FAQ
Straight answers about building AI-native products.
The questions founders and teams ask us most, answered plainly.
Full FAQ
Everything, in one place
- What is an AI-native product studio?
- An AI-native product studio designs and builds products where AI is the core operating model, not a bolted-on feature. Dmware covers the full lifecycle — strategy, design, engineering, and the evaluations that keep the product reliable — and specializes in taking AI prototypes to production.
- How do I take an AI prototype from Lovable, v0, or Bolt to production?
- You rebuild it around the parts a prototype skips: real architecture, an evaluation harness for the AI behavior, guardrails, security, and observability. The prototype is a spec, not a foundation. Dmware hardens or rebuilds it into a system you own that scales and stays reliable, while keeping the momentum the demo created.
- Why doesn't my AI-generated app scale?
- AI-generated apps optimize for a convincing demo: happy-path code, no evals, secrets on the client, and no observability. Under real users and data they become unpredictable and unsafe to change. Scaling requires real architecture, evaluations, guardrails, and cost controls — the engineering a prototype deliberately skips.
- What makes a product "AI-native" versus just having AI features?
- A product is AI-native when it would be significantly weaker, or not exist, without AI: intelligence is the primary interface or control layer, outputs are probabilistic, and data and feedback loops are central. AI features are add-ons inside a traditional deterministic system.
- Should we hire an AI product consultancy or build the team in-house?
- Hire a studio when you need to move now and lack senior AI product and engineering experience in-house. A good studio de-risks the hardest decisions, ships a production system, and enables your team to own it — often faster and cheaper than assembling a senior AI team from scratch. Build in-house once the direction is proven and the work is steady-state.
- How much does it cost to build a production AI product in 2026?
- As a rough guide from current market summaries: a lean AI MVP runs about $25k–150k, and a seed-grade production AI SaaS about $150k–750k. The biggest cost drivers are no longer the model but product complexity, data needs, reliability, and go-to-market polish. Dmware scopes to a fixed outcome and cost envelope.
- Do you work with non-technical founders?
- Yes. Many of our engagements start with a founder who has a prototype or an idea and needs a senior team to turn it into a real product. We handle strategy, design, and engineering, and hand over something you understand and own.
- How do you keep AI systems reliable?
- We treat reliability as a discipline: every AI system ships with an evaluation harness, guardrails and fallbacks, observability into each model call, and cost controls. That is how you change prompts and models with confidence instead of guessing — and it is the rigor that separates a production system from a demo.
Work with Dmware
Have a prototype, or an idea that needs to become real?
Book a 30-minute intro call. We’ll tell you honestly whether we’re the right team, and what it would take to ship.