Ship an AI product
that works in the real world.
Not a demo. Not a prototype with AI sprinkled on top. A production-ready, AI-native product — built in weeks, shipped with confidence, ready for your first real users.
Used to ship: VetERP · ProLeap Platform · Vetyo · QReward · Resay
AI-native means AI is the product, not a feature.
Most MVPs add AI as a late-stage feature. An AI-native MVP is designed from the first line of architecture with AI as the core delivery mechanism.
The difference shows up everywhere:
In the architecture — data pipelines, vector stores, and agent loops are load-bearing structures, not plug-ins. They’re built to scale, not to demo.
In the product logic — the product’s value proposition only exists because of the AI layer. Remove the AI, and the product no longer works.
In the infrastructure — containerized, cloud-native, with observability baked in. Not a Jupyter notebook dressed up as a product.
This matters because retrofitting AI into a non-AI-native architecture is expensive, slow, and often structurally impossible. The cost of getting this wrong isn’t a refactor — it’s a rebuild. Getting it right from the start is how you ship in weeks, not months, and scale after launch without rewriting the foundation.
What we build.
Every MVP engagement is different. The common thread is production quality, honest timelines, and AI that does actual work.
Agentic Applications
Products where AI agents take autonomous or semi-autonomous actions — browsing, writing, summarizing, scheduling, researching, transacting — on behalf of the user. Built with ReAct loops, function calling, and multi-step planning.
AI research assistants, automated workflow tools, AI-driven CRM actions, autonomous reporting systems.
Multi-Agent Systems
Architectures where multiple specialized agents collaborate to complete tasks no single agent handles well. Orchestrated via the MCP standard, with clearly defined agent roles, handoffs, and state management.
Multi-agent data pipelines, orchestrated content production, multi-step customer journey automation.
RAG-Powered Knowledge Products
Products that let users query, understand, and act on large bodies of proprietary knowledge using retrieval-augmented generation. Built with HyDE and MultiQuery retrieval strategies for precision, not just recall.
Internal knowledge bases, document intelligence, AI customer support, compliance query engines.
AI-First SaaS & Platforms
Full-stack software products where AI is the primary value delivery layer. Built on modern frameworks (Next.js 15, React 19, FastAPI) with cloud-native infrastructure, deployed to production from day one.
Vertical SaaS, AI-powered analytics platforms, productized AI services.
AI-Embedded Workflows & Internal Tools
Internal-facing products that automate, accelerate, or augment specific business workflows with AI. Often the fastest path to measurable ROI for a company that doesn’t need a consumer-facing product.
AI onboarding flows, automated reporting, internal compliance assistants, intelligent intake forms.
Not sure which category fits your product? That’s what the Discovery Sprint is for.
The stack we actually ship on.
These aren’t buzzwords on a deck. This is the stack we run in production — on our own ventures and on client builds.
Stack choices are always made for the specific product — not because a framework is trendy. If your product has specific technical constraints or an existing stack to integrate with, the Discovery Sprint is where that conversation happens.
This engagement is right for you if:
Right fit
- You have a product idea where AI is the core, not a nice-to-have. You’re not adding a chatbot to an existing app — you’re building something that only works because of AI.
- You want production-ready, not a demo. Your goal is to ship something real users can use — not a prototype that impresses investors but breaks on day two.
- You’re in a 6–16 week build window. You need speed, but not the kind that creates technical debt you’ll spend a year unwinding.
- You’re open to honest scoping. If your idea needs more time or a different approach, we’ll tell you in the Discovery Sprint — not after the first invoice.
- You want a team that has shipped AI products before. Not just trained on AI — actually built and deployed it on real infrastructure, for real users.
Not the right fit
- You need a single no-code integration or a plug-in. This service is for bespoke builds. If you need a Zapier automation or a configured SaaS tool, we’ll tell you — and save you both time and money.
- Your budget is under $15K. A real AI-native product requires real engineering time. We don’t ship work we’re not proud of, and we can’t do that under this threshold.
- You need the full product built before the idea is validated. We’d recommend starting with an AI Audit & Strategy engagement first — shorter, cheaper, and often changes the scope in ways that save build cost.
Already have a product and need AI embedded in it? That’s our AI Integration & Automation service. AI Integration & Automation →
Need autonomous agents without a new product build? See Agentic Systems. Agentic Systems →
Every build starts with a Discovery Sprint.
We don’t take a build brief, write a proposal, and disappear into development for three months. Every engagement — regardless of size — starts the same way.
Discovery Sprint1–2 weeks, fixed fee
Before any code is written, we spend one to two focused weeks understanding your product, your users, your constraints, and the actual problem you’re solving.
You receive a written output: a technical scoping document, an architecture recommendation, a phased build plan with honest timelines, and a fixed-fee proposal for the full build.
This phase is paid. It protects you (you know what you’re buying before you commit) and it protects us (we don’t scope for free and lose the project to a competitor who underbids with a fake timeline).
Discovery Sprint fee: [PLACEHOLDER — confirm with Daniel. Typical range: $2,500–$5,000 depending on complexity.]
Build Sprints2-week cycles
Development runs in two-week sprints with a working demo at the end of each sprint. You see the product forming in real time — not a PowerPoint, a running build you can interact with.
Every sprint begins with a written scope. No mid-sprint additions without logging the trade-off. No silent overruns. If something changes — and it will — we name it and you decide.
Ship & Handoff
Production deployment on the agreed infrastructure. Full technical documentation — architecture diagrams, API specs, deployment runbook, environment setup. A working knowledge-transfer session with your team.
We don’t believe in dependencies. When we’re done, your team owns what we built.
Optional: Ongoing Partnership
If the product needs continued iteration, support, or AI model management after launch, we offer a monthly retainer that keeps a named engineer on your product. This is not a default — it’s an option you control.
Fixed-fee for Discovery Sprint. Fixed-fee or milestone-based for Build. Monthly retainer for Ongoing Partnership. We also offer equity + cash deals for early-stage startups where budget is constrained but the opportunity is strong. [PLACEHOLDER — final pricing guidance to be confirmed by Daniel before launch.]
Products we’ve shipped.
We build for our own ventures on the same architecture we build for clients. Here’s a selection of products we’ve shipped — internal and external.
VetERP
Veterinary Practice Management System — MVP to Phase 2
A full veterinary ERP built from scratch, through MVP approval, and into Phase 2 feature development. [PLACEHOLDER — add 1-line outcome or client context if approved for public mention.]
ProLeap Platform
AI-Powered Learning & Talent Platform
Built inside Kharaayo Tech — cohort-based learning, learner progression tracking, and an instructor workflow built for the AI career-development era. AI Career Consultant and AI Placement Manager agents in technical scoping.
Vetyo
Internal venture
[PLACEHOLDER — Vetyo one-line description from Daniel.] Built through frontend–backend integration with weekly demo cadence. Currently in testable state.