COMMAND DASHBOARD
Company snapshot: ~50 employees; significant capital raised; Series A; backed by Andreessen Horowitz and Lightspeed Venture Partners; real-time audio AI foundation model for voice-first applications; first wave of GTM leadership being hired to build the revenue engine from scratch.
Research-to-revenue gap: Cartesia has built technically superior audio AI infrastructure — real-time latency benchmarks that competitors cannot match, a state-space model architecture that outperforms transformer-based audio models at scale. But world-class technology does not generate enterprise revenue without a commercial structure to package, price, and sell it.
Buyer communication challenge: Cartesia's buyers are technical and non-technical simultaneously: developers who evaluate on latency/accuracy benchmarks AND product executives who evaluate on integration speed, reliability SLAs, and total cost of ownership. The commercial narrative must work at both layers without alienating either.
Pricing and packaging undefined: API-first AI infrastructure companies face a fundamental commercial design question: per-character, per-minute, per-request, or enterprise license? The wrong pricing model creates misaligned incentives, caps ACV, and produces the wrong customer behavior. Cartesia's pricing architecture is still being defined.
GTM motion ambiguity: Cartesia can pursue developer-led PLG (like Stripe or Twilio), direct enterprise sales (like AWS), or platform partnerships (embedded into voice AI platforms like Bland AI or Retell). Each motion requires a completely different commercial architecture. The choice made in the first 90 days will shape the company's revenue trajectory for 3+ years.

Cartesia's path from research breakthrough to infrastructure company requires someone who can simultaneously architect the pricing model and commercial packaging that captures enterprise value without alienating the developer community, design the sales motion (PLG vs. direct enterprise vs. platform partnerships) from a blank canvas, and operationalize the AI-powered GTM systems that let a 50-person company compete for enterprise budgets against audio AI offerings from Google and AWS. Cartesia's existing commercial infrastructure was built for research partnerships and design partner relationships — not for systematic enterprise revenue generation at scale. Operating without this architecture costs Cartesia an estimated – in addressable enterprise ACV in Year 1 alone — and delays the infrastructure positioning that makes the company defensible against well-resourced incumbents.

Days 1–90Q1 — FOUNDATION
Days 91–180Q2 — BUILD
Days 181–270Q3 — SCALE
Days 271–365Q4 — OPTIMIZE
Conservative

Establishes committed enterprise ARR; 3+ platform partnerships generating revenue-share income; pricing model validated with 10+ enterprise reference customers; commercial infrastructure operational for Series B

Target

Establishes committed enterprise ARR; platform partnership GMV contributing an additional revenue stream bookings; developer-to-enterprise conversion funnel operational with measurable conversion rate

Stretch

Establishes committed ARR (enterprise + platform partnerships combined); Cartesia positioned as the default audio AI infrastructure layer for 3+ of the top 10 voice AI platforms; Series B raised at enterprise valuation

Strategic Summary

Core Opportunity

Cartesia's path from research breakthrough to infrastructure company requires a commercial architecture built from a blank canvas — and the company has neither the pricing model nor the operator to build it before Google and AWS commoditize the audio AI space.

Execution Thesis

Deploy AI-powered developer onboarding, platform partnership infrastructure, and enterprise sales architecture to capture – in committed ARR while establishing Cartesia as the default audio AI infrastructure layer for the voice-first application ecosystem.

Production systems, not theory. Revenue captured, not demos given.