Ship AI products you can measure and trust.
Product Vector helps product and engineering teams turn AI and ML ideas into shipped features, with clear success metrics, rigorous model evaluation, and agentic workflows that hold up in production.
Startups and mid-size companies that need AI to work in the real world, not just in a demo.
Teams shipping a first AI feature
You have a promising use case and need a product definition, success criteria, and a plan engineering can build against.
Teams moving from pilot to production
The prototype impressed people. Now you need evaluation, monitoring, and release gates so quality holds as usage grows.
Leaders adding agents and GenAI to workflows
You want to automate real work with LLMs and agents, with humans in the loop where it matters and guardrails where it counts.
AI/ML product leadership grounded in the industries where hardware, data, and software meet, with deep experience across automotive, manufacturing, transportation and infrastructure, education, and consumer goods.
Four ways I help AI products get built, measured, and improved.
AI/ML product strategy and roadmapping
Decide where AI creates real value, which use cases to back first, and how to sequence the work against engineering capacity. I define 0→1 AI products and align customer needs, business goals, and technical delivery.
- Use case discovery and prioritization
- Product requirements and success criteria
- Roadmap tied to data, model, and team readiness
Model evaluation frameworks
Know whether a model is good enough to ship, and keep knowing after launch. I design evaluation approaches that connect model quality to user and business outcomes, from test data through acceptance thresholds and retraining loops.
- Evaluation plan with datasets and review workflows
- Acceptance criteria and release gates
- Error analysis and a prioritized improvement backlog
Metrics and performance tracking
Turn vague goals into a metric system your team trusts. I define the metrics that matter for AI products, set up how they are tracked, and build the review rhythm that turns numbers into decisions.
- Metric tree from model quality to business impact
- Instrumentation and dashboard requirements
- Monitoring for quality, drift, adoption, and cost
Agentic and GenAI workflow design
Find the work worth automating and design agent and LLM workflows that people can rely on. I have used generative AI in my own product work to cut cycle times and lift engagement, and I bring that practical lens to your processes.
- Process mapping and automation opportunity review
- Agent and LLM workflow design with human review points
- Pilot plan, guardrails, and success measures
A simple loop that keeps AI work tied to outcomes.
Diagnose
Understand the goal, the users, the data, and where the team is stuck.
Define
Agree on the product scope, the metrics, and what good looks like before building.
Build alongside
Work with your engineers and data scientists on requirements, evals, and launch readiness.
Measure and hand off
Leave behind dashboards, playbooks, and a team that can keep improving without me.
Outcomes from product leadership roles in AI, mobile, and connected products.
Pick the engagement that fits where your team is today.
Advisory sprint
A focused engagement on one question, such as defining an AI product, designing an evaluation approach, or setting up a metric system.
Fractional AI product lead
Embedded product leadership for your AI roadmap. I work with your engineers and data scientists on requirements, evals, and launch decisions.
Workshop or review
A working session or written review for your team, such as an AI readiness assessment, an evaluation gap review, or an agent workflow design session.
Hi, I'm Radhika Cherukuru.
I'm a senior product manager who has spent my career taking technical products from concept to scale across AI/ML, mobile apps, and connected hardware. I led product for a portfolio of 30+ computer vision and LiDAR based AI/ML models in transportation and infrastructure, brought generative AI into everyday product work for connected consumer products, and delivered over-the-air update integration across 390k+ engines in automotive and heavy-duty powertrains.
I have led junior product managers, worked with engineering by influence and by ownership, and learned to define metrics before the first line of code is written. I enjoy the unglamorous parts that make AI products work, like clean evaluation data, honest metrics, and clear launch criteria.
Let's scope your first engagement.
Tell me what you are trying to ship, evaluate, or measure. I will reply with questions and a suggested next step.