StemChain clinic directory
A complete clinic-directory build covering architecture, frontend, Laravel backend and data collection.
Navigation
Read case study Filtering by specialty, country and city
AI-Powered Web Application Development
AI integrations planned for real data and operating costs.
Discuss a project
The scope
AI is useful when it improves a defined task and the product can handle uncertain output. I start with the user's decision, source data, review step and operating cost, then choose whether an AI service belongs in the flow at all. Hyperhub documents AI tools behind shared accounts and subscriptions. StemChain documents translation support in a clinic-directory workflow. Neither case is evidence that I trained a foundation model or built a medical decision system.
When this helps
- People repeat a content or data task that AI could assist but should not silently decide
- An AI demo must become a usable feature with accounts and operational controls
- You need to understand the cost and risk before connecting a model API to real users
From process to production.
A useful AI system starts with a defined task, earns access to the right context, and proves itself on real work.
Discuss your workflowDefine the job
Map the trigger, inputs, decisions and exceptions. Agree on what the system may do and when a person takes over.
Working resultA focused, testable scopeConnect the context
Connect approved data and tools. Set access rules, handoffs and fallback paths around the workflow.
Working resultAn integrated working flowEvaluate in use
Test representative tasks, review errors, time and running costs, then improve the flow before and after launch.
Working resultA system you can inspect and refine
What I can deliver
A use-case assessment that compares AI with simpler deterministic options
A scoped integration with input, output and human review points clearly defined
Account, usage and cost controls where the feature is part of a paid product
Tests using realistic examples, failure cases and an agreed fallback
How I work
Define the task
I identify what the user needs, what data is available and how success can be checked.
Prototype the boundary
I test examples and decide where human review, limits and fallback belong.
Integrate and measure
I build the agreed feature and review output quality, cost and failure behavior before wider use.
Starting point
From $890per project
One bounded AI prototype using supplied examples
This price covers the starting scope described above. I confirm a quote after reviewing your brief and existing systems. Third-party fees are separate.
Send a brief on WhatsApp90-day cover for delivered development
For development work I deliver, I correct defects in the agreed scope reported within 90 days of launch. New features, changes by others and third-party outages are separate work.
Need ongoing support?See maintenance and support
Technology
- OpenAI API
- LangChain
- RAG
- Vector DB
- Python
- Node.js
- React
Frequently asked questions
Will you train a proprietary model for my company?
The public examples here concern product integrations and translation support, not foundation-model training. A custom model would be a separately researched scope.
Can AI output be published automatically?
It depends on the task and risk. I usually define review and fallback first, especially when output affects customers or sensitive information.
Is AI always necessary for automation?
No. A rule-based workflow is often cheaper and easier to verify. I compare the options before recommending a model.
Talk to me.
Tell me where your product stands. I'll give you a direct view of the options and what I can take on.