Company details

HEADCOUNT

1-24

ORG TYPE

SECTOR

Technology & Digital

WEBSITE

https://www.supersonik.ai

About the company

Supersonik builds an AI agent that can run live, interactive product demos for B2B SaaS, joining video calls and navigating real software in real time. Supersonik positions the product as “always-on demos”, including multilingual support and personalization using customer context like docs and CRMs. Supersonik publicly announced a $5M seed round led by Andreessen Horowitz in September 2025 and markets the company as operating across Barcelona and San Francisco. Current hiring is focused on founding-level engineering and a customer-facing implementation role.

Locations and presence

Supersonik describes the team as based in Barcelona and San Francisco, with engineering roles advertised as on-site in Barcelona. Some public company profiles disagree on team size (ranging from “2–10” to “11–50”), which limits confidence on the exact band.

Palpable Score

41.0

/ 100

Supersonik looks like a high-intensity early-stage team with meaningful equity upside and real technical scope, but most roles are not realistically accessible for new grads. The score is mainly held back by a lack of visible early-career roles and almost no public proof of junior progression or retention yet.

Pillar 1: Early-career access

Score

4.0

/ 20

The company advertises engineering roles that explicitly ask for senior experience, including a Software Engineer post set at “5+ years of experience” in Barcelona.
Supersonik frames engineering hiring as “founding” and “from day one” architecture ownership, which is typically out of reach for 0–3 year candidates.
The company does not show internships, apprenticeships, “Junior/Associate” titles, or a repeat entry-level funnel on the public careers surface.

Pillar 2: Hiring fairness and transparency

Score

12.0

/ 20

The company describes responsibilities and stack clearly in at least one engineering posting (FastAPI/Asyncio, event-driven low-latency backend, Terraform/AWS), which helps candidates self-select.
Supersonik’s public careers experience is hard to evaluate because the primary ATS pages are not consistently readable outside the vendor site, limiting transparency for candidates.
The company does not publish salary ranges and mostly uses broad phrases like “above-average salary” and “significant equity,” which leaves candidates guessing on pay specifics.

Pillar 3: Learning and support

Score

9.0

/ 20

The company promises “fast learning cycles” and high ownership, which can help strong mid-level hires grow quickly, but this is not the same as structured support for juniors.
Supersonik’s engineering role description emphasizes performance work (milliseconds matter, observability, resilience) but does not spell out onboarding, pairing, or mentoring expectations.
The company does not publicly describe manager 1:1s, reviews, a learning budget, or a defined progression framework that early-career hires can rely on.

Pillar 4: Pay fairness and stability

Score

11.0

/ 20

The company offers equity explicitly (“significant equity” in an a16z-backed company) alongside full-time roles, which is a stability signal versus repeated short contracts.
Supersonik talks about relocation support in public hiring posts for Barcelona roles, which reduces barrier for candidates who would need to move.
The company does not disclose salary bands or benefits detail (healthcare, pension, paid leave), so pay fairness and stability can’t be verified from the main job pages.

Pillar 5: Early-career outcomes

Score

5.0

/ 20

The company is very early, and there is no meaningful public track record yet showing junior promotions, time-to-progression, or 12–24 month retention patterns.
Supersonik has credible business momentum signals (seed funding and product launch coverage), but these do not substitute for employee outcomes like growth, manager quality, and retention.
The company’s LinkedIn footprint and third-party company profiles are currently too thin to evidence repeat early-career hiring or visible internal mobility patterns.