AI Product • Enterprise Software • Automation

Complex systems.
Clear products.
Useful AI.

Kunal Mahurkar
Kunal Mahurkar Enterprise software → AI-first product leadership

I’m Kunal Mahurkar. For 9+ years I’ve worked where customers, enterprise software and engineering meet — turning difficult workflows and technical constraints into solutions people can actually use. Now I’m bringing that experience into AI-first product leadership.

Hiring manager? Start here →Explore my work ↓ Resume ↓ LinkedIn ↗
9+ years enterprise softwareAI workflow automationERP / MES APIs & integrationsCross-functional deliveryRAG / LLM experiments AI-first product management
00 — 30-second view

Why Kunal, in three signals.

Tip: recruiters can open a role-specific view using ?role=ai-product, ?role=technical-product, ?role=product-ops or ?role=customer-product.

01

Enterprise depth

9+ years inside complex customer, platform, ERP/MES, API and integration environments.

02

Product judgment

I frame problems, align stakeholders, manage trade-offs, validate releases and learn from user feedback.

03

Practical AI

I apply AI to real knowledge and workflow friction: documentation, automation, RAG and operations concepts.

Leadership case →AI case →Download résumé ↓Why hire Kunal? →
01 — About

I bridge users, systems and execution.

My product experience was built inside complex enterprise delivery: discovering the real problem, aligning stakeholders, shaping workflows, coordinating technical teams, validating releases and learning from users after launch.

9+Years in enterprise software
5Environments in a major upgrade
13Weeks for the upgrade program
AI × ERPWhere I’m building next
01

Start with the real problem.

I get close to users and workflows, separate symptoms from root causes, and make the problem concrete before deciding what to build.

02

Create shared context.

Customers, engineering, R&D and operations move faster when goals, constraints, ownership and risks are visible to everyone.

03

Use AI where it earns its place.

I look for measurable reduction in manual effort, ambiguity or knowledge friction — not AI for the sake of an AI label.

02 — Selected work

Proof through problems solved.

The first three are flagship case studies with deeper visual storytelling. The remaining projects show range across AI, automation and enterprise operations. Sensitive customer details remain intentionally abstracted.

Program ownership01
Flagship case

Enterprise Platform Upgrade

Coordinated a major 3DEXPERIENCE upgrade across five environments in a 13-week window with customer, engineering and R&D dependencies.

ReleaseRiskStakeholders
Challenge: technical and organizational dependencies could block readiness.

Approach: shared milestones, clear ownership, DR feasibility and coordinated testing.

Product lens: readiness, risk reduction and predictable go-live.
Read full case study →
Frontline UX02
Flagship case

Warehouse Scanner Experience

Translated warehouse-user feedback into usability and responsive-design improvements for an industrial handheld scanner workflow.

VueNode.jsUser feedback
User: warehouse operators on industrial handheld devices.

Constraints: speed, small screens and scan reliability.

Success lens: task time, errors and workflow friction.
Read full case study →
Developer experience03
Flagship case

AI-Assisted API Documentation

Built tooling to transform API and Postman information into structured OpenAPI/Swagger documentation, reducing integration knowledge friction.

OpenAPISwaggerAI
User: consultants and developers consuming APIs.

Problem: large interfaces take time to understand and document.

Success lens: documentation time, completeness and onboarding speed.
Read full case study →
AI automation04

Quote Creation Workflow

Designed an n8n-driven concept that turns incoming request data into structured ERP quote creation while retaining human review at critical points.

n8nERPWorkflow
Problem: repetitive quote-entry effort.

Approach: distinguish deterministic steps from AI-assisted steps and preserve review where errors matter.

Success lens: cycle time, manual touches and correction rate.
Enterprise AI05

Private RAG & LLM Experiments

Explored local models, embeddings and retrieval patterns for enterprise settings where privacy and controlled data access matter.

RAGOllamaLLMs
Question: when should enterprise knowledge stay local?

Approach: test retrieval + generation before committing to a product direction.

Trade-off: quality and speed versus privacy, cost and control.
AI operations06

Incident Intelligence Concept

Defined manager and worker experiences combining operational data, historical incidents and AI suggestions rather than forcing every role into the same dashboard.

AgentsMCP conceptsOps UX
Insight: different roles need different decisions, not simply more data.

AI role: surface context and suggestions while keeping responsibility visible.

Success lens: triage time, resolution time and reporting effort.
03 — How I work

From messy problem to useful product.

01

Understand

Users, jobs-to-be-done, pain, constraints and business context.

02

Frame

Turn requests into problem statements, outcomes, assumptions and risks.

03

Prioritize

Choose the smallest change that can create meaningful value.

04

Prototype

Use flows, POCs and quick interfaces to make ideas testable.

05

Deliver

Align contributors, manage risks, validate UAT and release readiness.

06

Learn

Measure adoption, time, errors and decision quality; then iterate.

Technology becomes valuable when people trust it, understand it and move faster because of it.

04 — Journey

Deep enterprise experience. Product leadership next.

2017 → Present

Dassault Systèmes Global Services

Enterprise consulting across platforms, integrations, upgrades, APIs, deployment, testing and customer delivery.

2025 → 2026

AI & automation expansion

AI-assisted API documentation, workflow automation, RAG/LLM experiments, AI operations concepts and reusable integration approaches.

2026 → Present

AI-first Product Management

Strengthening discovery, prioritization, experimentation, metrics, strategy and AI-native product decision-making.

05 — Capabilities

Product × AI × enterprise delivery.

Product

Discovery, problem framing, user stories, prioritization, UAT, release readiness and feedback loops.

AI & Automation

LLMs, RAG, n8n, AI workflows, agent/MCP concepts and AI-assisted documentation.

Technical Fluency

REST, GraphQL, OpenAPI, SQL, Vue, Node.js, .NET, ERP/MES and enterprise platforms.

Leadership

Cross-functional delivery, customer communication, stakeholder alignment, risk management and execution ownership.

06 — Now

Building the next layer.

Currently sharpening

AI-first Product Management

Applying structured discovery, experimentation, prioritization, metrics and AI product strategy to real enterprise problems.

Portfolio direction

More proof. Fewer claims.

Next: deeper case studies, PRDs, product teardowns, AI evaluations, journey maps and measurable experiments.

07 — Role fit

Different role. Same core advantage.

Choose a role to see the part of my background I would lead with.

AI Product

Enterprise problem context + enough technical fluency to work with LLM/RAG/automation concepts + a bias toward measurable workflow value instead of AI theatre.

Best proof: AI-Assisted API Documentation • Private RAG/LLM experiments • Incident Intelligence concept
08 — Product artifacts

How I make thinking visible.

These are anonymized portfolio representations of the tools I use to turn ambiguity into shared decisions.

Problem brief

Problem → user → constraint → outcome

A one-page framing format that stops teams jumping from a request directly into implementation.

User Who experiences the friction?
Signal What evidence do we have?
Outcome What should improve?
Decision matrix

Value × risk × effort

A lightweight prioritization view for comparing workflow changes when enterprise dependencies make “just build it” expensive.

AI evaluation

Where should AI be trusted?

Separate deterministic steps, AI-assisted steps and human approval points before automating a critical workflow.

Deterministic ✓
AI assist ◇
Human decision ◎
Release readiness

One operating picture

Dependencies, owners, validation evidence and unresolved risks made visible before a release decision.

Environment ✓   Testing ✓
Recovery ✓   Owners ✓
09 — Recognition

Trusted with difficult work.

APPLAUSE • H2 2025

Major upgrade delivery

Recognition connected to the 3DEXPERIENCE upgrade program.

3DS Excellence

Nomination

Internal recognition associated with enterprise delivery contribution.

Cross-functional ownership

Customer × Engineering × R&D

Repeatedly trusted to connect technical execution with stakeholder readiness.

10 — 60-second intro

Meet the person behind the projects.

▶

Video-ready introduction

“I’m Kunal. I’ve spent 9+ years solving enterprise software problems where customers, engineering and complex systems meet. What excites me now is using that experience to build AI-enabled products that remove real workflow friction — not AI for the label, but AI that makes people faster and decisions clearer.”

Record this as a 45–60 second Loom/phone video. Once you have the video URL, replace this card with the embedded video.

11 — Contact

Let’s make complex technology easier to use.

I’m interested in AI Product, Technical Product, Product Operations and customer-facing product roles where enterprise depth matters.

Connect on LinkedIn ↗ GitHub ↗ Resume ↓