Service
Applied AI engineering
We build the AI systems behind the product: retrieval-augmented generation, tool-using agents, structured extraction, and multi-model pipelines. Every system ships with an evaluation harness, guardrails, and observability so its behavior is measurable and safe, not a black box you hope works.
The problem
The gap between an LLM demo and a dependable system is evaluation, reliability, latency, and cost. Without evals you cannot ship changes with confidence; without guardrails you cannot put it in front of customers.
What you get
- Reliable LLM/RAG/agent systems in production
- Evaluation harness with regression protection
- Guardrails, fallbacks, and safety checks
- Latency and cost within a defined budget
- Observability into every model call
Good to know
- 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.
Next service
AI product design →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.