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QBIT.LABS
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AI-first MVPs
for founders who need to move fast.

We build AI-first B2B SaaS MVPs for early-stage founders, solo founders, and small startups - from idea, to prototype, to deployed product in weeks.

MERN + AI 4-person studio Available globally
Studio Build Flow
Discovery Prototype Build Launch
Typical MVP: 4-8 weeks
Services

Three ways to take an AI MVP from idea to launch.

We help founders validate the problem, design the product, and ship the first working version without unnecessary process overhead.

MVP Discovery Sprint

Validate the right product before you build it.

For founders who need clarity on scope, user flow, and technical feasibility before committing build budget.

Deliverables: problem framing, user journey, MVP scope, tech recommendations, and build plan.

Outcome: a clear roadmap your team can execute or hand to us for implementation.

Best for pre-build validation
Prototype MVP

Turn the core workflow into something founders can test.

For teams that already know the problem and need a working prototype to demo, validate, or pitch.

Deliverables: clickable UX, core screens, authentication, AI workflow, and deployment setup.

Outcome: a polished prototype that proves the product direction quickly.

Best for demos and investor conversations
Launch MVP

Ship the first production-ready version with confidence.

For startups ready to launch a real product with the right stack, integrations, and deployment discipline.

Deliverables: end-to-end build, API integrations, admin tools, analytics, QA, and handoff.

Outcome: a deployed MVP your team can improve after launch without rework.

Best for launch-ready execution
Process

A simple four-step studio process.

We keep the engagement tight, the build cycle visible, and the handoff clean so founders can move quickly without losing control.

01 1

Discovery & Scope

We clarify the problem, user, and MVP scope, then define what is worth building first.

02 2

Wireframes & Architecture

We map the product flow, key screens, and system architecture before any heavy development starts.

03 3

Weekly MVP Builds

We ship in small, visible increments so you can review progress and make decisions weekly.

04 4

Launch & Handoff

We deploy the product, document the system, and hand over the source code and next steps cleanly.

Selected Work

Case studies from recent MVP builds.

Representative work across B2B SaaS, internal tools, and AI workflows. Each engagement focused on speed, clarity, and deployable outcomes.

FEATURED PROJECT

FairCouncil

Solved unchecked AI bias by building a council of LLMs that dynamically elects a fairness-adjusted chairperson — instead of trusting one model blindly. Final answer is selected by the least-biased, best-performing model.

• AI / Multi-LLM
Problem
Single-model bias, no fairness layer.
Built
Multi-LLM pipeline, bias scoring, adaptive chairperson.
Stack
FastAPI, React, Gemini, Groq, LLaMA.
Scoring formula
Perf score − λ × Bias score

LexiRisk

Legal workflow

Solved slow manual contract review by turning legal PDFs into an AI-assisted clause-by-clause risk analysis — with plain-English summaries and exportable reports.

Built: PDF upload, clause classifier, risk scorer, PDF report export.

Stack: React, Express, FastAPI, BART, scikit-learn.

Timeline: Approx. 4 weeks.

Clarivo

Speech therapy AI

Solved inaccessible speech therapy by building an AI tool that listens, scores pronunciation in real time, and gives encouraging feedback — like a therapist in your pocket.

Built: Whisper transcription, WER/CER/semantic scoring, TTS guidance.

Stack: React, Node.js, Flask, OpenAI Whisper, BERT.

Timeline: Ongoing.

Why Us

Why founders choose Qbit Labs.

A small, focused studio is easier to trust when you need momentum, clarity, and direct ownership of the build.

AI + full-stack delivery

One team owns product, frontend, backend, and AI integration end to end.

Fast iteration cycles

We work in short loops so you can review the product while it is still moving.

End-to-end MVP execution

From scope to launch, we stay responsible for the whole outcome, not just a slice.

Clean handoff, no lock-in

You get the source code, deployment notes, and a setup that your team can own.

Small, focused team

You work directly with the people building the product, not a layered agency structure.

Engagement Models

Pricing that matches studio work, not subscriptions.

Every project is scoped around the outcome you need. Choose the format that best matches where you are right now.

Discovery Sprint

Starting at $300

For founders who need scope, direction, and a clear build plan before committing to development.

  • Problem framing and opportunity map
  • MVP scope and feature prioritization
  • Architecture recommendation
  • Delivery plan and timeline
Prototype MVP

Typical range $1200-$2500

For early-stage teams that need a polished prototype with the core user journey and AI workflow working end to end.

  • Clickable product experience
  • Core backend and AI integration
  • Authentication and basic admin tools
  • Deployment-ready handoff
Custom MVP Build

Custom quote

For founders ready to launch a fuller product with integrations, iterative development, and a clean transition to ownership.

  • End-to-end product build
  • Integrations and workflow automation
  • Analytics, QA, and release support
  • Source code, docs, and handoff
FAQ

Common questions from founders.

Who do you work with?

We work with international early-stage founders, solo founders, and small startups, especially teams building B2B SaaS products with AI inside the workflow.

What does an MVP include?

A typical MVP includes the core product flow, essential backend logic, AI integration where needed, deployment setup, and a clear handoff.

How long does it take?

Discovery sprints can take a few days. Prototype and launch builds usually land in the 4-8 week range depending on scope.

Do you work with non-technical founders?

Yes. A large part of our work is helping non-technical founders make the right product and architecture decisions early.

Do we get the source code?

Yes. The codebase, deployment notes, and essential documentation are handed over cleanly so your team can own the product.

Can you add AI features into an existing product?

Yes. We can add AI workflows, automations, or copilots to an existing product without reworking the whole stack.

Have an idea, prototype, or early product that needs an AI-powered MVP?

Tell us what you’re building and we’ll point you toward the fastest path to a deployable product.

qbitlabs.studio@gmail.com

Usually reply within 24–48 hours.

Free 5-minute MVP teardown

Send us your idea, landing page, or current flow.