AI products that people actually use, from first screen to production release.
We design and build user-facing and internal applications where AI is the core of the experience — combining product design, model engineering and solid application code, then iterating on real usage after launch.
- Product and interaction design
- Model and prompt layer
- Application engineering
- Feedback and analytics
- Release pipeline
Where this helps
What we deliver
How it works
- 01
Product discovery
We define the users, the job they need done, and what a useful AI-assisted result looks like, then test clickable concepts with real users.
- 02
Technical spike
A narrow slice of the AI capability is built on real data to confirm feasibility, latency and cost before the full build.
- 03
Build in increments
Design, model and application work run together in short cycles, with a usable build at the end of each one.
- 04
Beta with real users
A limited release gathers feedback and usage data while evaluations run against production-like inputs.
- 05
Launch and iterate
General release, followed by a roadmap driven by feedback, analytics and evaluation results.
Design decisions we make with you
Where AI sits in the experience
Suggest, draft, or act — the product design depends on how much the AI does on its own and how the user stays in control.
Latency budget
Streaming, background processing or smaller models where users are waiting; slower, stronger models where accuracy matters more than speed.
Platform and stack
We build on frameworks your team can maintain. For work that is mostly conventional software, our [web application](/software/web-applications) team leads.
Data and privacy
What user data the AI can see, what is stored, and how long — designed with your security and legal requirements.
Ownership after launch
You own the code and design files. We can hand over to your team, co-develop, or keep supporting the product.
Applications
Related capabilities
- Enterprise Knowledge AssistantAnswer staff questions from your own policies, procedures and records, with sources shown and access rules respected.
- Personalization & RecommendationsShow each customer products, content and offers that fit their behavior, tested against a control and within the consent they have given.
- Generative AI & LLM DevelopmentApplications built on large language models that are grounded in your data, tested against real cases and costed before launch.
- AI IntegrationConnect AI models and assistants to your CRM, ERP, databases, knowledge stores and APIs — with identity, logging and fallbacks that production systems need.
- RAG & Enterprise Knowledge SystemsAnswers drawn from your own documents and data, with sources shown, permissions respected and content kept current.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
Questions buyers ask
AI App Development is for products where AI is central to the value. If AI is a small part of a larger application, our custom software team leads and brings in AI specialists as needed.
Yes. We work inside your codebase and conventions, or build the AI capability as a separate service your product calls.
Yes. Designing for AI output — sources, uncertainty, editing and feedback — is part of the engagement, not an afterthought.
Often. A focused first release tests whether users value the AI capability before you invest in the full product. See MVP development.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.