}
}Modern web platforms designed to improve discovery, sharpen buyer journeys, and support the systems behind lead flow.
// Server component with real-time updates
export default async function Dashboard() {
const data = await fetchUserAnalytics();
return (
<div className="grid gap-4">
<AnalyticsCard data={data} />
<PerformanceChart metrics={data.metrics} />
</div>
);
}Integrations that connect marketing, sales, and operational systems so visibility and follow-up do not break between teams.
// Unified API gateway with service mesh
router.post('/api/data', async (req, res) => {
const legacy = await legacyService.query(req.body);
const modern = await modernDB.transform(legacy);
cache.set(req.id, modern, '1h');
res.json(modern);
});AI systems and agent workflows that help clients respond faster, scale intelligently, and stay more present in the market.
// TensorFlow model pipeline
const model = tf.sequential({
layers: [
tf.layers.dense({units: 128, activation: 'relu'}),
tf.layers.dropout({rate: 0.2}),
tf.layers.dense({units: 1, activation: 'sigmoid'})
]
});