Discover
Define the user problem, success metric, data constraints, and the smallest valuable AI workflow.
Code Huddle Solutions
AI Product Development
We help teams turn an AI idea into a reliable product with a clear user journey, measurable quality, and a production-ready architecture.
Discuss your projectWho this is for
- Founders, product teams, and enterprises validating an AI product or adding AI to an existing application.
Problems we solve
- Unclear AI product scope and user value
- Prototype quality that does not survive real usage
- Uncontrolled model cost, latency, or hallucinations
- Security and privacy gaps around customer data
How we work
A practical path from idea to reliable delivery
Prototype
Test prompts, retrieval, model choices, UX, and evaluation criteria with representative data.
Engineer
Build the product boundary, observability, permissions, fallbacks, and repeatable evaluation pipeline.
Operate
Launch with monitoring for quality, cost, latency, safety, and user feedback.
Scope and investment
Start with the smallest valuable scope
Most focused AI product engagements begin with a 2–4 week discovery and prototype phase, then move into a staged MVP. Final scope depends on integrations, data readiness, and evaluation requirements.
Technology patterns
- OpenAI
- Anthropic
- Google Gemini
- Python
- FastAPI
- Node.js
- Next.js
- PostgreSQL
- Redis
- AWS
Evidence and related work
- GYMYG virtual fitness platform
- House Hint AI real estate platform
Tradeoffs we make explicit
- Hosted models reduce infrastructure work but introduce vendor cost and dependency.
- Open-source models can improve control but require more evaluation and operations.
- Automation should be bounded by permissions, review steps, and observable failure modes.
Questions
Frequently asked
Can you add AI to an existing product?
Yes. We can add an AI workflow behind a stable API boundary while preserving the existing product, authentication, analytics, and deployment model.