The AI demo that dazzled the investors, then died on real inputs.
The prototype looked incredible on demo day. Clean inputs, rehearsed prompts, a model that answered every time. Then real users arrived with messy data, and it fell apart.
Six months and $30K later, the model still wasn't wired into the product. The backend team built one thing, the ML work lived in a notebook, and nobody had connected the two.
A working demo is not a working product. The architecture is where the two diverge.
According to McKinsey's 2024 State of AI report, 78% of organizations are now using AI in at least one business function, up from 55% a year earlier. For founders building AI-native products, the window for differentiation is narrow, and the competitive lever is speed to a working, production-grade product. RaftLabs has shipped 20+ AI products in 24 months for founders across the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia, for clients including Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin, rated 4.9/5 on Clutch across 9+ years of shipping software and AI products. One team scopes the MVP, designs the AI into the architecture from sprint one, ships it in 8 weeks at a fixed price, and hands it over.
Building an AI MVP isn't the same as building a standard software MVP with a GPT wrapper on top. The failure modes are different. An AI MVP built without a defined data strategy will hallucinate in production. One built without proper context management will lose coherence after three messages. One scoped for 50 users but architected for 50,000 will cost ten times more to run than it should.
An AI MVP pays off when you're ready to put real users in front of it.
Everything on the left should already be true. Even one thing on the right, and a prototype or a longer discovery is the smarter first step.
A fit01You're a founder with an AI-native product idea and budget to build a real, deployed MVP, not just another prototype.
02You have data samples the AI can be tested against, or a clear path to them before the build starts.
03You want a working product in weeks, at a fixed price, with the code, models, and infrastructure owned by you outright.
Not a fitYou want a demo-day prototype to show investors, not production software real users will touch.
The core assumption can't be tested until the full product is built, so there's no minimum scope to ship first.
You want an open-ended monthly engagement rather than a scoped, fixed-price project with a defined end.
What we build
What we build into your AI MVP
01Launch your AI MVP in 8 weeks, not 8 months
The 8-week timeline works because we resolve the decisions that stall most AI projects in Week 1: model selection evaluated against your actual use case inputs, data pipeline design, and integration architecture, all specified before application code is written. Sprint 1 prototypes the core AI interaction with your real data and measures output quality against acceptance criteria. A provider-agnostic model abstraction layer ships from day one, so switching providers later changes one service class, not 40 call sites.
02Grow to 100K users without rewriting your code
The AI service layer deploys as an independent, auto-scaling microservice, so added instances add capacity linearly without touching your application code. Database schemas are designed for the query patterns at 10,000 users from day one, with caching for deterministic AI responses. Infrastructure is defined as code from Sprint 1 and load tested against 500 concurrent users before launch. The stack we reach for here: AWS ECS Fargate, GCP Cloud Run, Redis, Terraform, and k6.
03Built for engagement, not just aesthetics
AI product interfaces have a design challenge generic patterns don't address: users need to understand what the AI can do, when it's working, and when to question its output, all in the first session. Loading states are calibrated to real inference wait times, streaming output renders token by token, and layouts cite sources alongside claims. Wireframes at mobile, tablet, and desktop breakpoints are delivered in Figma before development starts.
04Your post-launch pit crew: tweak, track, fast-track growth
The 4-6 weeks after launch surface problems test data doesn't reveal: real user inputs are messier and more varied than anything you designed against. Monitoring deploys from day one, with Sentry error tracking plus latency and output-quality alerts. Prompt iteration runs weekly against the most common logged failure patterns, with prompt versions tracked in the database so rollback is a data update, not a deployment.
The three most common ways we see AI MVPs fail before launch:
1. AI as an afterthought. The product is scoped as a standard web app. AI gets added in week eight because a competitor announced a feature. The architecture wasn't designed for it. The result is slow, expensive, and brittle.
2. Building the AI layer before the product layer. Founders spend $40K fine-tuning a model before knowing what the user actually wants from it. The model is impressive in demos. Nobody uses the product.
3. No validation loop. The AI MVP ships. Nobody knows if the AI outputs are good. There's no feedback mechanism, no accuracy tracking, and no way to know whether the product is generating trust or eroding it.
We design AI MVPs to avoid all three. The AI component is scoped in discovery, designed into the architecture, and shipped with validation built in from the first sprint. You get a product that works in production, not one that works in a demo.
Ready to scope your AI MVP project?
30 minutes. You walk away with a clear cost, timeline, and honest assessment. No commitment.
How it works
From scope to shipped
Every project follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 0
01Free discovery call
30 minutes where we review your idea, the data you have available, and the AI capability you're trying to build. We'll tell you honestly whether the AI component is feasible with your current data, whether there's a simpler technical approach you haven't considered, and whether the 8-week timeline is realistic for your scope. If we think the idea isn't ready or the timing is wrong, we'll say so. That conversation is free and you walk away with an honest assessment.
- Weeks 1-2
02Discovery and architecture ($2K)
Week 1: discovery sessions to define the single core workflow the MVP must nail; technical architecture design covering the data model, the AI integration approach (direct API call, RAG pipeline, or fine-tuned model), and the backend/frontend stack; model selection with actual prompt prototyping against your real data samples to confirm the model produces usable output before any application code is written. Week 2: Figma wireframes at 375px, 768px, and 1280px breakpoints covering the primary user journey end to end; sprint-by-sprint development plan with clear deliverables and acceptance criteria per sprint. Deliverables you own regardless of whether you proceed: the Figma wireframe file, the technical architecture document, the AI integration spec, and the sprint plan.
- Weeks 3-8
03Build and integrate ($10K)
Six weeks of development producing a working, deployed product. A production environment with real authentication (Auth0 or NextAuth), a real PostgreSQL database, infrastructure you own (AWS or GCP with Terraform-defined resources), and AI responses generated live from actual user inputs against your chosen model provider. Week-by-week delivery: Sprint 1 (weeks 1-2), backend API scaffold, authentication, database schema, AI service layer wired and returning real responses; Sprint 2 (weeks 3-4), frontend built against the core user journey with AI output display, error handling, and loading states; Sprint 3 (weeks 5-6), end-to-end testing with Playwright, Sentry error monitoring deployed, k6 load test against the AI endpoint, and production deployment with DNS configured and SSL active.
- Weeks 8+
04Launch and post-launch support
First 10 real users onboarded during week 8. Production deployment with monitoring activated on launch day. 8 weeks of post-launch support included: prompt iteration, edge case handling, and weekly review of failure patterns. You leave with a live product, a Sentry dashboard showing real usage, and a codebase that is yours. GitHub repo transferred to your organisation, all infrastructure credentials in your accounts, zero dependencies on our accounts or infrastructure after handover.
What clients say
Most clients stay.
Some say so on camera.
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Nuala C.
IrelandDirector, BrandFire
“This team nailed it! Their process was seamless, and we launched our SaaS platform in record time. Couldn't be happier!
The price is fixed before development starts, structured as two milestones you can stop between:
- Discovery and architecture, $2,000
- A technical specification, Figma wireframes, and a sprint plan, all yours to keep whether or not you continue to the build.
- Build and integrate, $10,000
- The six-week development sprint that produces the working, deployed MVP: real authentication, a real database, infrastructure you own, and AI responses generated live from actual user inputs.
What it costs
Milestone payments, starting at $2,000, pause at any stage.
AI designed into the architecture from sprint one, a working product in 8 weeks, and a codebase that is yours: GitHub repo, infrastructure, and credentials transferred at handover.
Starts at $2,000Two milestones. The first, $2,000, covers discovery and architecture. The second is priced once discovery defines the scope, and covers the six-week build. Pause after either one and keep everything produced to that point.
The only commitment on day one is $2,000. Most clients move on to the build once discovery confirms the plan, but nothing obligates them to.
No hourly billing
Each milestone price is locked in writing before work starts. No hourly billing, no surprise invoices, no change fees you didn't agree to first.
No lock-in
Modular architecture, a provider-agnostic model layer, and infrastructure you own. Scale it, or hand the codebase to your own team, with no dependency on our accounts after handover.
