Solution Accelerator Program powered by Condense, for Mobility & Automotive Use Cases

TLDR;

This webinar, presented by Akshay Dixit, AVP and Business Unit Head – Automotive & Mobility at Zeliot, walks through how enterprises are building powerful, real-time solutions without starting from zero. Using Condense Zeliot’s fully managed, Kafka Native real-time data platform alongside the Solution Accelerator Program, customers are launching production-grade, domain-specific applications in record time. 

The session covers use cases from mining, mobility, diagnostics, and logistics each built using Condense vertical ecosystem, GitOps-native transforms, and prebuilt logic modules from the accelerator library. 

Here’s a breakdown of what was covered: 

Watch the Full Webinar Here: Webinar Recording

What is the Solution Accelerator Program? 

The Solution Accelerator is not just a set of tools. It’s an execution model. It combines the Condense platform with Zeliot’s consulting, implementation, and technical expertise to help enterprises move from idea to deployment, fast. 

Akshay explains it simply: “It’s not about throwing a bunch of SDKs at you. It’s about getting you from concept to real-time outcomes.” 

Built on Condense, the program brings: 

• Kafka-native streaming at its core 

• Real-time application orchestration 

• Prebuilt streaming utilities (alerting, windows, joins, enrichment) 

• Git-based logic deployment 

• Deployment within your cloud (BYOC on AWS, Azure, GCP) 

Use Case 1: Connected Mine Site Operations 

Problem

Mining sites span up to 200 km with hundreds of moving assets. Vehicles like dumpers and excavators operate round-the-clock. But operational inefficiencies from trucks idling in queues to poor assignment visibility, lead to productivity losses and wasted fuel. 

What Was Built

Using Condense as the real-time backbone, Zeliot created a driver-assist system that: 

• Tracks vehicle and excavator positions in real-time 

• Pushes dynamic excavator assignment suggestions to the driver’s mobile app 

• Reduces idle time by recommending the most optimal next job 

Key Outcome

Each truck performed 1.2 more trips per shift on average, directly increasing throughput. Fuel usage dropped. And all of this was achieved using live GPS streams processed via Kafka-native Condense pipelines, not batch syncs. 

“Excavator to dumper allocation was optimized using real-time recommendations, built using patented IP,” says Akshay. 

Use Case 2: Real-Time Vehicle Diagnostics via CAN Data 

Problem

For fleets, breakdowns are expensive and avoidable. The goal: monitor engine components and alert before failure, not after. This required real-time access to internal ECUs via the CAN bus. 

What Was Built

Using partner-provided CAN-enabled devices, data was streamed into Condense and processed into: 

• Real-time alerts (e.g., coolant temperature, fuel pressure, low AdBlue) 

• Diagnostic dashboards visualizing component behavior 

• Aggregated fleet health insights (vehicle-wise classification: Good / Attention / Bad) 

Key Outcome

The diagnostic data was no longer locked inside vehicles. It was available in real-time to decision-makers, with alerting logic and visualization powered by Condense transforms and Kafka topics. 

“This wasn’t about trip analytics. This was about component-level intelligence at streaming speed,” explains Akshay. 

Use Case 3: Trip Visibility + Fuel Theft Detection 

Problem

In long-haul logistics, unauthorized halts, fuel theft, and manual tracking hurt fleet performance. Static dashboards don’t cut it. What’s needed is real-time visibility and event-based alerts. 

What Was Built

A complete trip orchestration system powered by Condense: 

• Trip states: loading, en route, halt, refuel, unload 

• Real-time alerts on stoppages outside geofences 

• Dashcam integration to verify refueling and driver behavior 

• Dynamic KPI tracking: idle time, route adherence, alert distribution, etc. 

Key Outcome

Fuel theft cases were proactively flagged, with incidents linked to time, location, and video. Central teams could instantly respond, not investigate later. 

“If a vehicle halts for over 2 minutes in a non-geofenced zone, Condense triggers an alert. That’s how you move from post-mortem to prevention.” 

Use Case 4: Rapid Customization of an Existing FMS 

Problem

A large fleet operator had a basic FMS in place but needed to expand to serve new customers and GTM timelines were tight. 

What Was Built

Using Condense + the accelerator, the FMS was modularly extended: 

• Custom trip dashboard with KPIs like distance, idling, halts, fuel stops 

• Driver behavior scoring integrated with health dashboards 

• Monthly performance analytics for fleet managers 

Key Outcome

Time-to-market was accelerated by 8 months. The customer acknowledged the savings formally, citing Condense Git-based streaming logic as the enabler. 

What Makes This Different? 

Most real-time platforms stop at Kafka provisioning. Condense doesn’t. 

• It runs Kafka, but also your logic, your apps, and your transforms 

• All of this is deployed inside your cloud, not in a vendor’s black box 

• The accelerator program gives you ready-to-customize templates not code snippets 

Zeliot doesn’t just hand over infrastructure. It builds with you using prebuilt stream apps and logic blocks that are domain-aware and production-proven. 

Final Takeaways 

• Real-time is not just about ingest. It’s about converting events to decisions. 

• Kafka Native matters. But managed Kafka alone doesn’t give you real-time outcomes. 

• Condense + Solution Accelerator = Stream-to-outcome in weeks, not quarters. 

Whether it’s a mining dispatch system, diagnostics engine, or FMS enhancement, the architecture remains the same, Kafka-native Condense pipelines, GitOps transform logic, and customer-owned deployment.