$200
or 3 sessions for $500 (save $100)
You've built something impressive in the lab. A novel manipulation algorithm, a perception system that works in simulation, a control approach with great results on a benchmark. Now you need to turn it into something that works in the real world, reliably, at scale.
That transition is where most robotics projects die. I've watched it happen from inside production robotics teams and from advising startups. The gap between a research demo and a shippable product is wider than most people expect, and the mistakes are predictable.
Common situations:
What works in your controlled environment that will break in the real world. Identifying the assumptions baked into your research that production can't afford.
How to evolve from a research codebase to a production system without a full rewrite. What to keep, what to throw away, and what to abstract now vs. later.
Practical approaches to bridging the sim-to-real gap. When simulation is a crutch vs. when it's essential. How to build a testing pipeline that catches failures before deployment.
From prototype hardware to something manufacturable. BOM cost reality checks, supplier selection, reliability engineering, and when to use off-the-shelf vs. custom.
Sequencing the work from demo to MVP to v1.0. What to cut, what to nail, and how to tell the difference. Building a roadmap that investors and customers both find credible.
Research teams and product teams need different skills. How to identify what's missing, whether to hire or upskill, and how to change the culture without losing the people who built the thing.
We'd assess which parts of the research generalize to field conditions, design a data collection strategy for real-world training data, plan the path from a laptop running inference to an edge device on a drone, and identify the first customer segment to target.
We'd build a productization roadmap that investors will find credible, identify the engineering milestones that derisk the deal, figure out what "works in the lab" claims need more evidence, and plan the hires needed to execute.
We'd triage the prototype's technical debt, build a realistic timeline with dependencies, identify the organizational blockers (procurement, compliance, IT) that always take longer than expected, and design the testing strategy that production requires.
Research-to-product transitions don't happen in one conversation. A typical 3-session arc:
All 3 sessions must be used within 2 months.