What is included
- AI features within existing workflows
- Task and workflow automation
- Model and usage cost planning
- AI service integration
- Response evaluation
- Ongoing feature refinement
AI that has a job to do.
An AI feature begins with a useful task and a way to judge whether it works. We choose models and integrations around that task, then connect them to the existing web or mobile product. The work includes prompts, API integration, evaluation, error handling and monitoring. Model output is one part of the experience; loading, retries and usage cost need attention too.
How it runs
- Definition — Identify the task and what a useful response looks like.
- Selection — Compare models and APIs against the requirements.
- Integration — Connect the feature to the application.
- Evaluation — Review responses, errors and usage cost.
- Release — Add error handling and fallback behaviour.
- Observation — Monitor usage, response quality and performance.
Deliverables agreed for your scope
- Integrated AI features
- API adapters and utilities
- Error handling and fallbacks
- Usage cost management approach
- Integration documentation
- Monitoring configuration
Stack
- OpenAI API
- Anthropic Claude
- LangChain
- Vector databases
- Python / Node.js
- API integration
A Codebiy product: Dusora
The challenge
A journal entry should remain useful when AI interpretation and image generation are added to the experience.
The solution
Dusora, live on the App Store, connects AI interpretations and images to saved dreams. Personal reflection and follow-up questions are part of the same context.
The resulting approach
The AI capability sits inside a complete journaling flow. People can record, organize and revisit an entry as well as explore an interpretation.