The studio you graduate to after the v0 / Cursor MVP.
Production hardening, evals, and scale for AI-native startups that have shipped the MVP and need it to survive real users, real traffic, and real procurement reviews. Stanford-engineered, founder-led, NASA / Sequoia / Stanford-credentialed.
NASA · Sequoia · Stanford
Credentials investors recognize
We've worked with the Stanford-network firms and the NASA Artemis program; founders care because their investors care.
18 months
Operating production AI
Casetrack ran in production for real attorneys handling real matters. We know what breaks at scale.
8 weeks
Typical production-hardening engagement
Fixed-price, founder-led, with knowledge transfer built into every SOW.
Why hire AppRocket instead of building in-house or using a vibe-coding shop?
The answer depends on where you are in the company's life. Pre-product, hire a vibe-coding shop or build in-house with v0 / Cursor / Lovable — we will not be your fastest path to a demo. Post-product, when the MVP works and the next milestone is production hardening, enterprise procurement, or a Series A technical due diligence, AppRocket pairs Stanford-engineered eval discipline with senior product engineering at a price band that does not require a full engineering hire. Investor-recognizable founder credentials (NASA Artemis subcontractor, Sequoia InSITE Fellow, Savaree → Careem exit) and the operational experience of having shipped Casetrack to real attorneys mean the room takes the work seriously. Several of our deployments help portfolio companies clear Series A / B technical DD; that is the modal use case.
How we work with startups
Two engagement shapes, both founder-led, both fixed-price.
AI MVP Studio
For pre-seed and seed-stage teams that need a real MVP, not another v0-clone. 4-week founder-led sprint, fixed-price, shipped-to-production by week four.
How we ship MVPsProduction Hardening
For Seed and Series A teams whose MVP works but needs to survive enterprise procurement, Series-A technical DD, or a 50x traffic spike. The eval framework, the MLOps stack, the on-call rotation.
How we harden productionHow we compare
| Dimension | AppRocket | Vibe-coding shop | Big-4 lite | In-house only |
|---|---|---|---|---|
| Engineering team | Stanford MS&E founder + senior practice | Bootcamp-trained, fast and cheap | Tiered, recent hires | Whoever you can afford |
| Eval discipline | Production-grade, 18mo Casetrack | None | Conceptual | Whatever the lead does |
| Investor signal | Stanford / NASA / Sequoia / Careem | Mostly invisible | Recognized but not differentiated | Founder-only |
| Best for | Seed–Series B production hardening | Pre-product MVP iteration | F500 transformations | Solo / 2-person teams |
| Cost band | $80–250K + $8K/mo retainer | $30–80K project-based | $250K–$1M/year | Internal cost |
Founder-led, Stanford-engineered
The same credentials your investors care about.
Qasim Zafar
Founder & CEO
Stanford MS&E
Management Science & Engineering researcher
Sequoia InSITE Fellow
Sequoia Capital fellowship for early-stage founders
NASA Artemis subcontractor
Product manager — Lunar Gateway program
Savaree → Careem exit
Founding-team operator experience
18 months of production legal software
Built and operated Casetrack (sunset 2026)
Stanford MS&E researcher and Sequoia InSITE Fellow who founded AppRocket in 2017. Has shipped production AI for NASA Artemis (Lunar Gateway), digitized law firms end-to-end, and previously built and operated Casetrack, a production legal-software product (sunset 2026 to focus on services).
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Working with funded startups — frequently asked
Graduate from MVP.
Start with the AI Readiness Audit. We'll scope production hardening or MVP work against your stack specifically.