AI MVP Development
Quick answer
AI MVP development is building a working, launchable version of an AI-powered product - not a slide deck, not a proof-of-concept demo. Krishna Kumar builds these for non-technical founders in 2-6 weeks, with a fixed scope agreed up front and full code ownership on handover.
AI MVP Development
A Working AI Product, Not Another Prototype That Never Ships
Most AI MVP quotes are scoped for a company that doesn't exist yet - fine-tuned models, vector databases, multi-agent pipelines, all before the product has found its first ten users. I build the version that gets you those ten users first, then we add the rest once it's earned.
This is the build itself, not the strategy session. If you've already scoped what you want and just need it shipped - LLM integration, the product around it, deployed and handed over - that's what happens here. Still deciding what to build? Start with AI MVP Consulting instead.
Do You Actually Need a Custom Build?
Not every AI idea needs custom code on day one. Here's how I'd think about it before quoting you anything.
| If this is you | Go this route | Why |
|---|---|---|
| Simple internal tool, one user type, low volume | No-code AI builder (Zapier, Make, Bubble + AI plugin) | Fast, cheap, fine if you outgrow it later - not worth custom code yet. |
| Customer-facing product, needs to scale, handles user data | Custom build | No-code tools hit walls fast on cost control, data privacy, and rate limits at real usage. |
| Testing if the AI feature is wanted at all, before any spend | Manual/Wizard-of-Oz test - fake it with a human first | Cheapest possible validation. Don't build AI infrastructure for an unproven idea. |
| Idea validated, ready for real users, needs to be reliable | Custom build | This is where a proper AI MVP earns its cost - reliability and control matter now. |
If your honest answer is a no-code tool, I'll tell you that on the call - not quote you a custom build you don't need yet.
How a Build Actually Runs
Locked scope
We agree exactly what's getting built before any code is written - features in, features out, in writing.
Architecture & model pick
Model selection, prompt structure, and data flow decided up front, with a cost estimate at your actual expected usage.
Build in the open
Weekly check-ins, working software you can see - not a black box until the reveal at the end.
Test the outputs, not just the code
AI output quality gets tested separately from functional bugs - a feature can work and still produce bad answers.
Handover
Deployed, documented, and yours. Full codebase, no dependency on me to keep it running.
Tech Stack
No exotic stack for its own sake. Boring, provable choices win.
| Layer | Options | Why |
|---|---|---|
| LLM | GPT-4o, GPT-4o Mini, Claude Sonnet, Gemini Flash | Picked per use case, not defaulted |
| Backend | Node.js, Python (FastAPI) | Whichever fits the integration surface better |
| Frontend | Next.js, React | Fast to ship, easy to hand off |
| Database | PostgreSQL, Supabase | Boring and reliable beats novel here |
| Hosting | Vercel, AWS, Railway | Matched to your expected scale and budget |
Mistakes That Show Up After Launch, Not Before
Technical issues specific to AI builds - not strategy mistakes, build mistakes.
No cost monitoring from day one
A single unbounded loop or retry storm can turn a $200 API bill into a $5,000 one overnight. Spend alerts and rate limits go in before launch, not after the invoice.
Treating prompts like throwaway strings
Prompts change constantly during a build. Without version control on them, you lose track of which version was live when a bug happened - prompts get versioned like code, not edited in place.
No caching on repeated queries
Many AI calls in a real product are near-identical. Skipping caching means paying full price and full latency for answers you've already generated once.
Shipping without a fallback for AI failure
Every model call can time out, error, or return garbage. A product with no fallback state just shows the user a broken screen - the fallback path gets built, not bolted on later.
No rate limiting on user-facing AI features
Without limits, one user - or one bot - can run your inference bill up fast. Rate limiting is a launch requirement, not a nice-to-have.
What It Costs
Real ranges, not a "contact us for pricing" wall. Confirmed exactly after scoping.
Single-feature AI MVP
$10,000-$18,000
2-3 weeks
One core AI workflow, simple UI, no complex integrations
Full AI SaaS product
$18,000-$32,000
4-5 weeks
Multiple AI features, user accounts, billing, standard integrations
Multi-agent / complex system
$32,000-$45,000+
5-6+ weeks
Multi-step AI agents, custom data pipelines, enterprise integrations
Development or Consulting First?
If you already know what you're building and just need it built - this page is the right one. If you're still deciding between AI approaches, unsure which model fits, or haven't validated the idea yet, start with AI MVP Consulting instead - cheaper, faster, and it stops you building the wrong thing well.
AI MVP Development FAQs
Talk to an AI MVP Consultant Before You Build Anything
One 30-minute call can save you months of building the wrong AI product - and tens of thousands in wasted inference costs and development budget.
Book Free AI Strategy Call
30 minutes. Walk me through your AI idea and leave with a clear plan - including which model, what to build first, and what it will cost.
Call Directly
Prefer to skip scheduling? Call me at +91 90741 74001. I answer directly.
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