AI-Embedded Product Development
AI inside your product, not bolted onto the side
We design and build your product's AI features on Amazon Bedrock: part of the roadmap, with their own UX, their own metrics and their cost per user under control.
AI features your users actually use
The problem
- You know your product needs AI, but not which feature or how to price it.
- You added a generic chat and users tried it once and never came back.
- You don't know what each user costs you once the feature scales.
- Your team knows product, but not model evaluation or RAG.
What's included
- Use case selection: which AI feature moves retention, conversion or efficiency.
- Experience design: where the AI shows up, what the user controls and what happens when it fails.
- Built on Amazon Bedrock, with RAG over your product's data.
- Cost model per user and per plan, so the feature is profitable and not a hole.
- Continuous quality evaluation, with product metrics and not just technical ones.
- Go-live with observability, feature flags and progressive rollout.
AI is a product feature, not a lab project
An AI feature competes with the rest of the roadmap: it has to justify itself with product metrics. So we treat it like any other feature — hypothesis, progressive rollout, measurement, and a decision to keep it or kill it.
How we work
Discovery
Together with the product team we choose the AI feature with the best impact/effort ratio and define how it's measured.
Plan & Quote
We design the experience and the architecture, with tight scope and an estimated cost per user.
Execution
We build the feature, evaluate it with real users and iterate with a progressive rollout.
Hand-off & MSP
It stays in production, monitored for quality and cost. We can keep evolving it with your team.
Stack & technologies
AI embedded, measured and profitable
FAQ
Where should I start?
With one tight feature that has a clear product metric. Generic AI features almost never sustain usage.
How do I control cost per user?
We model it from the design: model choice per task, caching, per-plan limits and real consumption monitoring.
What happens when the model gets it wrong?
It's designed for that: show sources, allow correction, degrade gracefully and track the error rate as a product metric.
Does it work if my team already builds the product?
Yes. We work embedded with your team, contributing the AI, architecture and evaluation side.
