Start a Sensor Strategy Advisory on Vision Versus LiDAR

1. 1

   Frame the fork as a repeatable decision

   Your offer is a structured way to answer one recurring question: should a product use cheap commodity sensors compensated by better AI, or premium high-precision hardware. This fork shows up in HD mapping (cameras versus LiDAR), robotics, weather sensing, and more. Naming it as an analyzable tradeoff, rather than a gut call, is the core of the advisory.

2. 2

   Build the framework from concrete cases

   Anchor your method in real examples: a mapping operator that hit within 10 centimeters using cheap cameras and vision instead of LiDAR, and the parallel satellite-versus-ground-station debate in weather. Distill the factors that decide the fork: accuracy needs, scale, cost, update frequency, and AI maturity. A clear decision framework is what a client buys instead of a vendor's biased pitch.

3. 3

   Assess each client's specific requirements

   The right answer depends entirely on the use case: how much accuracy is truly required, at what cost, at what scale, and how mature the AI is. Run a rigorous requirements analysis for each client rather than pushing a favorite answer. Your value is an objective, case-specific recommendation, which is precisely what a company deciding on expensive hardware cannot get from a sensor vendor.

4. 4

   Stay vendor-neutral to protect trust

   Your credibility rests on independence, so avoid tying yourself to any sensor maker and disclose any relationship plainly. Clients hire you because you have no stake in the answer. That neutrality is your differentiator against both LiDAR sellers and camera-and-AI evangelists, each of whom has a product to push.

5. 5

   Sell scoped engagements to hardware-dependent teams

   Your buyers are AI-hardware-dependent companies at the sensing decision: autonomous systems, robotics, mapping, drones, and industrial sensing. Package a defined engagement (requirements analysis plus a sensor-strategy recommendation and roadmap) at a fixed advisory fee. Because the decision commits large hardware spend, an objective recommendation is worth far more than your hours, so price on that value.

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