Discover
We start with the business, not the backlog. What has to be true in six months for this to have worked?
A shared definition of done
AI Product Engineering Studio
We build products. Not just software.
An AI-native studio taking ambitious ideas to production. Architecture, design, engineering, and the judgment to ship.
ScrollIST
01The studio
Most software gets built in fragments. We work as one mind across strategy, design, and engineering.
The studio takes products from whiteboard to production and stays accountable for what happens after. Systems thinking decides the architecture. Product judgment decides what gets built at all. Execution is where both get proven.
01
AI-native
We design autonomous systems and live on them. The studio's own QA, code review, and documentation run on an agent fleet we built, around the clock.
02
Product thinking
Features get chosen the way an owner chooses: by cost, latency, reliability, and what the business needs to be true in six months.
03
Engineering excellence
Production systems sustaining 250+ requests per second with sub-second APIs and 99.9% uptime. Quality here is measured, not promised.
Studio led by Hemanshu Upadhyay. Five-plus years across engineering and product: owning the AI roadmap for an AI-native hospitality platform, fractional CTO work for an early-stage startup, and production systems serving 30K+ daily users.
02Capabilities
What we take on.
Eight disciplines, one accountable team. Expand any of them.
We take AI features from roadmap to production: deciding which workflows deserve autonomy, prioritizing by cost, latency, and reliability, then building them. Grounding, evaluation, and human handover are part of the spec, not afterthoughts.
- AI roadmap and workflow selection
- Grounded conversational features
- Persona and prompt systems
- Cost and latency budgets, held
Proof: Ranger, the KaribuKit guest concierge, answers from live reservation data and hands over to humans when it should.
Next.js portals, Node and NestJS APIs, Expo and React Native apps, structured in monorepos with clean seams. The kind of codebase a future team inherits gladly.
- Next.js and React applications
- Node.js / NestJS APIs
- React Native and Expo apps
- Monorepo architecture
Proof: KaribuKit spans staff portal, guest portal, API, and mobile app in one pnpm monorepo.
Agent fleets that review code, test products by driving real browsers, and run operations. Every agent we ship is caged: allow-list write guards, isolated worktrees, no self-merge, adversarially audited.
- Agent architecture and orchestration
- Browser-driving QA agents
- Security-first agent guardrails
- Structured, machine-parsed output
Proof: Sentinel runs three agents 24/7 at roughly $0.03 per QA run, and caught production bugs no human test covered.
Project tracking, documentation, and cross-team workflows built as one coherent internal operating system, sized for the team that exists today.
- Internal operating systems
- Workflow and process tooling
- Documentation infrastructure
Proof: Hanuneeb's four departments coordinate through one internal OS the studio built.
Modular monoliths before microservices. Isolation enforced at the database layer. Idempotency as the default. Architecture decided with reasons written down.
- System design and review
- Multi-tenant architecture
- Event-driven pipelines
- Migration and scaling plans
Proof: SignalOps enforces tenant isolation and billing limits where application bugs cannot route around them.
Specs, scoping, and build-versus-buy decisions from someone who has owned an AI product roadmap, not just implemented one. Business plans become dated engineering deliveries.
- Product specs and roadmaps
- Build-vs-buy analysis
- Technical due diligence
- Fractional CTO engagement
Proof: As fractional CTO for Hanuneeb, the studio owns hiring, budget, and stack decisions end to end.
AWS, Docker, and CI/CD pipelines tuned for small teams that ship daily. Deployments measured in minutes, uptime measured in nines.
- AWS architecture
- Docker and CI/CD pipelines
- Caching and queue infrastructure
- Performance budgets
Proof: KaribuKit's pipeline cut deployment time 30% while holding 99.9% uptime.
Messaging automations, background jobs, and scheduled agents that take real work off real people, with fallback routing for everything the machine should not decide.
- Messaging automations (WhatsApp, Telegram)
- Background job systems
- Scheduled agent workflows
- Human-fallback design
Proof: KaribuKit's booking automations measurably cut operational response times at live properties.
03Selected work
Case studies, not screenshots.
Each entry is a system running in the world, told the way it was built: problem, approach, architecture, outcome.
01
KaribuKit
An AI-native property management system for safari lodges, from reservation core to a guest-facing AI concierge.
Hospitality / AI-native SaaS2025 to present
02
Sentinel
A 24/7 autonomous agent fleet that reviews code, syncs docs, and QA-tests a live product by driving a real browser.
Developer infrastructure / autonomous agents2026
03
AFK Dev
Ship reviewed, screenshot-verified pull requests from a phone. SSH over Tailscale in, one-tap merge out.
Developer tooling / remote agents2026
04
SignalOps
An event-driven multi-tenant backend that treats tenant isolation and idempotency as invariants, not features.
Backend platform / multi-tenant SaaS2025
05
Hanuneeb
Technology ownership for an early-stage startup: architecture, delivery, and an internal operating system for four departments.
Early-stage startup / fractional CTO2026 to present
06
Enterprise Component Platform
A 30-component enterprise library adopted across 15+ projects, cutting front-end effort by 40%.
Enterprise / design systems2022 to 2024
04Process
From first call to running system.
Research
Users, constraints, prior art, and the honest question of whether AI belongs in this product at all.
Findings with recommendations
Architecture
Boundaries, data flow, and invariants, decided before they get expensive. Reasons written down.
Architecture decision records
Design
Interface and interaction design in code, where real constraints live. No throwaway mockups.
Working prototypes
Development
Short cycles, visible progress, working software from week one. You watch it grow, not wait for it.
Shippable increments, weekly
Testing
Automated coverage on critical logic, plus agentic QA that drives the real product in a real browser.
Structured QA reports
Launch
Deployment pipelines, monitoring, and rollback paths rehearsed before the day they matter.
A production system, observed
Growth
Post-launch iteration driven by usage, performance budgets held, and a roadmap that stays honest.
An ongoing partnership
05Why us
Why teams choose a studio of one.
Not despite its size. Because of it. Every reason below is specific and checkable.
01
Engineering-first
The person you talk to is the person who architects and ships your product. No account layer, no telephone game between sales and delivery.
02
Product mindset
We have owned an AI product roadmap, not just executed one. Features get prioritized by cost, latency, and reliability, the way an owner would.
03
AI-native, not AI-flavored
Our own operations run on autonomous agents: QA, code review, docs. We ship AI we trust because we live on it first.
04
Production quality
250+ requests per second, sub-second APIs, 99.9% uptime. These are numbers from systems we run, not aspirations from a pitch deck.
05
Fast iteration
Working software from week one, in short cycles you can see. Speed comes from small scope and senior judgment, not from cutting corners.
06
Transparent communication
Scoped, dated deliveries. Decisions argued with reasons. Bad news early. You always know where the project stands.
07
Long-term partnership
We build systems a future team inherits gladly, and we stay accountable after launch. Several engagements here are ongoing by design.
06Signals
Proof over promises.
We let shipped products speak first. Client words will live here as current engagements conclude; references are available on request.
30K+
daily users on platforms we engineered
QED42 production platforms, 2022 to 2024
99.9%
uptime on systems we run today
KaribuKit production infrastructure
24/7
autonomous QA on everything we ship
Sentinel agent fleet
References available on request.
07Technology
Tools chosen with reasons.
TypeScript end to end. Boring where boring wins, ambitious where it pays.
- Claude API
- Multi-agent systems
- LLM orchestration
- LangChain
- Agentic browser QA
- Playwright
- Prompt engineering
- Eval & cost optimization
- React
- Next.js
- TypeScript
- React Native
- Expo
- TailwindCSS
- GSAP
- Three.js
- Node.js
- NestJS
- GraphQL
- REST
- WebSockets
- PostgreSQL
- MongoDB
- Redis
- BullMQ
- AWS
- Docker
- GitHub Actions
- CI/CD
- Supabase
- CloudFront
- Modular monoliths
- Event-driven systems
AI & Agents
Claude API · Multi-agent systems · LLM orchestration · LangChain · Agentic browser QA · Playwright · Prompt engineering · Eval & cost optimization
Frontend
React · Next.js · TypeScript · React Native · Expo · TailwindCSS · GSAP · Three.js
Backend
Node.js · NestJS · GraphQL · REST · WebSockets · PostgreSQL · MongoDB · Redis · BullMQ
Infrastructure
AWS · Docker · GitHub Actions · CI/CD · Supabase · CloudFront · Modular monoliths · Event-driven systems
08 · Start here
Book a strategy call.
Bring the ambitious idea, the stalled build, or the AI roadmap that needs an owner.
- 01
A 30-minute strategy call
About your product and constraints. Not a sales pitch.
- 02
A written proposal
Scope, dates, and price. Decisions argued with reasons.
- 03
Work on a dated plan
If we are a fit, the build starts against commitments, not estimates.
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09 · The person behind the studio
Want to go deeper?
The studio is led by one engineer, and the engineering is public. Explore the personal portfolio: the experiments, the write-ups, and the work behind the work.
10FAQ
Fair questions.
The studio is led by one senior product engineer who architects, designs, and ships your product personally, augmented by an autonomous agent fleet for QA, code review, and documentation. You always talk to the person writing the code. We never resell your project to subcontractors.
By evidence. The systems described in Selected Work run in production today: 250+ requests per second, 99.9% uptime, platforms serving tens of thousands of daily users. A small studio with heavy automation ships with the leverage of a team and the accountability of an individual.
Most work is either a scoped product build (spec to production, typically 6 to 16 weeks) or an ongoing fractional CTO / product engineering partnership. Both start with a strategy call and a written proposal with scope, dates, and price.
Two ways. We build AI features into your product where they genuinely earn their place. And we use our own agent infrastructure (autonomous QA, code review, docs-sync) to hold quality on every project. AI accelerates the work; a human owns every decision that ships.
You do. Full ownership transfers on payment, repositories live in your organization from day one where possible, and there is no lock-in by design.
Launch is the midpoint, not the end. Monitoring, iteration cycles, and performance budgets continue as long as the partnership does. Several current engagements are ongoing by design.
Yes. Common shapes: owning the AI layer of your roadmap, architecting a system your team then builds, or fractional CTO work covering hiring, budgets, and build-vs-buy decisions.
TypeScript end to end. Next.js and React Native on the front, Node or NestJS with PostgreSQL, Redis, and BullMQ behind, AWS underneath. Boring where boring wins, ambitious where it pays.
11Contact