Parspec Product Management Intern at Parspec to develop and validate AI quoting agent features. Own end-to-end product definition, prototyping, and pricing analysis for AI quoting workflows.
Responsibilities
What does it take to turn an AI agent into a product customers trust and pay for? That question sits at the heart of this internship.
We are looking for an intern to work alongside Parspec's product leadership on our AI quoting agent, the product at the center of how distributors and rep agencies build quotes. You will own a piece of it end to end, from customer discovery through an engineering-ready product spec, and help shape the business model behind it. Quoting is the beating heart of our customers' business. It is also messy, manual, and full of judgment calls, which is exactly what makes it both a hard and a valuable problem for an AI agent to take on.
This is not a shadowing role. You will talk to real customers, write requirements that engineers build against, and build prototypes. If you see yourself becoming an AI product leader or founding a company someday, this is a chance to work on the two problems that define AI product management right now: making agents reliable and making them commercially viable.
What You Will Achieve and Key Responsibilities
Define what to build
Interview distributor and rep agency users to understand how quotes get built today, step by step, including the workarounds and judgment calls that never show up in a process diagram
Map the quoting workflow end to end and identify where an AI agent delivers the most value, and where human judgment needs to stay in the loop
Turn what you learn into a clear, prioritized view of what the agent should do next, backed by customer evidence rather than intuition
Prototype and validate
Build working prototypes to test agent workflows with real users before engineering commits to a full build
Translate customer needs into product requirements and an engineering-ready spec for your piece of the agent
Define the success metrics that tell us the agent is actually working: accuracy, time saved, trust, and adoption
Qualification
Mandatory QualificationsStrong analytical skillsStructured problem-solving abilityA real ownership mentalityGenuine interest in AI productsExposure to B2B SaaS business modelsInterest in construction
Required
You are curious about how products get built and sold. You like talking to users as much as you like working through data, and you can hold both the customer's reality and the business model in your head at the same time. You write clearly, you ask sharp questions, and you are comfortable forming a view on a problem nobody has fully solved yet. You are not afraid to roll up your sleeves and go deep into a messy workflow to map complex processes and identify how AI can enhance them. You do not need to be an AI engineer, but you need to be genuinely curious about how AI agents work, what makes them fail, and what makes people trust them.
Mandatory Qualifications
Currently pursuing or recently completed a degree (undergraduate or graduate) in Business, Economics, Engineering, Computer Science, Data Analytics, or a related field
Strong analytical skills, including comfort working through usage data, unit economics, and pricing trade-offs
Structured problem-solving ability: you can break an ambiguous question down into an answerable workplan
Excellent written and verbal communication skills, including the ability to run a customer interview and write a spec an engineer can build from
A real ownership mentality: you have taken something from question to shipped result without being managed through it
Genuine interest in AI products: you have used them seriously, formed opinions on what works and what does not, and can articulate why
Preferred
Prior professional experience in product management, consulting, software engineering, or a startup operating role
Hands-on experience building with or evaluating LLMs and AI agents, even on personal projects
Exposure to B2B SaaS business models, pricing and packaging work, or product discovery methods
Interest in construction, building materials, industrial distribution, or vertical SaaS businesses
Experience in fast-paced or early-stage startup environments
