A competitive-intelligence tool I'm building. The example below is a real, defensible competitive read in insurance telematics. v2 will add live generation against any two B2B companies.
AI does the research scaffolding. The positioning judgment stays human.
The complete competitive playbook: where each side wins and loses, verbatim objection scripts, pricing intel, landmines, and proof.
This is a preliminary v2. Live generation against any two companies (with the four modes wired) is in progress. The hand-validated Arity vs CMT example below shows the depth and structure the full tool produces.
Arity vs. Cambridge Mobile Telematics
Pre-loaded example
Insurance telematics, point-of-quote pricing use cases
Strategic frame
Arity competes on the scale and insurance-readiness of its driving data and its position inside the insurance distribution model. CMT competes on a mature mobile telematics SDK and a polished consumer safe-driving experience. The buyer's real decision is whether they're buying a data partner built around underwriting economics, or a platform built around app engagement and behavior change.
When Arity wins
The buyer needs pricing accuracy at point of quote, not just behavioral scoring after a program enrolls.
The use case is data-led: VMT, segmentation, and risk signals feeding underwriting or transportation analytics, not a consumer-facing app.
The buyer wants to start fast and prove value before committing, where a Marketplace-led motion lowers the barrier to entry.
When Arity loses (be honest)
The buyer's priority is a turnkey, branded consumer safe-driving app with mature engagement mechanics.
The deal is anchored on SDK maturity and proven large-scale program deployments where CMT has a longer reference list.
CMT's genuine strengths
Mobile-first SDK depth and sensor accuracy, refined over many program deployments.
Strong brand in the program-based safe-driving category.
Engagement and gamification mechanics that drive measurable behavior change.
CMT's specific weaknesses
Heavier lift when the use case is pure data and underwriting rather than an app program.
Engagement-led model assumes the carrier wants to run a consumer program, which not every buyer does.
Objection handlers (what to actually say)
"CMT has the stronger consumer app and engagement story."
Agree, then reframe: you're not buying an app, you're buying pricing accuracy and loss-ratio improvement. If the goal is underwriting outcomes, the question is data quality and how it's modeled for insurance, not app engagement. Let's look at what moves your combined ratio.
"Why not just license a telematics SDK and build it ourselves?"
An SDK is a sensor. The value isn't collecting the data, it's what the data is modeled for. Arity's models are built around insurance economics, so you get risk signals tuned for pricing, not generic activity tracking you'd have to interpret yourself.
"CMT is more established in telematics programs."
True in consumer programs. But ask where your value actually sits: if it's in pricing sophistication and data you can act on at quote, that's a different competency than running an engagement program. Match the partner to the job you're hiring them for.
Pricing & commercial intel
Them
Typically program- and deployment-oriented; commercials scale with app program scope and active user base.
Us
Data products accessible via Google Cloud Marketplace lower the entry barrier; buyers can start with a defined data scope and expand, rather than committing to a full program build up front.
Landmines to set (questions to ask the prospect)
"Is your primary goal pricing accuracy at quote, or running a consumer engagement program?" (separates the data buyer from the app buyer)
"How quickly do you need to prove value before a larger commitment?" (surfaces the time-to-value advantage)
"Who owns the outcome internally, underwriting or marketing?" (reveals whether the deal is a data sale or a program sale)
Proof points to keep loaded
VMT data drawn from a large base of active mobile connections, updated daily and aggregated to county level.
PLG motion via Google Cloud Marketplace delivered roughly 50% traffic lift and 2x MQL performance within three months.
First-of-its-kind data availability on a major cloud marketplace in the insurance telematics space.
Built by Robert Lee. The matchup above is a real, defensible competitive read from insurance telematics. Engine is generic so it will work for any B2B product once v2 live generation ships.