Untangling legacy chaos across Ikon's platform to drive revenue and scale.
At Ikon Technologies, I worked at the intersection of hardware, software, and dealership operations. Ikon builds the tech that helps dealers see their inventory in real time, tracking every car, every key, and every movement, while also powering theft recovery and a consumer add-on product.
Company
Ikon Technologies
Industry
Automotive Retail, IoT
Timeline
December 2024 - October 2025
Throughout my time at Ikon Technologies, alongside my role as a product designer leading 0→1 initiatives, I frequently stepped beyond my core responsibilities, particularly during a major platform refactor where I wore multiple hats to support critical, revenue-impacting work across the business.
During this time, I contributed across product strategy and business intelligence, working closely with engineering and leadership on company-wide initiatives. Rather than using a traditional case study format, I’m presenting a selection of the most impactful projects I led, from modernizing workflows and untangling legacy workflows to improving data trust and helping prepare the platform for scale.
Product Analytics
Defining product success with a HEART-driven framework and aligned teams on measurable outcomes.
Success metrics sheet. Click to expand.
Context
As we prepared for launch, there was no unified agreement on what success meant or which signals mattered most. Sales, ops, leadership, and product each prioritized different metrics — creating misalignment and making it unclear what we should track, monitor, and improve once Toolbox was live. We needed one consistent measurement framework before launch so we could objectively track product health and impact.
My Role & Approach
I collaborated with a Senior PM to define how we measure product success before launching. We aligned on the HEART model (inspired by Google) as the north star for how we evaluate product health. I then mapped every major workflow (mobile + web) into measurable touchpoints in Google Sheets and collaborated with engineering to instrument these events in Heap, Smartlook, and Google Analytics — ensuring we captured both quantitative behavior and qualitative context.
Outcome
This created a consistent, shared foundation for product analytics across teams. We now had one agreed-upon way to measure health, track improvements over time, and make decisions based on signal instead of assumption. This set the groundwork for how Toolbox would grow, scale, and evaluate success going forward.
Service Blueprint
Driving operational impact by establishing the single source of truth behind Ikon’s internal tools ecosystem.
Click here to view end-to-end device lifecycle process map. Password: ikon123
Context
Our internal operations across warehouse, accounting, field ops, and hardware were disconnected across tools, tribal knowledge, and manual workflows — making it hard to scale deployments, align teams, and understand where bottlenecks actually were.
My Role & Approach
I led cross-functional discovery with leadership and operations teams to map the end-to-end lifecycle of a device — from manufacturing to warehouse, to dealership, and ultimately activation. I collaborated with another PM to define the business rules and workflow systems map, along with the data flow across HubSpot → NetSuite → Zoho. This became the single source of truth for how decisions and informations actually moved across the systems.
Outcome
My work gave the organization a single operational source of truth, which the NetSuite consultants used directly to kick off the warehouse management system project, saving billable discovery time and avoiding rework. It also enabled C-Suite and Ops also to update warehouse SOPs based on real workflows instead of assumptions, creating company-wide alignment and operational clarity.
Data Dictionary
Creating a unified data dictionary to power future product expansion and eliminate cross-team ambiguity.
Data dictionary catalogue with internal and external data.
Context
We wanted to extend smarter features for dealers (customer intelligence, richer vehicle context, Smart Marketing, etc.) — but we didn’t actually have a single source of truth on what data we already had, what existed in external integrations, or what each department used day-to-day. Different teams owned different data, had different definitions, and there was no clarity on what data was usable, contractually available, or valuable.
My Role & Approach
I partnered with our data analyst and business intelligence (BI) analyst team to interview every major department in the company to understand data sources, dependencies, pain points, and usage. Together we created a unified data dictionary + data flow map that cataloged internal, first-party, and third-party data, and aligned definitions across teams — making it clear what data exists, what is accessible, and what future features could leverage.
Outcome
This work eliminated data ambiguity, created a shared understanding of our data capabilities, and gave the product team a clear foundation to prioritize which new features we could actually ship next. It also drove meaningful cost reduction — we discovered we were paying for the same data to multiple vendors, and by consolidating sources we reduced our monthly spend by roughly $6,000. Beyond clarity, this directly improved the business model and made future roadmap decisions faster, cleaner, and financially grounded.
I was recognized for my proactivity and was asked to lead our AI/ML agentic outreach initiative.
AI/ML Integration
Optimized service appointment scheduling with agentic outreach and increased booking efficiency.
Agentic outreach for service-scheduling using Stella AI.
Problem
Dealerships need to re-engage customers when their vehicles are due for routine maintenance so they can drive repeat revenue. But this outreach was manual — agents had to call customers one-by-one, explain why service was needed, and then try to book appointments themselves. Without real-time visibility into available time slots, this led to back-and-forth, delays, and a lot of wasted operator time.
My Role & Approach
I worked with a third-party AI conversation partner (Stella AI) to automate this flow end-to-end. The AI identifies who to call, holds the conversation, checks live scheduling availability from the dealer’s system, and books the appointment automatically. Human agents only step in when an exception needs escalation.
Impact (Early)
Early pilots are showing strong promise, with multiple service appointments successfully booked autonomously in the first week. As rollout continues, we expect this to meaningfully reduce manual workload, shorten time-to-book, and improve long-term conversion efficiency.
Mayank always impressed me with his drive to improve not only himself but the people, teams, products, and processes around them. There are few I’ve worked with that have as much dedication to the craft as Mayank has.
Jordan Detota, Director of Product Design
More work, more stories.
